In Japan, when constructing frames using Precast Concrete (PCa) methods, unique building components are used. These include integrating column tops with beam ends or using cast-in-place concrete in the panel zone. Planning these components requires considering various factors such as the loading capacity of trailers, crane lifting capacity, joining methods, and equipment penetrations. Building Information Modeling (BIM) technology has become increasingly common in construction planning. However, extracting the necessary information for construction planning directly from the design BIM model is challenging. This difficulty arises because the design BIM model organizes columns and beams in different division units than those used in construction. To address this issue, our study models the concept of the "panel zone" and proposes a method for representing a PCa BIM model composed of panel zones, columns, and beams as PCa products. The study decomposes and combines columns and beams, with parametric changes applied to the panel zone range. Additionally, our study analyzes factors related to the design and planning of column and beam PCa products through interviews and questionnaire surveys conducted with general contractors. An evaluation mechanism for the proposed column and beam division was also established. Based on the findings, a BIM-based method was developed for planning the PCa construction method of the frame using a genetic algorithm. This approach provides a technological solution that supports the planning of frame division, considering the construction rationale at the early design stage.

Rebar lap splice is the most commonly used at construction sites because it does not require any additional equipment or labor's skills. Rebar lap splice has high construction costs because they require approximately 15% more rebar due to the overlap length. To solve these problems of rebar lap splice, mechanical rebar coupler was developed. Mechanical rebar coupler has a strong bonding force, helping to keep the structure safe even during earthquakes. In addition, mechanical rebar coupler is suitable for modular construction and easy to construct, so the construction period can be shortened. And mechanical rebar coupler can reduce the amount of rebar compared to other joint methods, thereby reducing CO2 emissions. Despite these advantages, the use of mechanical rebar couplers is not widespread except in some developed countries. This is because the types and characteristics of mechanical rebar couplers vary widely, making it difficult for construction engineers to choose. Existing research has only been conducted on mechanical rebar couplers in terms of structural experiments. And there is no research that classifies and analyzes the shapes of rebars. Hence, it should be analyzed the characteristics of mechanical rebar couplers in terms of construction methods for each shape. Therefore, the objective of this study is a basic study on comparative analysis of the characteristics and performance of different types of mechanical rebar coupler for sustainable built environment. The most efficient mechanical rebar coupler was derived for each construction site environment.

Efficient path planning for mobile crane lifting operations in the construction industry is essential for ensuring smooth machinery operation, worker safety, and the timely completion of projects. The inherently complex construction sites, characterized by dynamic environments, constantly changing conditions, and numerous static and mobile obstacles, underscore the necessity for advanced algorithms capable of generating optimal paths under various constraints. Mobile crane path planning algorithms have been researched extensively and possess the potential to resolve the challenges presented by construction sites. However, the application of these algorithms in actual construction sites is rare, suggesting a need for ongoing research and development in this field. This paper begins by systematically identifying and analyzing relevant research papers using predetermined keywords, providing a comprehensive review of the current state of mobile crane path planning algorithms. Specifically, it categorizes mobile crane path planning algorithms into four main groups: Graph search-based algorithms, Sampling-based algorithms, Nature-inspired algorithms, and Newly developed algorithms. It performs a critical analysis of each category, offering guidance to researchers exploring path planning solutions suitable for the dynamic and complex environments of construction sites. Through this review, we affirm the need for continued interest and attempts at new methodologies in mobile crane path planning, suggesting improvements for further research and practical application of these algorithms.

Conventional construction methods face significant challenges in reducing carbon emissions and promoting environmental sustainability. Off-Site Construction (OSC) method is widely recognized as a low-carbon, high-efficiency alternative construction method. However, in practice, it often fails to deliver the expected benefits, leading to issues such as excessive carbon emissions, unpunctual delivery, and cost overruns in OSC projects. In order to ensure the carbon benefits of OSC and further its development,this study conducts an in-depth analysis of embodied carbon emissions in the precast production process, proposes a multi-objective optimization model based on the permutation flow shop scheduling problem, and designs an automated solution algorithm using NSGA-II to derive Pareto optimal schedules. Through the analysis of real-world case data, the proposed approach, compared to conventional scheduling methods, is estimated to reduce embodied carbon emissions by approximately 6 % while simultaneously cutting tardiness/earliness penalty by 75%. This study offers a model for precast production scheduling, effectively enhancing production efficiency and reducing carbon emissions, enabling construction component enterprises to engage in low-carbon, cost-effective, and efficient production, thereby fostering sustainable development in the construction industry.

Off-site construction (OSC) is an architectural approach involving the prefabrication of building structures, components, parts, and equipment in a factory, followed by transportation to the construction site for assembly and installation. This method is particularly suitable for buildings with simple, repetitive structures and straightforward processes, such as hospitals, hotels, and schools. However, the inherent prefabrication characteristic has led to a widespread negative perception among the general public, resulting in significant resistance, especially from parents, concerning educational buildings utilizing OSC. In response, this study targeted users currently employing OSC in institutional buildings to analyze critical perception factors and derive avenues for enhancing the activation of OSC methods. The survey, categorized into seven factors, revealed that safety factors received the most positive responses, while social factors were identified as the most negatively perceived. The analysis of improvement requirements for OSC indicated that addressing issues related to hazardous material exposure and improving air quality are crucial in the equipment and eco-friendly aspects. In terms of design and usability improvement, maintaining harmony with the surrounding environment was identified as essential. Ultimately, this study anticipates the activation of OSC through the analysis of user perceptions and improvement suggestions for each OSC factor.

The construction industry is a significant contributor to carbon emissions, with its life cycle emissions posing significant environmental challenges. Despite its increasing importance, embodied carbon (EC) generated from the construction process is often ignored. Modular construction (MC), characterized by a combination of off-site manufacturing and on-site assembly, has been recognized for its potential to contribute to environmental benefits. However, there is still a lack of systematic explanation of urban high-rise MC. This study aims to identify whether and to what extent high-rise MC can achieve EC reductions and lay the foundation for effective carbon reductuons in the construction industry. To achieve this, the study develops a multi-level EC measurement framework for assessing EC during the construction process, using a real case to quantify the EC and determine carbon reduction performance. The innovation is a more comprehensive understanding of the boundaries of EC, as MC includes the amount of superstructure work and decoration integration. The results show that although the MC will increase EC from the transportation stage due to heavier modules, it achieves a net reduction in total EC by reducing on-site machinery energy consumption and waste rates. In conclusion, this study contributes to a better understanding of the EC emissions associated with high-rise MC, offering a valuable measurement framework for global regions evaluating the EC impacts of high-rise MC in similar contexts.

In previous research on the production of free-form concrete panel, most studies have focused on single-curved FCP. Additionally, research has primarily targeted square-shaped FCP rather than polygonal ones. However, when it comes to manufacturing double-curved polygonal FCPs, there is a concern about significant errors in the lower shape. double-curved FCP have multiple directions of curvature progression and frequent changes in surface curvature. Therefore, manufacturing double-curved polygonal FCPs can be challenging in terms of substructure implementation. This study thus investigates the substructure of double-curved polygonal FCPs. Specifically, it involves designing double-curved polygonal FCPs, implementing the corresponding shape in the LSM, and comparing it with the design shape. Experimental results showed significant shape errors in areas with abrupt curvature changes, and inevitable errors occurred in areas where there were previously no errors, despite three rounds of shape corrections. This study attributes these results to the elasticity of the LSM and limitations in deriving rod displacement values. Based on the experimental findings, the study outlines directions for future research. The results of this study are expected to serve as reference material for researchers and professionals in the field of FCP production. Additionally, they are anticipated to be actively utilized as foundational research data for subsequent studies.

To enhance energy efficiency and reduce emissions in prefabricated construction, optimizing the production scheduling of precast concrete is considered an effective approach. Due to the unique characteristics of precast concrete during production, traditional scheduling models are no longer applicable. This present study introduces practical considerations, such as a limited number of molds, buffers, uncertainty of order arrivals and vehicles. Furthermore, to meet the requirements of contemporary industrial development, a mulit-objective optimization scheduling model is formulated by integrating total processing time, on-time delivery rate and work station idle time. A solution based on reinforcement learning algorithm is devised. Results indicate that this method can effectively undergo training and achieve outstanding performance in addressing such issues. The model has the potential to significantly reduce decision-making time in precast production, thereby contributing to the sustainable development of prefabricated construction.

In order to improve the production and supply level of PC components and reduce the impact of risk accidents on production, this article hopes to solve the risk accident problems faced in industrial construction production by introducing mature constraint theories applied in the manufacturing industry. Firstly, this article introduces the widely used constraint theory and DBR method in traditional manufacturing industry; Secondly, based on the constraint theory DBR method, a pull production mechanism is established for the constraint buffering and material delivery of PC components, and risk accidents are defined and estimated; Then calculate and set buffer zones to reduce their risks and ensure the full utilization of bottleneck resources; The ultimate goal is to reduce production scheduling risks and improve production supply levels.

The absence of standardized architectural design configurations poses significant challenges in stakeholder collaboration during the pre-design phase, particularly for modular buildings. Clients often lack comprehensive knowledge of potential design configurations for their proposed buildings, leading to dissatisfaction upon project completion. This issue is exacerbated in office buildings where floor plan layouts are uncertain due to diverse employee needs. To address these challenges, this research introduces a BIM-based dataset for modular office buildings using shape grammar principles. The research manipulates the location of core modules (including staircases, elevator shafts, and entrances) based on shape grammar principles, resulting in twenty unique configurations that provide standardized options for BIM model development. The initial phase involves developing a naming convention based on shape grammar principles to determine the core module locations. Using this convention, BIM models for modular office buildings are created, forming a database connected to the frontend and backend of a recommender system. This system recommends three different design options to clients based on their specific needs, fostering improved collaboration in the pre-design phase by involving clients directly. The user interface of the recommender system aids clients in understanding potential office building configurations, thereby enhancing collaboration and decision-making. Through the amalgamation of shape grammar principles and BIM technology, this proposed system offers a promising approach to advancing efficiency and precision in architectural design communication and representation.

The significance of welding quality cannot be overstated in ensuring the structural integrity of steel constructions. However, welding operations are inherently intricate, influenced by numerous variables. This paper introduces a novel welding digital twin framework grounded in a dynamic knowledge base. This framework serves to visualize the welding procedures and forecast optimal welding parameters. Such insights facilitate informed decision-making by operators, thereby enhancing both the quality and efficiency of welding processes. Furthermore, the study employs the welding of H-beam steel as an example, wherein a digital twin welding model is established, based on which the overall welding quality can be improved.

Reinforced concrete modular integrated construction (MiC) has been widely used in Hong Kong nowadays, but the solutions for temporary tying of the side walls during the construction of the composite wall have still shortcomings. Based on a MiC project in Hong Kong, this paper proposes a new inner tie system for composite wall. The system components are installed on the side walls of precast modules without penetrating through the side walls. After the loop is rotated to contact the hook, the tying effect can be generated when the concrete is poured on site between the middle gap of two modules. This system replaces tie bolts penetrating through precast side walls, so that the modules' interior fitting-out can be fully completed in factory and the on-site construction has no adverse effects on the internal decoration. This paper mainly describes the mechanism of the system, FEA simulation and optimization of the member size, as well as tensile and punching shear tests to verify the reliability, safety and to get more information about failure mode of the system. The system will be further examined by assembling 1:1 mockup modules, and finally applied to a real MiC project soon. The system can also act as permanent tie bars for the composite wall to reduce the total wall thickness, save the structural cost and increase the usable area. Compared with other existing tying methods in the industry, the system is easy to install, reliable to take loads, adaptable to large construction errors, and has the potential to be widely used in future practical projects.

Construction projects are responsible for significant carbon emissions, accounting for 23% of the world's total emissions. While efforts have been made to reduce these emissions, a comprehensive analysis of these efforts has yet to be conducted, making it difficult to identify research gaps and future directions. This study addresses this gap by conducting a systematic literature review of 208 papers in the Web of Science (WOS) database using Carbon, Emission, and Construction as keywords. The review was categorized into bibliometric and content analysis. The bibliometric analysis reveals that most papers focus on estimating and assessing carbon emissions through Life Cycle Assessment (LCA) (34%). The use of construction technologies, such as prefabrication and BIM, which can directly reduce carbon emissions, was limited to only 7% of the reviewed papers. Furthermore, the review revealed that 67% of the studies were conducted in China. Similarly, content analysis revealed the papers' essential findings and limitations in each selected category. Based on these findings, the study 1) suggests the technology applications in tacking, estimating and reducing carbon emissions in the construction supply chain (CSC) and 2) highlights the need for global attention to reducing carbon emissions in construction projects.

Roadbed stability is paramount in urban areas as it directly affects public safety and city operations. South Korea's major metropolis has experienced 1127 cases of ground subsidence since 2014, affecting subways, roads, railways, and construction sites. Notably, about 40% of these incidents coincide with heavy summer rainfall, while 60% resulted from utility damage, improper backfill, and groundwater fluctuations. Subsequently, roadbed instability leads to a range of cascading hazards, including sinkholes and road failures, endangering public safety and the economy. Therefore, continuous monitoring of roadbed stability and implementing proactive measures are essential for a resilient transportation infrastructure. However, terrestrial in-situ observations like GPS provide accurate surface's displacement with high temporal accuracy but limited spatial resolution. To address this issue, we used the InSAR permanent scatterer (PSInSAR) technique to process 35 Sentinel-1 SLC datasets acquired between 2017 and 2022 to monitor and prevent cascading hazards in Daejeon City, South Korea. The results revealed an average subsidence rate of -0.88mm/year with a maximum of -7.73 mm/year. Notably, the southern part of the city exhibited significant roadbed instability, with an average and maximum cumulative subsidence of -5.13 mm and -44.95 mm, respectively. The deformation data was then integrated with road geometry to develop a vulnerability map of the city, highlighting the pronounced roadbed deformation in the southern region. Time-series subsidence variations correlated with groundwater fluctuations data from 2017 to 2022, showing a decline in groundwater levels from 4.63m to 9.9m in the southern region. Furthermore, a comparison between subsidence rates and effective shear wave velocity (V_s³⁰) revealed that most subsidence events were associated with V_s³⁰ values below 420 m/sec, indicating a clear lithological influence on the spatial distribution of roadbed instability. Thus, the integrated geotechnical and hydrogeological data with PSInSAR monitoring can better understand the processes responsible for roadbed instability in areas with small-scale variations.

The frequency and scale of natural disasters are rapidly increasing due to global warming. Over time, the academic community has conducted numerous studies on post-disaster reconstruction projects. Additionally, international organizations have provided various guidelines for executing these projects efficiently. In this study, we examined and analyzed several influencing factors based on existing research to improve approaches to reconstruction projects. Although generalizing results from limited studies can be challenging, our findings suggest that "Assessing the extent of damage to facilities requiring reconstruction" should be prioritized in the implementation of reconstruction projects.

After increasing to 84 trillion yen in FY1992 following the burst of Japan's economic bubble, construction investment in Japan fell to 42 trillion yen in FY2010 in the wake of the global financial crisis. It subsequently recovered to 67 trillion yen in FY2022, driven largely by demand for reconstruction after the Great East Japan Earthquake and Abenomics. On the other hand, the sustainability of Japanese construction is under threat due to a declining population and a decrease in the number of construction workers. A breakdown in sustainability is not only detrimental to the population, but also leads to delays in disaster recovery for Japan, which is a disaster-prone country. It is therefore necessary to analyze the status and trends in building construction in recent years to clarify the future sustainability of building construction in Japan. This study reviews basic information on buildings constructed in recent decades using raw data from the Statistical Survey of Building Starts, a statistical survey undertaken by the Japanese government, and analyzes statistical data such as number of buildings, location, building type, total floor area, construction cost, and construction duration. Finally, the sustainability of building construction in Japan is discussed.

Flooding presents a significant threat to infrastructure, and climate change is exacerbating these risks. High-speed rail (HSR) infrastructure, designed based on historical data, may struggle to cope with future extreme flood events. Infrastructure stakeholders require forecasting capabilities to predict the intensity and frequency of future floods so they can develop adaptive strategies to mitigate flood risks and impacts. Floods can cause significant damage to HSR infrastructure networks, disrupting their operations. Traditional network theory-based frameworks are insufficient for analyzing the three-dimensional effects of floods on HSR networks. To address this issue, this study proposes a comprehensive approach to assess flood risk and vulnerability under future climate scenarios for HSR networks. The method consists of three components. (i) Generate flood inundation data by utilizing global climate models, Shared Socioeconomic Pathways(SSPs), and the CaMa-Flood model. (ii) Fit extreme flood depths to the Gumbel distribution to generate flood inundation scenarios. (iii) Overlay flood scenarios on the HSR network and quantitatively assess network vulnerability based on topology network. When applied to the HSR system in mainland China, the results indicate that flood severity does not necessarily increase under higher SSPs, but may worsen over time. The minimum flood return period that causes HSR disruptions is decreasing, with Hubei Province showing a significant increase in HSR segment failure probability. Discontinuous phase transitions in HSR network topology metrics suggest potential nationwide collapses under future infrequent floods. These findings can inform preventive measures for the HSR sector and flood-resistant standards for HSR infrastructure. The method used in this study can be extended to analyze the vulnerability of other transportation systems to natural disasters, serving as a quantitative tool for improving resilience in a changing climate.

Public infrastructure projects are implemented to achieve targeted economic development of nations. However, due to the delayed execution of projects, the investment cost of the project increases in proportion to time overrun. The increased investment cost for the defined scope of project may have an effect on the achievement of planned future benefits, but the effect of delay on the loss of estimated benefits is not well explored. The primary objective of this research is to assess the effect of delayed execution of road and bridge construction projects on the estimated future benefits. Furthermore, the relationship between delay and the percentage loss of future benefits is modeled using the linear regression analysis. The data consists of 395 road and 248 bridge construction contracts under the Department of Roads, Nepal. The statistical analysis of road and bridge construction contracts showed that there is a reduction in estimated benefits in future years due to the effect of delay. The relationship between the percentage loss of estimated benefit and delay period in months was found to be significant for both road and bridge contracts. The results show that delay not only affects the short-term cost overrun but also the achievement of estimated future benefits. This research thus contributes valuable insights into the understanding of the impact of project delays on both cost overruns and the loss of estimated future benefits. Furthermore, this research has practical implications for policymakers, private sector investors, and financing agencies involved in infrastructure development projects.

Road sign support systems are not usually well managed because bridges and pavement have budget and maintenance priority while the sign boards and sign supports are considered as miscellaneous items. The authors of this paper suggested the implementation of simplified machine learning algorithms for asset risk management in highway sign support systems. By harnessing historical and real-time data, machine learning models can forecast potential vulnerabilities, enabling early intervention and proactive maintenance protocols. The raw data were collected from the Connecticut Department of Transportation (CTDOT) asset management database that includes asset ages, repair history, installation and repair costs, and other administrative information. While there are many advanced and complicated structural deterioration prediction models, a simple deterioration curve is assumed, and prediction model has been developed using machine learning algorithm to determine the risk assessment and prediction. The integration of simplified machine learning in asset risk management for highway sign support systems not only enables predictive maintenance but also optimizes resource allocation. This approach ensures that decision-makers are not inundated with excessive detailed information, making it particularly practical for industry application.

Cracks in structural materials present a critical challenge to infrastructure safety and long-term durability. Timely and precise crack detection is essential for proactive maintenance and the prevention of catastrophic structural failures. This study introduces an innovative approach to tackle this issue using U-Net deep learning architecture. The primary objective of the intended research is to explore the potential of U-Net in enhancing the precision and efficiency of crack detection across various concrete crack detection under various environmental conditions. Commencing with the assembling by a comprehensive dataset featuring diverse images of concrete cracks, optimizing crack visibility and facilitating feature extraction through advanced image processing techniques. A wide range of concrete crack images were collected and used advanced techniques to enhance their visibility. The U-Net model, well recognized for its proficiency in image segmentation tasks, is implemented to achieve precise segmentation and localization of concrete cracks. In terms of accuracy, our research attests to a substantial advancement in automated of 95% across all tested concrete materials, surpassing traditional manual inspection methods. The accuracy extends to detecting cracks of varying sizes, orientations, and challenging lighting conditions, underlining the systems robustness and reliability. The reliability of the proposed model is measured using performance metrics such as, precision(93%), Recall(96%), and F1-score(94%). For validation, the model was tested on a different set of data and confirmed an accuracy of 94%. The results shows that the system consistently performs well, even with different concrete types and lighting conditions. With real-time monitoring capabilities, the system ensures the prompt detection of cracks as they emerge, holding significant potential for reducing risks associated with structural damage and achieving substantial cost savings.

In recent years, as buildings have become taller and taller, the continued usability of elevators after earthquakes has become an important issue. Conventional seismic design of elevators has focused mainly on inertial forces caused by earthquakes, but the influence of the story drift angle of buildings on elevator behavior has been unclear. Therefore, the objective of this study was to clarify the influence of the story drift angle of a building caused by an earthquake on the behavior of elevators through an experiment. The experiment specimens were the counterweight, guide rails, and surrounding components selected from the actual elevator components and mounted on a one-story steel pin frame. A static experiment was conducted using a hydraulic jack to apply force to the specimen by imposing the story drift angle on the steel frame. During the experiment, the reaction force at the end of the jack was monitored, and the displacement and strain of the counterweight, guide rails, and surrounding components were measured. The results of the experiments in one direction showed that even when the elevator components were subjected to a larger story drift angle than assumed in the seismic design of the building, no damage occurred that could lead to fallout.

Given increasing concerns regarding environmental issues, environmentally responsible behavior during the construction of cross-sea bridge projects becomes critical. However, a systematic investigation of the topic remains absent. To bridge the knowledge gap, this study first defines environmentally responsible behavior from four perspectives: purposes, means, contents and influencing factors. Then, the study uses a grounded theory approach to code and analyze 101 documentations retrieved from 30 cross-sea bridge projects carried out by Chinese construction companies worldwide. Results show that environmentally responsible behaviors in cross-sea bridge projects are highly influenced by four factors, namely government policies, public opinions, goals of the projects, and construction companies'philosophy of environmental protection. Results also indicate that Chinese construction companies have used managerial, technical, and ecological means to ensure that the goals of environmentally responsible behaviors, namely minimization of damage to marine ecology; prevention of air, noise and visual pollution; and reduction of resource consumptions, are achieved. Lastly, suggestions for promoting and governing environmentally responsible behavior are proposed.

The concept of the circular economy is gaining popularity due to the growing awareness of sustainability. However, Japan seems to have lower awareness of this concept compared to other countries. The aim of this study is to clarify the current status of the implementation of circular design in Japanese architectural design. The case studies utilizing the 'Circular Design Toolkit' framework and interviews with architectural designers who practice the circular economy is conducted for it.

Free-form buildings are composed of different curved surfaces and panels with varying curvatures used for the exterior. Because free-form curved surfaces differ from those of conventional buildings, they serve as landmarks worldwide and generate economic and social profits. However, molds used to realize the curved surfaces of free-form buildings are typically single-use, resulting in construction waste and posing limitations such as environmental pollution and increased construction costs. To address this issue, current research is focused on developing reusable forms that precisely implement free-form curved surfaces. Among these approaches, the Free-form Concrete Panel (FCP) employs reusable silicone material as a mold. The silicone mold consists of a lower part and a side part, with both parts fixed together by friction due to the same material. However, during the concrete pouring process into the silicone mold, lateral pressure can cause shifting, reducing the precision of the FCP and resulting in defective panels. To address this challenge, this study introduces the use of iron powder in the lower part and magnets on the sides to secure the form using magnetic force.

This research focuses on the extent to which the water efficiency elective items of the LEED certification system adapt to the environmental characteristics of regions with varying climatic conditions. The building industry, is being called upon to promote energy-efficient construction, and the issues surrounding water resources are becoming increasingly severe, underscoring the importance of sustainable water resource management. Based on the hypothesis that, despite its global recognition and widespread adoption, the LEED certification does not always adequately address the environmental realities of all regions, this study analyzed new construction cases certified by LEED in the United States, Japan, and Taiwan from 2019 to February 2024. The analysis revealed regional differences based on water efficiency elective items and certification level trends under different climatic conditions. The findings suggest that the LEED certification system may not fully respond to the environmental situations and water resource issues in all regions, raising concerns about the adaptability of water efficiency items and the potential for similar issues in other evaluation items.

The suburban residential areas encircling major Japanese cities, established during the era of rapid economic growth, grapple with a formidable challenge as their original residents age swiftly. The migration of individuals toward city centers and proximity to train stations, coupled with an aging populace and diminishing birthrate, portends a diminishing functionality of these towns, significantly impacting residents' lives and posing a potential threat to their future. Within the context of a rapidly aging society, the effective utilization of the substantial existing housing stock emerges as a critical issue, essential for shaping future housing policies in a super-aging society. This thesis investigates vacant rooms within detached houses, a segment of spatial stock, with the goal of comprehending the mechanisms instigating their occurrence. The aim is to predict their future through the formulation of a mathematical equation encapsulating the conditions leading to their formation. Through the analysis of data from 76 houses out of 118 questionnaires and 36 interviews, where respondents acknowledged having vacant rooms, the study seeks to elucidate the governing mechanisms. It identifies factors exhibiting correlation or causal relationships with the emergence of vacant rooms by scrutinizing the timing and circumstances of the current vacant rooms. The thesis asserts that the genesis of vacant spaces can be explicated by a simple equation, notably linked with life plans. It introduces three scenarios-optimistic, intermediate, and pessimistic-and provides recommendations for addressing potential outcomes. In the backdrop of a diminishing and aging population, understanding available spatial resources is pivotal. The thesis contends that the detailed exploration of each scenario offers crucial insights for cultivating sustainable residential communities, extending beyond urban planning to encompass area management, individual decision-making, and the development of commercially viable housing aligned with these decisions.

Many developed countries, including Korea, are rapidly aged owing to years of use. Infrastructures such as roads, water, and sewage are Social Overhead Capital (SOC), which provide convenience to the nation and support national economic growth. Thus, continuous maintenance and investment are required because infrastructure deterioration is directly related to social effects, such as quality of life and safety. In addition, because infrastructure maintenance costs a lot of the budget, it is necessary to appropriate criteria for budget allocation, given assessing the condition of infrastructure. This study developed an Infrastructure Maintenance Map (IMM) based on a Geographic Information System (GIS) for infrastructure maintenance budgets and investment priorities. The IMM uses maintenance information for roads, bridges, water, and sewage, obtained from Bridge Management System (BMS), Pavement Management System (PMS) and facility data in South Korea. The IMM can calculate deterioration levels and maintenance costs of infrastructure repair methods. Maintenance priorities are also evaluated based on Multi-Attribute Utility Theory using the deterioration level, economic feasibility, and effect of facilities. This study contributes to easy decision-making regarding infrastructure investment priorities and maintenance budgeting to the status of facility on the 3D map by IMM.

This paper aims to ascertain corporate social responsibility (CSR) and competitive advantage relationship with empirical evidence to help achieve competitive advantage of China's construction companies. Using a panel data set of 85 listed Chinese construction companies and 691 firm-year observations over the period from 2010 to 2019, the concurrent and lagged effects of CSR on competitive advantage were tested by using both static and dynamic panel regression models. The empirical analyses discover that there exists a concurrently positive impact of CSR on competitive advantage of China's listed construction companies. Competitive advantage in the prior year has a positive influence on itself in the current year. This empirical finding suggests that companies should engage in CSR activities continuously to sustain their competitive advantage. A competitive contractor is likely able to maintain its competitive position by sustaining its strong financial resources, innovative capabilities, and good corporate image. This study provides Chinese construction business with evidence to develop or fine-tune their CSR programmes for sustaining their competitive advantages.

This study investigates the corrosion rate measurements for multiple reinforcements in concrete by using the galvanostatic pulse technique. In order to know whether or not this technique can distinguish corrosion status for multiple reinforcements covered by the guard ring, two programs were conducted. For the first stage, a reinforcement was embedded in two concrete blocks and the part of reinforcement in one of the block was in a corrosion environment while another part of reinforcement in another block was not in a corrosion environment. Results reconfirmed that the galvanostatic pulse technique detected the local corrosion current owing to the help from guard ring. For the second stage, two parallel reinforcements (one epoxy-coated reinforcement and one plain reinforcement) were embedded in a chloride contaminated concrete block. Results showed that when two reinforcements were covered by guard ring, the galvanostatic pulse technique could not distinguish the corrosion current for each individual reinforcement and an average value would be obtained. In such a case, for the reinforcement which was not corroded one may overestimate its corrosion rate, and for the reinforcement which was corroded one may underestimate its corrosion. Therefore, results imply that a C-scan method (which is commonly used for the ultrasonic testing) may be required to obtain a correct measurement of corrosion rate.

The global shift towards sustainable energy practices underscores the importance of residential photovoltaic (PV) adoption, a trend that has been gaining more attention in Hong Kong. Despite this, existing research in this area lacks quantitative analysis, with a particular deficiency in statistically robust surveys across diverse residential sectors in Hong Kong and in comparative studies between PV installers and non-PV installers. Addressing these gaps, this study employed a comprehensive questionnaire survey to collect data from a representative sample of both PV installers and non-PV installers regarding their housing types and homeownerships, followed by chi-square tests of independence to explore the demographic distinctions influencing residential PV adoption. The findings highlight significant variance in adoption rates between homeowners and tenants, with a noticeable inclination for PV installation among owners of independent houses. In contrast, co-tenants in residential flats demonstrated a lower propensity for PV adoption. These insights provide a crucial understanding of the factors that affect PV system uptake and could inform the formulation of targeted policies to boost renewable energy integration in urban residential settings.

With the development of living standard, people spend more time indoors, and the diversified home decoration will lead to indoor hazardous gas emission. Among them, formaldehyde (HCHO) is one of the most important sources of indoor air pollution, which is commonly found in building materials as a human carcinogen. To address this issue, we developed highly efficient multifunctional green building coatings (GBC) by TiO₂, enhancement silica fume (ESF) and spent fluid catalytic cracking catalysts (sFCCC). Among these prepared GBC, the GBC-0.8 exhibited HCHO removal efficiency of 85.0 % under visible light at ambient temperature, which was much higher than that of commercial coatings (30.8%). In addition, moisture adsorption-desorption carrier tests were executed by different humidity. The humidity control capacity of GBC-0.8 could reach 293.8 g/m² and demonstrate superior stability after 3 cycles. Compared with pristine TiO₂, the addition of ESF and sFCCC showed higher specific surface area and pore size distribution, which was beneficial to improve humidity control and photocatalytic degradation performance. This study provides a promising green method for designing multifunctional green building materials coatings to recycle waste into high-value products and remove HCHO at room temperature

Automated route planning is an important tool in the field of built environment. For example, a high-quality route planning method can improve the logistics planning of projects, thereby enhancing the performance of projects and the effectiveness of management. However, the traditional automated route planning is performed based on the predicted mean value travel time of candidate routes. Such a point estimate neglects the purpose of the trip and can further lead to a suboptimal decision. Motivated by this challenge, this study proposes an innovative framework for trip purpose based route planning. The proposed artificial intelligence and stochastic optimization framework recommends the most appropriate travel route for decision makers by fully considering their trip requirements beyond just the shortest mean value travel time. In addition to its theoretical contributions, our proposed route planning method will also contribute to the current logistics planning practice. Future research may be devoted to the real-life implementation of the proposed methodology in a broader context to provide empirical insights for practitioners in various industries.

To enhance the overall well-being of individuals, acquiring only a quantitative inventory of the individual infrastructure is insufficient and comprehending public perspectives on service levels and infrastructure needs is equally important. This study presents a model for infrastructure service quality that considers the various needs of residents. The study examined the significance and appropriateness of infrastructure service indices in determining the priorities and objectives of government investment in infrastructures. The primary objective was to construct and authenticate a multidimensional model of infrastructure service quality, building on the SERVQUAL framework established in 1988. A survey was distributed to individuals residing in Korea, and 12,500 completed questionnaires were collected. In this study, we conduct a path analysis to test our hypotheses using the AMOS software (version 29.0). The findings of the study indicate that residential satisfaction has a considerable impact on the quality of life. Additionally, this study indicates that the level of infrastructure performance in a residential area has a substantial impact on people's satisfaction with their housing. Furthermore, the findings indicate that it is crucial to address both the quantitative and qualitative dimensions of infrastructure simultaneously. Finally, the evaluation of the efficacy of infrastructure enhancement investments should consider the quality of the infrastructure services.

The increasing occurrence of abnormal weather conditions such as unusually heavy rains and strong winds adversely affects construction work. However, although these phenomena are localized, conventional weather forecasts have insufficient spatial resolution and update frequency to accurately predict weather conditions at construction sites. To address this issue, we introduced a pinpoint weather forecasting technology that improves spatial resolution and update frequency. The weather information obtained from this technology is processed and provided to construction sites in a comprehensible state, which enables construction workers to better prepare for weather conditions, thereby reducing the risk of accidents and delays. Furthermore, a safety management system was developed based on the relationship between weather and labor accidents. Predicting workplace accidents that are likely to occur on that day based on the impact of weather on the body enabled performing safety awareness activities, such as morning and lunch meetings, from a new perspective, improving safety awareness among construction workers and reducing the number of accidents on construction sites. This paper describes the development process of the proposed system and the utilization of weather forecasting at construction sites, which can be applied to other industries and contribute to improving safety and efficiency in various fields.

In the construction industry, steel structures are prominent due to their exceptional strength and high bearing capacity, making them resilient against natural calamities. However, the stability and overall structural integrity of these steel structures depend significantly on the precision of the individual steel members used. Presently, the dimensions of these steel members are typically measured manually using mechanical instruments such as steel tape and vernier calipers. This conventional approach is not only time-consuming but also highly vulnerable to human error. Consequently, there is a growing need for more accurate and reliable methods for assessing the dimensions of steel members. This paper aims to measure the dimensions of key checklists of the cross-section surface of the steel H-beams using Terrestrial Laser Scan (TLS) data. This study involves the automatic extraction of scan points associated with the cross-section surface of the H-beam members using RANSAC. By the end, an algorithm was developed to predict the actual edge points belonging to the boundary of the extracted surface and introduced an edge loss compensation model to compensate the losses occurred due to uncertainties. Experimental evaluations were conducted using various scan data collected from steel H-beam and the measured dimensions were subsequently compared with manual measurements and dimensions obtained through the previously proposed method, demonstrating that the measurements meet 1mm accuracy and are within the allowable tolerance range followed in industry. This research underscores the efficiency and reliability of the introduced approach, offering a promising solution to enhance the dimensional quality assessment of steel H-beams in the construction industry.

The success of construction projects is influenced by various factors, with accurate management and prediction of the construction period playing a crucial role. The construction period is determined through contracts between the client and the contractor, and it is considered a key element in the management of construction projects, alongside cost management. To ensure the successful completion of projects, accurate prediction of the construction period is essential, as it aids in the efficient allocation of time and resources. The main objective of this study is to maximize the performance of construction period prediction models by applying and comparing various methods for handling missing data. Optimizing the model's performance requires accuracy and completeness of data, with the process of outlier removal and missing data imputation potentially having a significant impact on the model's predictive capability. During this process, the effect of changes in the dataset on model performance will be closely examined to identify the most effective method for handling missing data. Outlier removal and missing data imputation are crucial steps in the data preprocessing phase, and they can significantly improve the model's accuracy and reliability. This research aims to apply these data preprocessing methods and analyze their outcomes to find the most effective missing data imputation method for construction period prediction. After the selection process, considering the model's performance and stability, the mode imputation method was identified as the most suitable for predicting the construction period. The findings of this research are expected to contribute not only to improving the accuracy of construction period predictions but also to enhancing the overall efficiency and success rate of construction project management.

The improvement of the reality capture concept has made 3D datasets an important resource for visualization and documentation in the Architectural, Engineering, and Construction (AEC) field. Despite laser scanning providing the most accurate 3D models, it also entails some disadvantages, such as high equipment costs. Therefore, this study aims to compare the accuracy of four reality capture equipment types (LiDAR, UAV-based LiDAR, UAV-based photogrammetry, and 3D virtual tours), each offering different advantages and disadvantages. The findings suggest that aerial LiDAR, 3D virtual tour, and aerial photogrammetry offer increasingly less accurate alternatives to TLS, respectively. The results of the study may help construction companies when deciding on reality capture investment.

Physiological devices and immersive technologies are crucial innovations being implemented for construction safety. Physiological devices provide insights into the wellbeing of workers while immersive technologies have a potential to simulate or enhance construction environments. These two technologies present numerous benefits for construction safety and have been extensively implemented in various dimensions. In addition to the individual benefits of these two technologies, combining them presents more opportunities for construction safety research and numerous studies have been conducted using this approach. However, despite promising results achieved by studies which have used this technological combination, no review has been conducted to summarize the findings of these studies. This review therefore summarizes studies that have combined immersive technologies with physiological monitoring for construction safety. A systematic approach is employed, and 24 articles are reviewed. This review highlights four safety aspects which have been explored using a combination of immersive technologies and physiological monitoring. These aspects are (1) Safety training and evaluation (2) Hazard identification (3) Attention assessment and (4) Cognitive strain assessment. In addition, there are three main directions for future research. (1) Future studies should explore other types of immersive technologies such as immersive audio (2) Physiological reactions to hazard exposure should be studied and (3) More multi-physiological approaches should be adopted.

The rapid emergence of Artificial Intelligence (AI) across diverse sectors has also made its presence felt in the construction sector, where its adoption is gaining momentum at a remarkable pace. The anticipated impact of AI on decision-making processes pertinent to construction project management is considerable, necessitating a holistic understanding of AI's potential applications. As a first step towards that goal, this paper conducts a systematic literature review and in-depth content analysis of existing literature related to the applications of AI in the context of construction project management. The authors selected journal papers, technical papers, and conference proceedings published between 2010 and 2023 on the topic of Artificial Intelligence for construction project management applications. Additionally, the authors also reviewed several industry and trade publications in the same topic area. The search resulted in more than 200 relevant articles, after which the authors conducted a thorough content analysis. The results categorized applications of AI in construction project management across categories: construction productivity, construction safety, construction quality, construction document management, and construction site planning. Additionally, the review identified the current trends of AI applications in construction project management, advantages, and challenges to implementation. Understanding AI applications, advantages, and challenges to implementation helps contractors gain new insights into the efficient implementation of AI for various project management purposes.

In this study, we conducted a work analysis at an active building construction site, utilizing three-axis acceleration sensors affixed to four plaster-board-pasting workers for five days. Although acceleration data is less accurate than visual or image recognition in identifying specific tasks, it can be easily captured using smartphones, even in challenging conditions such as poorly lit or obstructed construction sites. This accessibility facilitates continuous data collection over extended periods, enabling automated analysis without significant cost or time investment. In addition, this method can identify trends in worker behavior that elude conventional visual inspection. Our approach encompasses various evaluation indices, beginning with an analysis of average work time per plasterboard sheet and the differentiation of walking motions using acceleration data. Furthermore, we introduced a new evaluation index that quantifies the distribution of high- and low-intensity work based on acceleration readings. Through comparative analysis with evaluation indices from previous studies, we confirmed common trends and discussed the strengths and limitations of our proposed index. Our findings suggest a correlation between work experience and performance, as evidenced by smoother operational patterns among seasoned workers. In particular, proficient workers exhibited fewer instances of extremely intense or sporadic movements. This observation underscores the influence of experience on work dynamics.

Quality control in construction projects necessitates the detection of defects during construction. Currently, this task is performed manually by site supervisors. This manual process is inefficient, labor-intensive, and prone to human error, potentially leading to decreased productivity. To address this issue, research has been conducted to automate defect detection using computer vision-based object detection technologies. However, these studies often suffer from a lack of data for training deep learning models, resulting in inadequate accuracy. This study proposes a method to improve the accuracy of deep learning models through the use of virtual image data. The target building is created as a 3D model and finished with materials similar to actual components. Subsequently, a virtual defect texture is produced by layering three types of images: defect information, area information, and material information images, to fabricate materials with defects. Images are generated by rendering the 3D model and the defect, and annotations are created for segmentation. This approach creates a hybrid dataset by combining virtual data with actual site image data, which is then used to train the deep learning model. This research was conducted on the tile process of finishing construction projects, focusing on cracks and falls as the target defects. The training results of the deep learning model show that the F1-Score increased by 12.08% for falls and cracks when using the hybrid dataset compared to the real image dataset alone, validating the hybrid data approach. This study contributes not only to unmanned and automated smart construction management but also to enhancing safety on construction sites. To establish an integrated smart quality management system, it is necessary to detect various defects simultaneously with high accuracy. Utilizing this method for automatic defect detection in other types of construction can potentially expand the possibilities for implementing an integrated smart quality management system.

Construction projects account for a significant proportion of workplace hazards globally. While construction cost reports typically emphasize direct accident costs such as treatment expenses, nursing care costs, or disability benefits, indirect factors like work interruption loss costs or consolation costs are frequently overlooked, because it is relatively difficult to estimate those factors in advance. Recognizing and accurately estimating the indirect costs factors associated with construction accidents would not only shed light on the monetary impact these incidents have on overall project costs but also would enable to estimate the total accident cost in advance. The current study seeks to identify factors influencing indirect costs, which ultimately govern the total accident cost, through a data mining approach. A survey was conducted in domestic construction companies, resulting in a dataset of 1038 accident records collected from construction sites. First, statistical analysis was performed to uncover characteristics and patterns of factors affecting construction accident costs from both direct and indirect perspectives. Later, this study proposes four distinct machine learning (ML) models, comparing their performances in predicting the total accident cost (including indirect costs) in advance. Additionally, this research sheds light on an important issue in construction data analysis, which is the scarcity of data in a particular class, by applying random oversampling and random undersampling techniques. The suggested framework can assist practitioners and management in estimating construction accident costs and identifying the relevant attributes that impact accidents at the construction site for future practices.

Bridge inspection is crucial for infrastructure maintenance. Current inspections based on computer vision primarily focus on identifying simple defects such as cracks or corrosion. These detection results can serve merely as preliminary references for bridge inspection reports. To generate detailed reports, on-site engineers must still present the structural conditions through lengthy textual descriptions. This process is time-consuming, costly, and prone to human error. To bridge this gap, we propose a deep learning-based framework to generate detailed and accurate textual descriptions, laying the foundation for automating bridge inspection reports. This framework is built around an encoder-decoder architecture, utilizing Convolutional Neural Networks (CNN) for encoding image features and Gated Recurrent Units (GRU) as the decoder, combined with a dynamically adaptive attention mechanism. The experimental results demonstrate this approach's effectiveness, proving that the introduction of the attention mechanism contributes to improved generation results. Moreover, it is worth noting that, through comparative experiments on image restoration, we found that the model requires further improvement in terms of explainability. In summary, this study demonstrates the potential and practical application of image captioning techniques for bridge defect detection, and future research can further explore the integration of domain knowledge with artificial intelligence (AI).

This research developed an urban information platform to enable holistic urban design across multiple disciplines and regions, addressing Japan's urban challenges. By aggregating a wide range of urban data into a geographic database, the study emphasizes data-driven decision-making in urban planning. The platform supports the visualization and analysis of critical domains like medical and water supply, enhancing decision-making processes. Key contributions include the creation of evaluation indicators and the demonstration of the platform's application in urban design discussions.

Mobile electroencephalography (EEG) can continuously and objectively monitor construction workers' psychological stress, thereby contributing to enhanced safety and health. Traditional EEG-based stress assessment techniques utilize headset-type devices that cover the scalp, including the frontal area, which is the most relevant brain part to stress. Yet, the invasiveness of such devices may pose a potential barrier to their field application. In response, ear-EEG technology presents a less intrusive alternative for continuous monitoring, potentially overcoming the limitations of scalp-EEG. The temporal regions monitored by ear-EEG hold anatomical and functional significance in the brain's response to stress, suggesting that ear-EEG could effectively detect stress. Despite its advantage, the effectiveness of ear-EEG in stress detection remains underexplored, largely due to the existing literature's focus on frontal brain regions. To address this gap, the authors aim to evaluate ear-EEG's effectiveness in measuring stress and compare it to high-density scalp-EEG. EEG signals were collected with ear- and scalp-EEGs from 10 subjects in a controlled laboratory while they performed the mental arithmetic tasks under time pressure and socio-evaluative threats to induce stress at different levels (high vs. low). Subsequently, the authors performed t-tests and point-biserial analysis to analyze differences between high and low-stress conditions in the most reliable stress biomarkers in literature: high-beta power in temporal regions for ear-EEG, and alpha asymmetry in frontal regions for scalp-EEG. The results indicate that both EEG techniques could effectively differentiate between stress levels, with statistical significance (p <0.001 for both) and moderate effect size. Furthermore, the results demonstrate ear-EEG's comparable effectiveness to scalp-EEG in detecting stress-induced brain activity given the comparable statistical metrics, such as p-value and effect size. This study provides a groundwork for further explorations into leveraging ear-EEG as a practical tool for the early detection of stress, aiming to enhance stress management strategies within the construction industry.

In Japan, bridge inspections are mandated every five years. The inspection database for bridges under the jurisdiction of the Ministry of Land, Infrastructure, Transport, and Tourism enables the acquisition of damage progression data for each structural element. This study develops a methodology for predicting the deterioration of girder bridges, employing a novel approach where inspection drawings are processed using Optical Character Recognition (OCR) to extract element numbers and their spatial relationships, subsequently creating a comprehensive graph of these elements. A key feature of this prediction methodology is its ability to consider the adjacency relationships between different bridge members, made possible by the detailed analysis of drawing information and a Graph Transformer model. The research examines and compares the accuracy of predictions made with and without considering adjacency relationships, highlighting the effectiveness of incorporating detailed structural information in the predictive analysis of bridge deterioration.

In bidding documents, valuable information about the project exists in the form of text. When undertaking a new bid, it is necessary to refer to relevant documents from previous bidding projects, similar to the current one, to effectively understand the requirements and characteristics of the project. However, manually comparing and analyzing these documents is a time-consuming and costly process. Especially with the incorporation of emerging technologies like BIM, comparing and analyzing documents involving these new technologies requires a deeper level of expertise and understanding, posing a significant challenge. To tackle this knowledge gap, this study aims to develop a BERT-based approach to assess project similarity for Korean bidding documents. To achieve the research goal, a two-stage strategy was adopted: 1) the development of a Korean tokenizer for bidding documents in BIM technology, and 2) word embedding using BERT and project similarity analysis employing cosine similarity. The developed BERT-based similarity analysis model can automatically evaluate each project and identify the most similar project. By matching target projects with the best benchmarks, this research can assist individuals in making more accurate and timely decisions.

The detection and tracking of construction workers in building sites generate valuable data on unsafe behavior, work productivity, and construction progress. Many computer vision-based tracking approaches have been investigated and their capabilities for tracking construction workers have been tested. However, the dynamic nature of real-world construction environments, where workers wear similar outfits and move around in often cluttered and occluded regions, has severely limited the accuracy of these methods. Herein, to enhance the performance of vision-based tracking, a new framework is proposed which seamlessly integrates three computer vision components: detection, tracking, and re-identification (DTR). In DTR, a tracking algorithm continuously tracks identified workers using a detector and tracker in combination. Then, a re-identification model extracts visual features and utilizes them as appearance descriptors in subsequent frames during tracking. Empirical results demonstrate that the proposed method has excellent multi-object-tracking accuracy with better accuracy than an existing approach. The DTR framework can efficiently and accurately monitor workers, ensuring safer and more productive dynamic work environments.

Large Language Models (LLMs) still encounter challenges in comprehending domain-specific expressions within construction documents. Analogous to humans acquiring unfamiliar expressions from dictionaries, language models could assimilate domain-specific expressions through the use of a thesaurus. Numerous prior studies have developed construction thesauri; however, a practical issue arises in effectively leveraging these resources for instructing language models. Given that the thesaurus primarily outlines relationships between terms without indicating their relative importance, language models may struggle in discerning which terms to retain or replace. This research aims to establish a robust framework for guiding language models using the information from the thesaurus. For instance, a term would be associated with a list of similar terms while also being included in the lists of other related terms. The relative significance among terms could be ascertained by employing similarity scores normalized according to relevance ranks. Consequently, a term exhibiting a positive margin of normalized similarity scores (termed a pivot term) could semantically replace other related terms, thereby enabling LLMs to comprehend domain-specific terms through these pivotal terms. The outcome of this research presents a practical methodology for utilizing domain-specific thesauri to train LLMs and analyze construction documents. Ongoing evaluation involves validating the accuracy of the thesaurus-applied LLM (e.g., S-BERT) in identifying similarities within construction specification provisions. This outcome holds potential for the construction industry by enhancing LLMs' understanding of construction documents and subsequently improving text mining performance and project management efficiency.

Sand is a vital component within a concrete admixture for variety of structures and is classified as one of the crucial bulk material used. Assessing the Fineness Modulus (FM) of sand is an essential part of concrete production process because FM significantly affects the workability, cost-effectiveness, porosity, and concrete strength. Traditional sand quality inspection methods, like Sieve Analysis Test, are known to be laborious, time-consuming, and cost ineffective. Previous studies had mainly focused on measuring the physical characteristics of individual sand particles rather than real-time quality assessment of sand, particularly its FM during concrete production. This study introduces an image-based method for detecting flawed sand through deep learning techniques to evaluate the quality of sand used in concrete. The method involves categorizing sand images into three groups (Unavailable, Stable, Dangerous) and seven types based on FM. To achieve a high level of generalization ability and computational efficiency, various deep learning architectures (VGG16, ResNet-101 and MobileNetV3 small), were evaluated and chosen; with the inclusion of transfer learning to ensure model accuracy. A dataset of labeled sand images was compiled. Furthermore, image augmentation techniques were employed to effectively enlarge this dataset. The models were trained using the prepared dataset that were categorized into three discrete groups. A comparative analysis of results was performed based on classification performance metrics which identified the VGG16 model as the most effective achieving an impressive 99.87% accuracy in identifying flawed sand. This finding underscores the potential of deep learning techniques for assessing sand quality in terms of FM; positioning this research as a preliminary investigation into this topic of study.

Under the influence of pervasive digital revolution, the world is overwhelmed with data with an increasing speed of data generation. The accessibility and analysis of 'big data' can provide useful insight and help various industry sectors revolute. Although the concept of 'big data' has gained popularity in the construction industry in recent years, the construction industry remains at a nascent stage in the adoption of big data technologies and lags behind other sectors. To the best of our knowledge, few empirical studies have been done to examine the status quo of big data adoption in the construction sector and its influencing factors. This paper fills the gaps and examines the current status of big data adoption in companies with different sizes and roles and projects with different types, and the drivers and challenges in adopting big data technologies. After an extensive literature review, a questionnaire survey and post-interviews were conducted. The results of the analysis show that the big data adoption in the construction sector is affected by the size of companies and the work experience of employees. Technology advancement, competitiveness, government plan, and policy initiatives are the main drivers of the big data adoption, while design appropriate system, difficulty in data collection and the lack of knowledge and experience were found to be the major challenges for the big data adoption in the construction sector. Finally, the identified top three strategies to overcome challenges and promote big data adoption are 'clear organization structure', 'government incentives' and 'the training of IT personnel'. The findings of this study guide construction practitioners in different companies and projects put domain specific strategies in place to enhance the big data adoption.

This research paper explores the integration of Unmanned Aerial Vehicle (UAV)-based remote sensing survey methods, specifically LiDAR and photogrammetry, into the measurement and payment processes of beach fill construction projects managed by the U.S. Army Corps of Engineers (USACE). The primary objective is to evaluate the feasibility, accuracy, and cost-effectiveness of UAV technology in contrast to traditional topographic and hydrographic survey methods. The methodology includes a comprehensive literature review, case studies, accuracy standard assessments, and a detailed cost comparison between conventional and UAV-based survey techniques. The findings reveal that UAV-based remote sensing can offer significant improvements in terms of efficiency and cost savings. UAVs are capable of capturing large data volumes quickly with reduced manpower and equipment needs. However, the accuracy of UAV surveys is contingent upon environmental conditions and the proper staging of control points. Moreover, the initial investment and operational costs of UAV equipment are substantial and warrant further analysis. The paper argues for flexibility in measurement and payment methodologies during the project design phase to accommodate UAV technology. While environmental conditions may occasionally necessitate traditional survey methods, the study suggests that regions like the Florida Peninsula, with clearer water and more favorable weather, are particularly well-suited for the implementation of UAV-based surveys. The significance of this study lies in its potential to guide future beach fill construction projects, promoting more efficient and cost-effective survey methods while adhering to accuracy standards and environmental considerations.

Social conflict surrounding public infrastructure projects has grown because of increasing project complexity and accelerated conflict propagation. This social conflict stems from various concerns ranging from environmental issues to regulatory and compliance requirements resulting in the intervention of various stakeholders with different interests. Against this backdrop, understanding the stakeholders involved and their dynamics is crucial for effective project management and smooth implementation of the project. Therefore, this paper introduces an analytical process utilizing ChatGPT to automatically identify stakeholders involved in the social conflict surrounding the public infrastructure project from news articles. As a result, a stakeholder network is constructed to delve into the complex stakeholder interrelationships and identify key stakeholders of the specific period. To explore the potential of the proposed process, an experimental case study of the Jeju 2^nd Airport project, which suffered from a high level of social conflict, was conducted. The proposed process enables timely analysis of the conflict situation which is crucial for successful conflict management. This study highlights the significance of a systemic approach to timely stakeholder analysis, setting the groundwork for a quantitative and up-to-date investigation of social conflicts around public infrastructure projects.

Recent advances in construction automation have led to increased use of deep learning-based computer vision technology for construction monitoring. However, monitoring systems based on supervised learning struggle with recognizing complex risk factors in construction environments, highlighting the need for adaptable solutions. Large multimodal models, pretrained on extensive image-text datasets, present a promising solution with their capability to recognize diverse objects and extract semantic information. This paper proposes a methodology that generates training data for multimodal models, including safety-centric descriptions using GPT-4V, and fine-tunes the LLaVA model using the LoRA method. Experimental results from seven construction site hazard scenarios show that the fine-tuned model accurately assesses safety status in images. These findings underscore the proposed approach's effectiveness in enhancing construction site safety monitoring and illustrate the potential of large multimodal models to tackle domain-specific challenges.

The task of vision safety monitoring in construction environments presents a formidable challenge, owing to the dynamic and heterogeneous nature of these settings. Despite the advancements in artificial intelligence, the nuanced analysis of small or tiny personal protective equipment (PPE) remains a complex endeavor. In response to this challenge, this paper introduces an innovative safety monitoring system, specifically designed to enhance the safety monitoring of working both at ground level and at elevated heights. This novel system integrates a suite of sophisticated technologies: instance segmentation, shape classification, object tracking, a visualization report, and a real-time notification module. Collectively, these components coalesce to deliver a safety monitoring solution, ensuring a higher standard of protection for construction workers. The experimental results…..

Monitoring unsafe activities on construction sites is challenging due to a variety of factors including the diversity of tasks and workers involved, the potential of human error and lack of real-time hazard detection. With technological advancements, several digital technologies have been proposed and applied to improve the monitoring process. Despite the potential of these technological advancements to reduce manual effort in traditional monitoring, the challenge lies in selecting and implementing the technology that best meets the specific needs of contractors. This paper aims to streamline the research of digital technologies in the construction domain by achieving three key objectives: (1) classify the types of unsafe activities that can be monitored automatically, (2) determine the specific data required for effective monitoring processes, and (3) identify the technologies that can facilitate such data collection process. We conduct a systematic literature review on cutting-edge technological studies to achieve the research aims. The findings of this research serve as a valuable resource for construction practitioners, offering insights into both the benefits and limitations of digital technologies in enhancing the monitoring process. Moreover, the study recommends preparatory elements that practitioners should undertake to integrate these technologies effectively into their monitoring frameworks. The study empowers practitioners by providing a deep understanding, enabling them to create a comprehensive safety management program aligned with the digital transformation process.

This study investigates the potential of Retrieval-Augmented Generation (RAG)-based Question Answering (QA) technology for accurate and relevant responses of Large Language Models (LLMs) to construction safety-related queries. Despite LLMs' advancements, their application, especially a Q&A Chatbot faces challenges due to hallucination and lack of domain-specific details. This study explores RAG's potentials to mitigate these issues by making LLM refer to external databases, such as the OSHA Field Safety and Health Manual, for generating precise and factual contents. A comparative analysis of different RAG technologies-Naïve-RAG, Rerank-RAG, and Iterative Retrieval-Generation-demonstrates their effectiveness over traditional LLM approaches. The findings highlight RAG's significance in producing structured, fact-based responses, underscoring its superiority in addressing the domain-specific informational needs regarding construction safety practices. This research marks a step forward in the application of generative AI technologies to enhance safety standards and practices within the construction industry.

Construction site monitoring is pivotal for overseeing project progress to ensure that projects are completed as planned, within budget, and in compliance with applicable laws and safety standards. Additionally, it seeks to improve operational efficiency for better project execution. To achieve this, many researchers have utilized computer vision technologies to conduct automatic site monitoring and analyze the operational status of equipment. However, most existing studies estimate real-world 3D information (e.g., object tracking, work status analysis) based only on 2D pixel-based information of images. This approach presents a substantial challenge in the dynamic environments of construction sites, necessitating the manual recalibration of analytical rules and thresholds based on the specific placement and the field of view of cameras. To address these challenges, this study introduces a novel method for 3D visualization and status analysis of construction site objects using 3D reconstruction technology. This method enables the analysis of equipment's operational status by acquiring 3D spatial information of equipment from single-camera images, utilizing the Sam-Track model for object segmentation and the One-2-3-45 model for 3D reconstruction. The framework consists of three main processes: (i) single image-based 3D reconstruction, (ii) 3D visualization, and (iii) work status analysis. Experimental results from a construction site video demonstrated the method's feasibility and satisfactory performance, achieving high accuracy in status analysis for excavators (93.33%) and dump trucks (98.33%). This research provides a more consistent method for analyzing working status, making it suitable for practical field applications and offering new directions for research in vision-based 3D information analysis. Future studies will apply this method to longer videos and diverse construction sites, comparing its performance with existing 2D pixel-based methods.

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.

Recent wearable devices can measure workers' physical and mental stress levels in the workplace, enabling timely interventions or adjustments to improve safety, well-being, and productivity. However, stress is a subjective metric, response and recovery from stress varies depending on the individual's physical condition. This study is a preliminary study to test whether there are relationships between stress and physical conditions (i.e., body compositions) of individual workers. To find the relationship between various body compositions of the participants and their stress levels, Spearman correlation coefficients and linear regression analysis were conducted. The results showed a significant relationship between workers' stress level and their body composition. This suggests that by utilizing easily measurable body composition, customized stress monitoring for individual workers can be achieved, contributing to the prevention of construction accidents and the creation of a safer construction site.

Effective physical fatigue monitoring is crucial for ensuring the health, safety, and productivity of construction workers, given the physically demanding nature of their work and the challenging environment in which they operate. In recent years, wearable sensors have shown growing potential for physical fatigue monitoring among construction workers. However, such fatigue assessment methods exhibit a significant gap as they often overlook the impact of inter-individual variability, such as differences in height, weight, and body mass index, on physiological signals that indicate physical fatigue. Therefore, this study aimed to investigate the role of personal factors in altering physiological responses, thereby improving the reliability and accuracy of fatigue monitoring using wearable physiological sensors. To explore the impact of these inter-individual factors, we experimentally analyzed the relationship between personal characteristics, physiological signals, and physical fatigue. Our findings reveal that although the inter-individual factors may not be directly correlated with fatigue levels, they significantly affect fatigue through their influence on physiological signals. Incorporation of these factors into a random forest predictive model significantly enhanced its predictive performance. Furthermore, integrating personal features with other variables to create new features in the physical fatigue prediction model notably improves its accuracy, highlighting the potential for developing personalized fatigue detection systems.

This study reviews the recently conducted case studies to explore the innovative integration of Artificial Intelligence (AI) and Machine Learning (ML) in the domain of building facility management and predictive maintenance. It systematically examines recent developments and applications of advanced computational methods, emphasizing their role in enhancing asset management accuracy, energy efficiency, and occupant comfort. The study investigates the implementation of various AI and ML techniques, such as regression methods, Artificial Neural Networks (ANNs), and deep learning models, demonstrating their utility in asset management. It also discusses the synergistic use of ML with domain-specific technologies such as Geographic Building Information Modeling (BIM), Information Systems (GIS), and Digital Twin (DT) technologies. Through a critical analysis of current trends and methodologies, the paper highlights the importance of algorithm selection based on data attributes and operational challenges in deploying sophisticated AI models. The findings underscore the transformative potential of AI and ML in facility management, offering insights into future research directions and the development of more effective, data-driven management strategies.

Presently, prefabricated concrete panels are extensively employed in diverse construction projects across the globe due to their exceptional quality. To maintain the overall quality of these construction projects, it is crucial to ensure that the dimensions of precast concrete panels align with their designated design specifications. Therefore, it is essential to develop a methodology capable of quickly and accurately measuring the dimensions of precast concrete panels. Currently, there are many advanced technologies used to examine the dimensions of prefabricated concrete panels such as terrestrial laser scanning, which is prone to time consuming and cost inefficiencies. To address these limitations, this study suggests a computer vision-based approach that utilizes April Tag markers and images taken from a mobile phone to measure and evaluate the dimensions and quality of precast concrete panels. The proposed algorithm operates as follows: Initially, the RGB image coordinates are converted to the world coordinate systems using April tag markers. Following, the masks of the precast concrete components are extracted using the state-of-the-art Segment Anything Model (SAM). Finally, an algorithm based on image processing technique is developed to estimate the dimensional properties of precast concrete panels. The effectiveness of the proposed method is validated through preliminary experiments conducted in the field-scale precast slabs, and the result is evaluated by comparing to the manual measurement result.

This paper aims to make clear the transition process of information transfer in wooden post and beam construction in Japan during the introduction and development of machine precutting technology. The machine precutting technology started from 1970s and developed toward line production or automated CAD-CAM production and have got over 90 % market share in the Japanese newly built wooden houses. Based on literature review and interviews with manufacturers of precutting machines, the transition of wooden post and beam construction houses was divided into four periods: i) before the introduction of precutting, ii) the introduction of precutting (the early stage), iii) the automation of precutting, and iv) the maturity of precutting, and the review reveals how the information of design and processing was made and transfered in each period. In addition, the modeling and comparison of information transfer in each period visually shows the transition of information transfer with the development of machine precutting technology. Before the introduction of precutting, when carpenters were responsible for the generation of both design and processing information, information was coordinated at each stage of the process based on expert knowledge, but highly precise and uniform processing of machine precutting reduced the need for such coordination skills. On the other hand, the review suggested that the precut processing technology has changed step by step based on the traditional technical system, which has maintained the rational transfer of information.

Building Information Model (BIM) is increasingly being used in the research of construction. The demand for low-cost and efficient access to architectural models is also on the rise. However, generating a parametric model from a point cloud will face interference from other facilities and will be affected by the quality of the measured point cloud. This paper describes a method for generating parametric models from laser-scanned point clouds. With slice voxel selection and line segment detection, the structural framework of the walls can be quickly extracted. By reducing the impact of missing furniture and data on the room, the new approach is applicable to most raw point clouds. This method has potential in multiple directions such as rapid BIM modeling, large-scale room reconstruction, and robot spatial perception.

Construction is considered one of the most hazardous industries because of fatalities on site due to workers' unsafe behaviour. Occupational Health and Safety Practitioners are providing safety training through modern technologies like immersive and non-immersive Virtual Reality (VR). Workers are repeatedly exposed to construction hazards and thus become accustomed to underlying hazards. Providing simulated accidents in VR safety training aims to minimize worker risk habituation but repeated exposure to accidents can affect risk habituation toward the underlying construction risk. To this end, this study proposes a simulated accident VR safety training environment that exposes workers to repeated construction hazards and simulates a fall accident when workers don't follow the safety procedure. Longitudinal experiments were conducted, and participants' risk perception was measured using questionnaires. The results revealed that simulated accident safety training has potential effects on construction workers. The outcomes of this study lay the foundation for further studies to employ a VR safety training environment that enables workers to experience simulated accidents. This contributes to the development of an improved VR safety training design, taking into account the appropriate interval at which it should be provided. Such an approach can help workers become more sensitized to construction risks.

This study introduces a comprehensive framework for 4D safety analysis in construction site layout planning (CSLP), using a Site Information Model (SIM) environment to enhance spatial hazard identification and effectively integrate it with activity-based safety management. The framework, grounded in a continuous-space layout approach, accurately positions objects to mirror temporary facilities' actual boundaries, incorporating spatial relationships and inherent safety hazards. It also features rasterization to translate layouts into a grid system. Central to this framework are three modules for spatial hazard identification: Visibility Analysis, Spatial Hazard Mapping, and Travel Path Analysis, designed to identify less visible spaces, assess spatial hazards, and simulate optimal travel paths considering safety aspects. By applying this framework to case studies of a residential complex and a commercial office project, the research demonstrates its practical utility in improving visibility and spatial hazard assessment, despite the inherently complex dynamics of construction sites. The study acknowledges challenges, such as the reliance on safety managers' experiential knowledge for setting hazard parameters and the need for further development in integrating these insights with activity-based safety management. It underscores the framework's significant potential to advance construction safety management by offering a method to preemptively recognize and mitigate spatial hazards. The approach promises not only to contribute to accident prevention but also to enhance overall project performance by incorporating spatial and temporal dimensions of safety into CSLP. This research marks a significant step toward a more holistic and integrated approach to construction safety, highlighting the importance of continuous improvement and adaptation in safety practices.

Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) epitomize the technological frontier of the Fourth Industrial Revolution, bearing significant relevance to the construction sector. These immersive technologies, closely aligned with the burgeoning metaverse paradigm, have spawned a variety of applications within the construction industry, notably in the whole construction stage. However, their transition to on-field applications remains limited, especially in South Korea. This study aims to meticulously scrutinize the current landscape of VR, AR, and MR research in construction by delving into various overseas studies that employ these technologies across the construction lifecycle. Utilizing the RISS, Dbpia, KCI database, a systematic accumulation of bibliographic data from pertinent research papers will be conducted to discern the prevailing research trends and the practical implications of VR, AR, and MR in Korean AEC field. The analysis will encompass a review of the goals, methodologies, and outcomes of these studies, providing a scaffold for future research in this domain. The investigation will also shed light on the potential synergy between these immersive technologies and Building Information Modeling (BIM), which encapsulates the whole construction lifecycle, thereby illuminating pathways for enhanced digital model utilization in pre-construction processes. This endeavor not only seeks to bridge the existing research gap but strives to propel the Korean AEC field towards a digitally-augmented horizon by leveraging the capabilities of VR, AR, and MR.

Among various risk factors that need managing in large scale complex infrastructure projects, geotechnical risk is one of the most prominent factor particularly for underground works like tunnels. Uncertainties in soil conditions cannot be avoided 100% even after extensive geotechnical investigations. Therefore, underground works face large delays and cost overrun especially for hydropower projects in developing countries. Its uncertainty ex ante and ex post directly cause increased transaction cost in terms of contract administration, claims, variation orders and disputes. It also reduces trust and increases opportunistic behaviors due to asymmetric information between the parties. Subsequently, parties are spending more time on claim management rather than handling the project execution. Traditional project management tools are becoming less effective under these conditions. FIDIC published the Conditions of Contract for Underground Works wherein a Geotechnical Baseline Report (GBR) sets out the allocation of risks between the parties for subsurface physical conditions determining the foreseeable and unforeseeable conditions. At the same time, Building Information Modeling (BIM) is being adopted for efficient design, quality control and cost management. In this study, soil classification along the tunnel alignment for on-going hydropower projects is modelled in the virtual environment of Autodesk Revit (2024). The actual soil encountered along the tunnel during construction stage can be compared with the baseline conditions. In addition, BIM serves as a central source providing symmetric information to the Parties to develop an environment of trust and coordination. It is anticipated that these tools will improve the project management skills for underground works through minimizing the opportunistic behavior and transaction cost.

Despite the acknowledged benefits by incorporating daylighting in the lighting system of office buildings to enhance energy efficiency and ensure occupants' well-being, a significant gap in understanding the integration of daylighting control system (DCS) with Building Information Modeling (BIM) exists, which can lead to improved energy efficiency and enhanced building design, specifically regarding the impact of daylight on occupant comfort. Previous studies have highlighted the potential of BIM to revolutionize both architectural design and building performance. However, an untapped potential of BIM in facilitating daylighting control in office areas is yet to be explored. The significance of this study lies in prioritizing occupants' well-being and enhancing building performance. This research identifies the feasibility of BIM-enabled DCS through a literature review from three perspectives: BIM-enabled DCS and daylight strategies, BIM-assisted façade system improvement, and user-centric daylight utilization within BIM platforms. As for results, a sensor network diagram illustrating network structure, data flow, and connections between devices of BIM enabled daylight control system for office buildings are established. Additionally, a BIM assisted daylight control strategy diagram is presented to outline user-centric control facilitated by BIM platform. In terms of contribution to the body of knowledge, this research will provide a comprehensive synthesis of existing literature in this domain. Additionally, this research could provide architects, DCS designers, and sustainable building professionals with potential advancements and inspirations to promote energy-efficient and user-centric building design in the future.

Railway track facility management (FM) is an intricate and multifaceted discipline that necessitates precise data management and scheduling for ensuring the safety and efficiency of railway operations. Although the Industry Foundation Classes (IFC) version 4.3 has incorporated railway infrastructure into its data schema, it still falls short in catering to the specialized needs of track FM. This paper presents an exhaustive extension to the IFC schema, specifically designed to address the challenges and complexities inherent in railway track FM. A two-step approach was employed in the development of this extension. The initial phase involves the development of a Unified Modeling Language (UML)-based conceptual model, encapsulating four pivotal elements: "component" for track asset and condition identification, "action" for the related tasks during track FM, "resource" for required materials and equipment as well as involved actors, and "operation" for track operation information capturing. This conceptual model serves as an intricate blueprint, offering a comprehensive structure for various FM facets. Thus, the proposed IFC extension is developed and aligned consistently with the conceptual model, forming an integrated, interoperable data management framework that can be easily adapted into the openBIM environment. The efficacy and applicability of the proposed extension are substantiated through real-world case studies, thereby demonstrating its capability to significantly enhance data visualization, interoperability, and overall decision-making in railway track FM.

This paper presents the development of an optimization framework for road slope design. Recognizing the limitations of current manual stability analysis methods, which are time-consuming, are error-prone, and suffer from data mismatches, this study proposes a systematic approach to improve efficiency, reduce costs, and ensure the safety of infrastructure projects. The framework addresses the subjectivity inherent in engineers' decision-making process by formalizing decision variables, constraints, and objective functions to minimize costs while ensuring safety and environmental considerations. The necessity of this framework is embodied by the review of existing literature, which reveals a trend toward specialization within sub-disciplines of road design; however, a gap remains in addressing the complexities of road slope design through an integrated optimization approach. A genetic algorithm (GA) is employed as a fundamental optimization tool due to its well-established mechanisms of selection, crossover, and mutation, which are suitable for evolving road slope designs toward optimal solutions. An automated batch analysis process supports the GA, demonstrating the potential of the proposed framework. Although the framework focuses on the design optimization of single cross-section road slopes, the implications extend to broader applications in civil engineering practices. Future research directions include refining the GA, expanding the decision variables, and empirically validating the framework in real-world scenarios. Ultimately, this research lays the groundwork for more comprehensive optimization models that could consider multiple cross-sections and contribute to safer and more cost-effective road slope designs.

Clashes between architectural, structural, and mechanical, electrical, and plumbing (MEP) systems are unavoidable as each discipline typically develops its own BIM models prior to federation. Commercial model checkers identify these clashes but do not classify them with respect to their severity, requiring every clash to be evaluated manually by the parties involved. Moreover, the assessment of their severity can be subjective and open to misinterpretations. To address these inefficiencies, an ontological approach was employed exclusively for clashes between multi-disciplinary BIM models. For a given clash, the ontology linked two elements, and encompassed their relevant geometric data and topology, which were retrieved using Navisworks and Python mesh packages. The clashes, distinguished as hard and soft, used separate approaches to classify their severity. Hard clashes employed machine learning algorithms to infer their severity based on geometric and project type features. Soft clashes used SPARQL-based rules which have predefined conditions for distinguishing clash severity based on semantic, geometric, and topological features. The ontology was implemented using RDF/OWL standards and programmed in Navisworks as an add-in module. Validation performed on an actual BIM model with 18,887 number of clashes showed that the ontology enabled highly accurate clash severity detection for both hard and soft clashes.

Recently, digitalization technologies for data analysis have become a global issue. As a result, in the construction market, Building Information Modeling (BIM), which is a core technology of smart construction, is being actively utilized not only in the architectural sector but also in the civil engineering field worldwide. In this study, the process of creating BIM models using a 3D scanner is examined, and automated extraction of numerical information for infrastructures necessary for library creation is conducted. In experiments utilizing infrastructurs such as retaining walls and employing algorithmic methods, the accuracy of cross-sectional numerical information for each retaining wall was confirmed to be over 95%. This enables not only BIM modeling but also the generation of drawings for facilities lacking BIM drawings by confirming the shape information of infrastructures, thus facilitating efficient maintenance.

The construction sector is known as a main source of increased CO2 emissions, and BIM-LCA integration is expected to reduce the environmental burden by helping to control emissions and energy during the basic design stage. However, numerous studies showed that LCA is complicated as it requires great efforts in creating BOQ and linking multiple databases together. In addition, a precise LCA requires LOD 300, a level of detail that often takes a lot of effort to establish at the basic design stage. To address this issue, this study proposes a BOM-based BIM-LCA integrated model that uses a combination of Uniclass, IDEA, and the Calculation Formula Library, which focuses on the development and exploitation of information, even if the 3D Object's Level of Detail is low. The model allows the decomposition of the building information in order to create BOM and BOQ at a high level of detail from the basic design stage onward. Furthermore, it allows the flexible connection of data sources to perform LCA in a systematic manner.

In recent years, the construction industry has rapidly adopted offsite-manufacturing and distributed construction methods. This change brings a variety of challenges requiring innovative solutions, such as the utilization of AI-driven and generative design. Numerous studies have explored the concept of multi-objective generative design with genetic algorithms in construction. However, this paper highlights the challenges and proposes a solution for combining generative design with distributed construction to address the need for agility in design. To achieve this goal, the research delves into the development of a multi-objective generative design optimization using a weighted genetic algorithm based on simulated annealing. The specific design case adopted is an educational complex. The proposed process strives for scalable economic viability, environmental comfort, and operational efficiency by optimizing modular configurations of architectural spaces, facilitating affordable, scalable, and optimized construction. Rhino-Grasshopper and Galapagos design tools are used to create a virtual environment capable of generating architectural configurations within defined boundaries. Optimization factors include adherence to urban regulations, acoustic comfort, and sunlight exposure. A normalized scoring approach is also presented to prioritize design preferences, enabling systematic and data-driven design decision-making. Building Information Modeling (BIM) tools are also used to transform the optimization results into tangible architectural elements and visualize the outcome. The resulting process contributes both to practice and academia. Practitioners in AEC industry could gain benefit through adopting and adapting its features with the unique characteristics of various construction projects while educators and future researchers can modify and enhance this process based on new requirements.

This study was conducted to establish a hydrogen pipe network integrated control platform architecture for integrated management of full cycle information and data using digital twin technology, which is widely used in general industry and construction industry. To this end, we present a hardware configuration diagram for platform operators and service delivery methods, and an architecture(functional configuration diagram) for the development of a hydrogen pipe network integrated control platform. The results of this study conducted through the above process are summarized as follows. First, to establish the platform basic structure, the platform hardware configuration diagrams for WEB server, WAS server, DB server, BIM file conversion server, storage, and backup were presented. Second, the architecture(functional configuration diagram) for the hydrogen pipe network platform, platform utilization interface, external system, hydrogen pipe network standard system, and BIM-based hydrogen pipe network digital twin construction was presented to secure consistent data and manage information standards for each construction stage.

In an era marked by rapid technological advancements, the term "metaverse" has emerged at the forefront of discussions, yet its conceptualization remains nebulous, especially in specialized domains such as construction. The metaverse represents an interconnected digital realm where physical and virtual realities converge, enabling transformative experiences and collaborations. This study seeks to disambiguate the notion of the metaverse, particularly contextualizing it within the construction industry's paradigm. By juxtaposing the metaverse with existing technologies like Building Information Modeling (BIM) and digital twins, this paper elucidates the unique technological components that would define a construction-centric metaverse. This paper highlights precepts and requirements for a construction domain metaverse. Potential applications of the metaverse within construction settings are explored, offering practitioners insights into avenues for research and development. This research aims to offer a guide for industry professionals, technologists, and researchers, providing clarity on harnessing the metaverse's capabilities effectively and setting the foundation for its meaningful integration in construction endeavors.

The modeling and simulation of built environments are crucial preliminary steps for their design, planning, and management. Among various simulation methods, agent-based modeling (ABM) has recently gained traction for simulating built environments due to its ability to effectively model and capture complex interactions between simulated entities. The increasing applications of ABM for the simulation of built environments necessitate a comprehensive review of past scientific endeavors with positive accomplishments and those that remain unsolved. This study seeks to address this gap by reviewing ABM and its applications in the simulation of built environments, with a specific focus on the planning and design phases. First, the research introduces ABM and its unique features concerning the simulation of built environments. Second, it conducts a systematic review of past studies in the planning (e.g., feasibility analysis, risk management, and scheduling under constraints) and design (e.g., automated design, collaborative design, improving operations, and facilitating evacuation) aspects of built environments. Finally, following the in-depth review and subsequent analysis, the study identifies the strengths and weaknesses of using ABM for simulating the built environments. The study concludes with a remark on potential future research directions to overcome the limitations of the existing studies.

Progress monitoring and quality control using as-built Building Information Modeling (BIM) are actively applied to construction industry. In order to effectively perform these management works, Scan-to-BIM is a key process to create as-built BIM models. In the Scan-to-BIM process point cloud segmentation is a critical task to identify object semantic information from point cloud data. While segmentation methods of main structural components such as walls, slabs, columns, and ceilings are actively studied and used for the management works, segmentation considering the finishing works of these components is still challenging. Therefore, this study proposed a point cloud segmentation method that considered wall finishing information, utilizing both point clouds and 2D images acquired from terrestrial laser scanners. The proposed method is composed of three main steps: 1) Segmenting as-built point clouds of main structural components through the comparison with as-planned BIM. 2) Applying a SegFormer material segmentation model that trained with wall finishing data (2D images) from terrestrial laser scanners to segment wall finishing information in 2D images. 3) Labelling the point cloud with recognized wall finishing information using back projection based on camera pose data. The proposed method is expected to contribute to the enchantment of the level of details (LoD) in as-built BIM and be useful in progress monitoring and quality control of finishing works.

The development of immersive virtual environment (IVE) technologies has allowed for virtual simulations and exploration of architectural spaces before building the facilities. Although various researchers have implemented IVEs to demonstrate their effectiveness, these rigorous methods for evaluation have obtained little attention. For education facilities, learning environments are crucial factors influencing students' academic performance and attention. Previous studies have evaluated the capabilities of spaces in terms of the learning performance of students in actual conditions. However, various spatial features cannot be experienced in real-world situations despite the introduction of IVEs that can validate the learning performance. This study aims to propose a framework to compare learning abilities in real space and identical ones implemented by two different methods: Virtual Reality and Mixed Reality. To this end, various cognitive and creativity tests are conducted i.e., N-back, Go/No-go, Spatial working memory updating, and Torrance Test of Creative Thinking-verbal tests. Then, a comparison is conducted to show cognition and creativity between real and virtual experiences.

In construction projects, the safety and productivity of machinery operations are of paramount importance. Contemporary research and industry endeavors predominantly concentrate on equipping machine operators with sensory information and establishing a comprehensive situation-aware operating environment, such as virtual reality-based machine manipulation training. However, significant limitations exist in direct information exchange and processing by on-site personnel. Notably, research on analyzing communication patterns in construction machinery operations remains scarce despite its critical role in preventing hazardous instructions/actions and enhancing machinery work efficiency. Thus, this research aims to (1) develop a novel interpreter modeling system predicated on millimeter-wave radar technology and (2) select the crane as an illustration to investigate the potential applications of this emerging communication paradigm during construction machinery operations. In this investigation, a spatial gesture signal interpreter was devised specifically for machine operators and signalers to augment the quality of communication during the execution of spatial localization tasks. Corresponding limitations that will be encountered in current communication systems were also addressed. This research uses a 60 GHz millimeter wave radar as a gesture trajectory detector, with the benefits of portability and robustness. Its millimeter-level precision enables the capture of highly accurate micro-gestures. The research constructs a novel 3D Spatial Gesture-based Trajectory Modeling system, which will be compared with traditional communication models in future research stages.

Building Information Modeling (BIM) technology has been widely adopted in the construction industry. However, a challenge encountered in the operational phase is that building object data cannot be updated in real time. The concept of Digital Twin is to digitally simulate objects, environments, and processes in the real world, employing real-time monitoring, simulation, and prediction to achieve dynamic integration between the virtual and the real. This research considers an example related to indoor air quality for realizing the concept of Digital Twin and solving the problem that the digital twin platform cannot be updated in real time. In indoor air quality monitoring, the ventilation rate and the presence of occupants significantly affects carbon dioxide concentration. This study uses the indoor carbon dioxide concentration recommended by the Taiwan Environmental Protection Agency as a reference standard for air quality measurement, providing a solution to the aforementioned challenges. The research develops a digital twin platform using Unity, which seamlessly integrates BIM and IoT technology to realize and synchronize virtual and real environments. Deep learning techniques are applied to process camera images and real-time monitoring data from IoT sensors. The camera images are utilized to detect the entry and exit of individuals indoors, while monitoring data to understand environmental conditions. These data serve as a basis for calculating carbon dioxide concentration and determining the optimal indoor air exchange volume. This platform not only simulates the air quality of the environment but also aids space managers in decision-making to optimize indoor environments. It enables real-time monitoring and contributes to energy conservation.

Building Information Modeling (BIM) involves the integration of equipment information and component parameters across various engineering disciplines. The complex processes during model construction can lead to human errors. Furthermore, design changes often occur at various stages of the building's lifecycle, requiring designers and modelers to make timely modifications, resulting in significant costs and time consumption. Mechanical, electrical, and plumbing (MEP) design is considerably more complex than architectural design. Therefore, this study focuses on the automatic generation of a heating, ventilation, and air conditioning (HVAC) ductwork model with MEP design through BIM. Dynamo, a visual programming language (VPL), offers features such as arrangement, connectivity, and scalability. Thus, this research applied Dynamo to develop the Automatic Ductwork BIM Model Generation System. The BIM model generated by the system facilitates collaborative efforts and enables the analysis of HVAC System Conflicts. The system extracts coordinates for air handling units, supply air, and exhaust air outlets. The equipment is automatically positioned based on these coordinates, and the corresponding duct paths are generated by reading CAD files. At each duct connection point, appropriate fittings are fabricated according to specifications and dimensions. The duct system is configured with distinct colors, and the results are visualized in Revit, facilitating HVAC system clash detection in the future. This study undertakes a real project to validate the proposed system and processes and assesses its impact on modeling efficiency, real-time responsiveness, and accuracy, realizing automated ductwork generation.

Various facilities such as safety railings and safety nets are used to prevent safety accidents at construction sites. As construction progresses, additional safety facilities may need to be installed or existing facilities may be damaged, so continuous management is required. To manage safety facilities, it is best for safety managers to check them directly, but there are parts of the site that cannot be checked directly. This study proposes a method to identify the installation status of safety facilities using photogrammetry. A drone is used to take photos and a three-dimensional model is created using these photos. By reviewing the implemented model to determine whether safety facilities are installed, it is possible to secure the safety of construction sites.

The importance of 3D city models for sustainable urban development and management is underscored, but existing models often overlook indoor spaces and attribute information. This issue can be tackled with BIM models, though the conventional method requires accurate and extensive information, incurring considerable time and cost in data collection and processing. To overcome these limitations, this study proposes a method to automatically generate BIM models that include indoor spaces using street view images. The proposed method uses YOLOv5 to identify façade elements and DBSCAN to normalize façade layouts, facilitating the generation of detailed BIM models with a parametric algorithm. To validate the method, a case study of a building in Korea was conducted. The results showed that indoor spaces similar to the actual building were generated, with an error rate of object quantities between 8.46% and 9.03%. This study is anticipated to contribute to the efficient generation of 3D city models that incorporate indoor spaces.

This research presents a method for Augmented Reality (AR) object superimposition leveraging reinforcement learning techniques to significantly reduce manual input during the exploration of digital information on construction sites. A distinctive feature of this approach is the application of a reinforcement learning neural network, trained with pairs of real and virtual view images, for AR superimposition. This approach enables the precise adjustment of the virtual camera's position and orientation within a virtual scene, aiming to seamlessly integrate AR objects into real-world views. This research initially focuses on simpler scenarios involving 2 and 3 degrees of freedom for orientation and position adjustments. The purpose is to explore the feasibility of the application through those experiments, with the expectation that the results would be interpreted positively. These initial findings highlight the promise of the suggested method in improving AR applications, especially within the construction sector, by enabling more natural and precise merging of virtual and physical objects, without requiring user intervention.

This study introduces a novel system for the 4D simulation of Building Information Modeling (BIM) objects in mixed reality (MR) environments, addressing challenges of file format compatibility and data management. By developing a system that effectively partitions and manages BIM data, specifically utilizing the OBJ format, the study enables precise simulation of construction project changes over time. This advancement enhances visualization and decision-making in construction project planning and execution. The study highlights the integration of BIM and MR technologies, facilitating enhanced project management and operational workflows. Future work will focus on expanding compatibility with various BIM data formats and MR devices, and integrating AI-based data analysis for more accurate and efficient construction simulations.

In Japanese steel fabricators, significant time is devoted to drafting fabrication drawings compared to the fabrication process itself, highlighting a need for efficiency improvements. A primary issue is the inadequate consideration of constructability at the design stage. Steel fabricators often sign contracts based on partially completed designs, leading to frequent modifications from the creation of fabrication drawings up until just before fabrication begins, which hampers productivity improvements. To address this challenge, this study proposes separating and contracting engineering from fabrication early in the design phase through front-loading detailed design. If front-loading is feasible, it could reduce the frequency of changes and corrections, improving the drawing process and potentially enhancing efficiency in steel fabricators. Furthermore, the study explores what constitutes feasible engineering for front-loading, considering that steel fabrication involves aspects beyond mere structural design.

According to the South Korean Ministry of Land, Infrastructure, and Transport, instances of defect dispute resolutions, primarily between construction contractors and apartment occupants, have been occurring at an annual average of over 4,000 cases since 2014 to the present day. To address the persistent issue of disputes between contractors and occupants regarding construction defects, it is crucial to use customer sentiment analysis to improve customer rights and guide construction companies in their efforts. This study presents a methodology for effectively managing customer complaints and enhancing feedback analysis in the context of defect repair services. The study begins with collecting and preprocessing customer feedback data. Semantic network analysis is used to understand the causes of discomfort in customer feedback, revealing insights into the emotional sentiments expressed by customers and identifying causal relationships between emotions and themes. This research combines text mining, and semantic network analysis to analyze customer feedback for decision-making. By doing so, defect repair service providers can improve service quality, address customer concerns promptly, and understand the factors behind emotional responses in customer feedback. Through data-driven decision-making, these providers can enhance customer rights and identify areas for construction companies to improve service quality.

Large-scale infrastructure projects are often characterized by a high degree of innovation but are also accompanied by uncertainties and risks. In university research projects, the complexity is even higher because, in addition to unknown factors, like unclear paths forward, they come with adaptive targets. These lead to higher uncertainties, and it is crucial to adhere to the magic triangle of project management to continuously monitor and ensure the achievement of the project goals. Additionally, in research, a dead-end does not inevitably lead to the failure of the project, but mostly it leads to new opportunities. This paper explores the relationship between project goals and risk management of university projects to create a tailored project plan. First, the challenges and needs were mapped out through a survey of ten project leaders at our university. This survey helped us understand the problems and led to a new approach for university projects. Second, the new approach including adjusted risk management and methodology is developed. Third, the results were combined with a project plan using a probabilistic methodology to modify the approach through predictive evaluations. This includes integrated cost, time, and risk analysis. The probabilistic results are based on a Monte Carlo simulation. In the paper differences and similarities between the management of large-scale infrastructure projects and research projects are highlighted. Therefore, a process for creating an exemplary, holistic project plan using a digital twin, which helps to optimize the management strategy for research is presented. Furthermore, the project plan is tailored to the needs of applied research, so that the results of the research can be useful for the industry.

Public-private partnership (PPP) projects are characterized by the bundling of construction and operation phases and risk sharing, which motivate private sponsors to enhance project efficiency throughout its life-cycle. However, internal conflicts of interest among sponsors can potentially distort these incentives. Building on agency theory, this study presents a game model to examine the effect of internal conflicts among private sponsors on bundling and risk sharing. The results show that the degree of the bundling and risk transfer from the government to private sponsors depend on the sponsors' shareholding and capabilities. This study contributes to the PPP knowledge body by introducing the internal conflicts among sponsors into the incentive mechanism of risk-sharing between the government and private sponsors. The findings also provide support for the government to formulate risk-sharing strategies and shed light on the sponsor selection of PPP projects.

Researching the phenomenon and answering research questions to generate an integrated management system to manage the post-disaster reconstruction phase calls for a well-created or structured framework for the research methodology plus a mixed method. Aim and Purpose: To produce an Integrated Management of Environmental, Occupational Health & Safety and Quality Management Systems, a Disaster Management framework for Post-Disaster Reconstruction Projects Management and Empirically Validate the Framework. Research methodology and mixed methods framework study activities are the following stages: Literature Review, Formulating Research Methodology and Mixed Methods, The Research Aim and Objectives, The Research Question Statements - Mixed Methods (Qualitative and Quantitative), Planning and Procedures for Participants and Service Users' Involvements, Designing of Questionnaires and Surveys Research Question, Using Mixed Method Design Data Collection and Analysis with NVIVO and Final Development of the Integrated Management System for Post-Disaster Construction Management Phase, Recommendation and Conclusion. OBJECTIVES: Explore the awareness and practice of environmental, occupational health, safety, and quality management systems, as well as disaster management practices for the post-disaster reconstruction phase and routine reconstruction. Furthermore, the mixed methods part addresses the research aim and objectives. Then, it facilitates the achievement of the research goals and contribution to the knowledge and development of an integrated management system for the post-disaster reconstruction management phase. The end addresses the significance of the research methodology and mixed methods framework developed.

The Nigerian Public Procurement Act (PPA, 2007) is still lagging in its vague definition of the requisite qualifications for procurement managers. Hitherto, tender evaluation committees have been constituted arbitrarily without any feed-forward structure. An advanced close-ended support system for constituting tender evaluation committees is hereby presented. A theoretical concept was followed by a design science methodology. A deterministic team-building model for tender evaluation committees is demonstrated. The optimisation algorithm relies on an additive analytical sequence and a complex factoring of anticipated group interaction. Match models, which proactively determine the effects of complementary participation of team players were adapted. It is an emerging technical solution for a relatively ignored research concern, which now makes a scientific team-building processin procurement possible through itinerant simulation. The deterministic strategy was validated by a hypothetical entity case while augmenting a few data entries. A restrictive decision space was created to enable the determination, identification and composition of tender evaluation team members' inclusion criteria in a committee. A questionnaire drawn from ten (10) procurement managers from Nigeria helped validate the soft team-building model. The support system is inflexible for accommodating probabilistic entries where target data is discontinuous. Minimising the discretionary assemblage of personnel to evaluate tenders is a potential benefit of adopting the support system. Public procuring entities can deploy the soft team-building support system with little adjustments to accommodate entity-specific peculiarities.

The accident rate in the South Korean construction industry has increased by 50% over the past ten years, reaching seven times the average growth rate of the entire industry. However, the number of management personnel at construction sites is decreasing, making it increasingly difficult to establish a safety monitoring system through professional personnel. This study aims to develop an intelligent control system to address the problem of insufficient management personnel and support the establishment of a continuous safety monitoring system. This system consists of a mobile information collection robot (S-BOT) and an intelligent algorithm. The visual information collected by S-BOT can be analyzed in real-time using computer vision-based intelligent algorithms to detect unsafe situations. The results of this study will contribute to preventing unnecessary social and economic losses by maximizing safety management efficiency and supporting timely decision-making through the sharing of information provided by the intelligent control system.

Cost measurement plays the fundamental role within the modern construction and project management models, where not only materials, labors and services are measured by cost but also programme delays, quality defects and project risks are converted to be measured as cost. However, the problems of cost measurement models have been analyzed only from the aspect of owners and contractors who construct the buildings, not from the aspect of users who use buildings. In this article, analysis of data surrounding the current high inflation of construction costs in England is conducted, to find out its route causes within the current and historical development of construction cost measurement models. The conclusion is that current cost measurement models are based on the aspect of owners and contractors, which is to assess buildings as monetary asset for short-term taxation purpose, without due regard how buildings are used by users for long-term. Alternative cost measurement models based on the aspect of users are proposed, which assess buildings as functional asset for its long life-cycle. Pros and cons of these two adverse models are discussed in details, and harmonization between owners, contractors and users are sought, in order to arrive at a more consistent cost measurement approach which can be equally applied to buildings and built-environment by all stakeholders involved.

This paper presents a study that compares and analyzes the practices of utilizing BIM (Building Information Modeling) for construction cost estimation in Japan and China. The study delves into the nuanced differences and similarities in cost estimation methodologies between the two countries. The overview section explored their respective standard specifications, and the methodologies for construction quantities take-off, covering both the bottom-up estimating approach and the all-in unit rate approach. Additionally, the paper delves into the item code system used in BQ (Bills of Quantities), elaborating on its introduction and practical application. The paper meticulously breaks down the process of quantities take-off facilitated by BIM models and cost-estimating software. The study also delves into the developmental trends in comprehensive BIM standards about construction cost, coupled with the proposition of a BIM code for seamless integration into construction cost practices as part of a forward-looking research plan. In conclusion, the paper encapsulates the comparative findings, highlighting the strengths, weaknesses, and potential areas for improvement in the BIM-enabled construction cost estimation practices of Japan and China. This study contributes to a deeper understanding of the utilization of BIM technology in the construction industry, offering valuable insights for practitioners, researchers, and policymakers alike.

Warehouses and Self-Storage facilities are essential for e-commerce operations, providing inventory, distribution, and delivery space. As per the Bureau of Labor Statistics, the number of warehousing and storage businesses in the United States increased from 15,152 in 2010 to 20,002 in 2021, showing continuous growth over the decade. Given the recent surge, there is a need to better understand the impact of these delivery methods on the quality metrics of these facilities. This study aims to contrast the measure of quantitative quality performance of design-build (DB) versus traditional design-bid-build (DBB) project delivery methods (PDM) for warehouses and self-storage facilities. This research conducted a literature review to document quality metrics to compare DBB and DB on project performance. ANOVA was conducted to compare the significant difference between the means to compare the performance between delivery methods and project outcomes across these critical areas. With these insights, owners can make informed strategic decisions about selecting the optimal PDM for future storage facility projects based on their unique quality and scope management objectives.

The South Korean construction industry has grown larger and more complex through collaboration with various fields. In the construction industry, faced with the era of the 4th Industrial Revolution, the importance of project managers is growing for successful construction projects. Amid these changes, it is necessary to grasp the capabilities and importance of project managers for the development of the South Korean construction industry and analyze how they affect it. This study aims to review the required capabilities of the project manager and suggest implications with a comparison of current capabilities and importance. To this end, a survey was conducted on the importance and Performance of the project manager. Using collected data, changes in capabilities are identified by comparing the Performance and importance of project managers respectively in 2010 and 2022 through the Importance - Performance Analysis (IPA) method. The analysis results show changes in the Performance and importance of project managers according to changes in the construction environment, and the insufficient capabilities of them. Based on this research, it is expected that efficient construction management will be possible amid changes in the construction environment by presenting the method to improve necessary capabilities to project managers in South Korea.

The availability of PMT(Project Management Tool) in the market has been increasing rapidly in recent years and Significant advancements have been made for project managers to use for planning, monitoring, and control. Recently, studies applying the Reinforcement-Learning Based Construction Schedule Simulation algorithm for construction project process planning/management are increasing. When reinforcement learning is applied, the agent recognizes the current state and learns to select the action that maximizes the reward among selectable actions. However, if the action of global optimal points is not selected in simulation selection, the local optimal resource may receive continuous compensation (+), which may result in failure to reach the global optimal point. In addition, there is a limitation that the optimization time can be long as numerous iterations are required to reach the global optimal point. Therefore, this study presented a method to improve optimization performance by increasing the probability that a resource with high productivity and low unit cost is selected, preventing local optimization, and reducing the number of iterations required to reach the global optimal point. In the performance evaluation process, we demonstrated that this method leads to closer approximation to the optimal value with fewer iterations.

This systematic review comprehensively analyzes the application of computer vision in construction productivity measurement and emphasizes the importance of worker accountability in construction sites. It identifies a significant gap in the connection level between input (resources) and output data (products or progress) of productivity monitoring, a factor not adequately addressed in prior research. The review highlights three fundamental groups: input, output, and connection groups. Object detection, tracking, pose, and activity recognition, as the input stage, are essential for identifying characteristics and worker movements. The output phase will mostly focus on progress monitoring, and understanding the interaction of workers with other entities will be discussed in the connection groups. This study offers four research future research directions for the worker accountability monitoring process, such as human-object interaction (HOI), generative AI, location-based management systems (LBMS), and robotic technologies. The successful accountability monitoring will secure the accuracy of productivity measurement and elevate the competitiveness of the construction industry.

As cities continue to evolve and expand, the importance of accurately modeling and simulating urban environments to predict and assess various risk scenarios has become increasingly recognized. Since city simulation can capture the intricate dynamics of urban life, the versatility of city simulation has been demonstrated in numerous case studies across diverse applications. Owing to this capacity, city simulation plays a critical role in the disaster risk management field, especially in accounting for the uncertainties in natural/man-made disasters. For example, in the event of an earthquake, having detailed information about an urban area is instrumental for evaluating stakeholder decisions and their impact on urban recovery strategies. Although numerous research efforts have been made to introduce city simulation techniques in disaster risk reduction, there is no clear guideline or comprehensive summary of their characteristics and features. Therefore, this study aims to provide a high-level overview of the latest research and advancements in urban simulation under different hazards. The study begins by examining the simulation techniques used in urban simulation, with a focus on their applicability in disaster scenarios. Subsequently, by analyzing various case studies, this research categorizes them based on their unique characteristics and key findings. The knowledge gained from this literature review will serve as a foundation for subsequent research on simulating the impacts of urban areas under various hazards.

Construction industry records poor safety records annually due to a large number of injuries and accidents on construction jobsite. In order to improve existing safety performance, object detection approaches have been extensively studied using vision-sensing techniques and deep learning algorithms. Unfortunately, an insufficient number of datasets (e.g., images) and challenges that reside in manually collecting quality datasets constitute a significant hurdle in fully deploying object recognition approaches in real construction sites. Although advanced technologies (e.g., virtual reality) have attempted to address such challenges, they have achieved limited success because they still rely on labor-intensive work. A promising alternative is to adopt generative AI-based data augmentation methods attributed to their efficiency in creating realistic visual datasets and proven performance. However, there remain critical knowledge gaps on how such alternatives can be effectively employed by safety managers on real construction sites in terms of practicability and applications. In this context, this study establishes a framework that can identify effective strategies for improving object detection performance (e.g., accuracy) using generative AI technologies. The outcome of this study will contribute to providing guidelines and best practices for practitioners as well as researchers by exploring different generative AI-driven augmentation approaches and comparing the corresponding results in a quantitative manner.

Construction planners for hard rock tunnel projects often encounter practical challenges caused by inherent uncertainties in ground conditions and resource constraints. Therefore, planners cannot rapidly generate optimal excavation schedules for the shortest project durations with a given equipment fleet by considering the uncertainties in ground conditions. Although some schedule optimization methods exist, they are not tailored for resource-constrained hard rock tunnel projects. To overcome these limitations, the authors specified a formal Q-learning-based schedule optimization methodology for resource-constrained hard rock tunnel projects. States are defined according to the locations of tunnel faces under excavation. Actions consist of multiple and comprehensive heuristic-based rules, which are efficient methods for resource allocation. Rewards are the time intervals required between current states and next states. After that, the methodology is validated using a case study. The generated Q tables indicate (1) best actions under different states and (2) the shortest remaining durations when the project starts from specific (state, action) pairs. The results demonstrate that the optimal schedules can be obtained by applying the proposed methodology. Furthermore, it is beneficial for planners to rapidly assign optimal rules for each state under one ground condition scenario. The results further show the potential to consider the uncertainties in ground conditions using the information of possible ground condition scenarios provided.

Urban road maintenance creates potential risks for both road users and workers in addition to traffic congestion and delays. The adverse effects of maintenance work could be minimized through mitigation measures of work zone layout and construction arrangement, such as reducing the dimension of work zone segments and scheduling construction during low-traffic periods. However, these measures inevitably escalate construction costs. Consequently, decision-making in urban road maintenance necessitates a balance among multiple strategic objectives to facilitate optimal development via a comprehensive road maintenance management system. This study aims to propose an integrated framework to accomplish the multiple and conflicting objectives for maximizing safety and mobility while minimizing construction costs by optimizing the work zone layout and construction sequence dynamically. The framework enables the seamless information exchange among building information modeling (BIM), geographic information system (GIS), and domain-specific computational engines (DCE), which perform interdisciplinary assessments and visualization. Subsequently, a genetic algorithm is employed to determine the optimal plan considering multiple objectives due to its versatility in resolving highly complex conflict problems.

This study aims to facilitate the integration of digital technology into construction supervision by leveraging construction supervision checklists, a fundamental tool for inspections in this field. To achieve this, this research analyzed the tools and practices used in construction supervision, identifying 12 key supervision task types. These task types formed the basis for developing four distinct digital technologies, each tailored to specific inspection tasks based on practical feasibility. A checklist development process followed with the use of predefined criteria. Checklist items from the Ministry of Land, Infrastructure, and Transport of South Korea were analyzed and categorized according to the identified inspection task types. Digital technologies were then integrated for each task type, with corresponding supervision locations documented. This process enhanced the checklist's effectiveness by incorporating methods for digitizing tasks. The findings suggested that approximately 61% of traditional tasks could be potentially replaced by digital technology, highlighting the potential for successful digitization implementation. In addition, surveys among industry professionals provided insights into the level of productivity enhancement achievable through technology adoption compared to traditional practices, offering a basis for predicting productivity improvements across various disciplines. In conclusion, this research supports the effective integration of digital technology into construction supervision through an enhanced checklist. It also sheds light on practitioners' perceptions of technology usage and aids in developing strategies for technology adoption in this domain.

Effective project planning is essential in construction project management for timely delivery and economic benefit realization. Work packages are pivotal in this planning, providing clear organization and progress tracking. However, existing methods for creating work package schemes often overlook environmental sustainability, specifically carbon emissions-a growing concern in construction. This study introduces a tabu search-based optimization method for work package schemes, aiming to reduce both project costs and carbon emissions. A cost-carbon model is devised, and a tabu search algorithm is developed to identify the Pareto frontier for total project cost and carbon emissions. A case study shows the tabu search outperforms existing heuristics, reducing carbon emissions by 6.19% with a marginal cost increase of 0.9%. The algorithm's adaptability and generalizability suggest it could significantly enhance economic and sustainable outcomes in construction project planning.

Public construction projects have a significant impact on the local residents' economy and social lives due to their large scale and construction costs. If residents suffer losses and damages from public construction projects, it can lead to complaints, which can negatively affect to the projects, such as cost overrun and schedule delay. Therefore, the managerial efforts should be made to minimize these complaints. The purpose of this study is to analyze the complaints associated with construction projects based on their characteristics and assess the impact of complaints on the projects, aiming to minimize the complaints arising from construction projects. This study is conducted in three steps: 1) extracting the complaints' information from the existing construction projects, 2) analyzing the complaints based on projects characteristics using post-evaluation data, and 3) analyzing how the complaints are actually handled. Through this study, it is possible to understand the characteristics of complaints in actual public construction projects in Korea.

Construction projects in urban areas often disrupt pedestrian paths and expose pedestrians to risks by forcing them to detour onto roadways. Despite rising pedestrian fatalities and injuries near construction sites, most research predominantly focuses on the safety of on-site workers, with limited studies addressing pedestrian safety. This study aims to fill this gap by identifying environmental factors that cause discomfort to pedestrians, potentially leading to hazardous impacts. A total of 252 photos of streetscape areas near construction sites, including seven environmental factors (i.e., traffic cones, fences, barrier walls, materials, heavy equipment, roads, and sidewalks), were collected and evaluated by 41 participants using a 5-point Likert scale. The survey findings indicate that barrier walls enhance pedestrians' perception of safety. Conversely, it is observed that traffic cones, materials, and heavy equipment have adverse effects on pedestrian safety. These results underscore the need for enhanced safety measures targeting these high-risk factors to create pedestrian-friendly construction sites. This study contributes to developing more proactive pedestrian safety management strategies and ultimately reduces pedestrian injuries.

A project's success is directly guaranteed by the prevention of construction-related problems. Nevertheless, the prevention of quality issues frequently overlooks how issues are coupled with one another, which might result in a domino effect of quality issues. In order to solve the above problems, this work first preprocesses unstructured text data with quality problem coupling. Then the pre-processing data is used to build a knowledge base for the prevention of construction quality problems. Then the text similarity algorithm is used to mine the coupling relationship between the qualities and enrich the information in the database. Finally, some text is used as test object to verify the validity of the method. This study enriches the research around the prevention of building quality problems.

Many international development (ID) projects have continued to fail to deliver their intended socio-economic benefits to the beneficiary (i.e., a group of primary recipients of these benefits). In this research, drawing from project stakeholder and benefit management literature, we investigated how ID project performance is perceived by beneficiary, how beneficiary is engaged, and how such engagement contributes to ID project performance. The results from thirteen semi-structured interviews with those leading beneficiary engagement in ID projects in Indonesia showed that, as compared with other projects, ID projects place a stronger emphasis on benefit realization when it comes to performance evaluation given their focuses on achieving socio-economic objectives. Beneficiary engagement during the entire life cycle was found to be critical to benefit realization in ID projects. Although the specific methods employed can vary, the beneficiary engagement in ID projects usually take a participatory and longer-term perspective, aiming to facilitate not only the benefit realization but also the sustainability of the benefits over time. This research extends prior project stakeholder and benefit management literature to a specific stakeholder group (i.e., beneficiary) in a unique context (i.e., ID projects). It also offers practical insights to assist organizations effectively plan and manage beneficiary engagement in future ID projects.

A project virtual team in general refers to a group of geographically dispersed members who rely on communications technology to accomplish project tasks. This team structure has been increasingly utilized in project environments, especially post COVID, given its potential to accommodate flexible work arrangement, reduce project costs; and leverage knowledge and expertise from people around the world to enhance project performance. Drawing from the shared leadership literature and sixteen semi-structured interviews with practitioners involved in project virtual teams in Indonesia, we investigated how shared leadership is exercised in project virtual teams; and how it affects project performance. The results showed that shared leadership can be exercised through collaborative decision-making, collaborative task allocation/monitoring, and empowerment. In project virtual teams, shared leadership practices can trigger positive affective reactions from team members, leading to improved project delivery efficiency and better knowledge transfer. This study extends prior shared leadership literature, which predominately focuses on teams in permanent organizations, to a temporary project environment. It offers theoretical insights into the mechanism through which shared leadership affects project virtual team performance. Our findings also offer important implications for shared leadership practices in future project virtual teams.

How information is communicated and stored is an important factor in the decision-making process for architectural projects. With the recent spread of smartphones among all generations and the rapid expansion of various digital services, an environment has been created in which anyone can easily use digital services. In Japan, there are a variety of Social Media platforms, of which LINE is the most popular among people of all ages as a means of communication. In the business world, chat tools have emerged as convenient tools, and teams are increasingly using chat to exchange information on a dayto-day basis. In this study, the author will examine how information communication, storage, and decision-making, which have been carried out by conventional means, can be streamlined through the use of a combination of highly versatile Social Media services and 3D digital technology, which has become easier to introduce even in small companies over the last few years. The project will examine how information communication, storage, and decision-making can be streamlined through the use of 3D digital technologies, which have been introduced in the past.

In the dynamic construction industry, particularly within the United States Army Corps of Engineers (USACE), ensuring worker safety in high-risk environments is a critical challenge. This study explores the integration of wearable technology in construction safety, focusing on its potential to enhance personal protective equipment (PPE). The primary goal of this study is to understand the factors influencing USACE employees' acceptance and adoption of wearable technology. Additionally, the research aims to assess the experiences of employees who have already used such technology to identify its practical benefits and levels of user satisfaction. A mixed-method approach was employed to gather qualitative insights from interviews with USACE safety experts and quantitative data from an online survey of USACE personnel. The findings indicate a general reluctance among workers to adopt wearable technology for monitoring work activities, mainly due to privacy concerns, usability issues, and perceived additional workload. However, there is interest in technologies that provide direct safety benefits, such as hazard alerts. This study illuminates the gap between the potential benefits of wearable technology and its current level of acceptance in the construction industry. It identifies the need for strategies to enhance worker acceptance and offers recommendations for future research.

Near miss reporting is essential for improving safety performance in construction organizations. Traditional methods, however, often fail to sufficiently motivate worker participation due to a lack of incentives and the absence of secure, reliable, and transparent reporting mechanisms. This paper introduces a conceptual framework that leverages blockchain technology and token incentives to increase employee involvement in near miss reporting. The integration of blockchain ensures transparency and decentralization, while token incentives effectively monetize employees' efforts in reporting near misses. The study begins by identifying key modules essential for developing this framework and proceeds to detail their integration, illustrating a comprehensive process flow of activities within the system. This research significantly contributes to the enhancement of near miss reporting practices in construction, ultimately fostering safer and more proactive workplace environments.

The construction industry has been recognized as one of the most high-risk industries globally, promoting a shift towards enhancing safety culture to mitigate accident rates. With a notable good safety performance in Australia, this study therefore compares its advanced safety culture with the evolving safety culture in China through a systematic review of literature published over the last two decades. The aim of the research is to explore the influence of differing societal cultural contexts on the development of safety culture. The study covers various aspects of safety culture, including leadership and management commitment, regulatory environments, safety communication, workers'involvement, and organizational safety systems. Findings indicate a strong commitment from industry participants in both countries. However, there are notable differences in safety culture conceptualization and implementation. Australia showcases a mature safety culture, deeply integrated with stringent regulations and fostering individual proactive engagement. Conversely, China's safety culture, marked by rapid evolution, emphasizes regulatory compliance, with challenges in achieving broad worker participation. The analysis highlights that Australian construction workers' inclination towards a proactive approach in managing safety, in contrast to Chinese construction workers who tend to focus more on adhering to safety regulations than actively participating in safety initiatives. These findings emphasize the significant role societal culture plays in shaping construction safety cultures. The study's insights are instrumental for practitioners across the global construction industry, advocating for the adoption of nuanced, culturally sensitive safety management strategies to enhance safety outcomes.

A large number of construction accidents are caused by workers' unsafe behavior under excessive workload. Despite the demonstrated effectiveness and advantages of current portable electroencephalogram (EEG) devices in workload monitoring, accurate data acquisition remains challenging due to motion artifacts in dynamic environments. Consequently, most current research is limited to static conditions, thus restricting its application to construction tasks that inherently involve bodily movements. In this study, an innovative signal filtering framework is introduced that employs the principles of adaptive filtering to integrate acceleration signals containing motion information for the correction of motion artifacts in EEG signals. The experimental results demonstrate that this approach effectively eliminates motion-induced artifacts in EEG signals, thereby improving the preprocessing of hybrid kinematic-EEG signals acquired during bodily and muscular movements. By enhancing signal quality and reliability, this preprocessing framework aims to broaden the application of portable EEG devices for real-time workload monitoring among construction workers. This advancement is expected to enhance the practicality of EEG in construction safety management and ultimately contribute to safer construction practices.

The emergence of Building Information Modelling (BIM), reality data, Virtual Reality (VR), and Augmented Reality (AR) has significantly enhanced the collaboration between stakeholders in construction management. The utilization of VR/AR devices holds considerable potential for monitoring safety in complex and constrained working environments on the construction site. On the other hand, near-miss incidents remain an important early sign of struck-by accidents. However, research on early warning and prevention methods for this risk is still limited. This paper, therefore, presents a framework for on-site and off-site collaborative safety monitoring framework using augmented and virtual reality for near-miss incidents. In the proposed framework, three phases to develop a VR/AR-based safety monitoring system include (1) construction safety simulation environment, (2) localization-based interaction system, and (3) safety monitoring system. The system can undertake the processing of data and enables communication among disparate VR/AR devices. VR clients are observational tools and offer guidance, while the AR client stays onsite for construction tasks. All clients connect to a processing computer, which also works as a host. The system embedded in the AR device can trigger an alarm or receive signals from the VR client when a near-miss issue happens. Additionally, all device clients possess the capability to share data acquired from onsite monitoring cameras, thereby fostering effective discussions and decision-making. The efficacy of this cross-platform system has been validated through the implementation of an outdoor coordination case study.

Despite various government and institutional movements to promote implementation of smart construction, the utilization of smart technologies in the construction industry is still low compared to other industries. To take a systemic look at the impediments in the implementation of smart construction, this study identifies and analyzes the challenging factors of smart construction within the Korean construction industry. Through content analysis of relevant literature, including official documents, research reports, databases, 19 challenging factors have been identified. The intricate relationships among these challenging factors have been examined based on a hierarchy structure established by using the Interpretive Structural Modeling (ISM) approach. Furthermore, factors are classified into four distinct clusters by using the MICMAC analysis: driving factors, dependent factors, autonomous factors, and linkage factors. This classification delineates the interrelationships among the challenging factors and identifies the key factors that drive the system, which is different from that in traditional studies where the relative importance is generally given between factors. The findings will provide crucial information for policy designers and top-level authorities, indicating which challenging factors to prioritize limited resources and efforts. It will aid in formulating effective policies, standards, and regulations to foster the implementation of smart construction in the Korean construction industry.

Struck-by accidents on construction sites are one of the major accidents that need to be prevented. Poor visual environments (especially, dark environments) and multiple hazards appearing simultaneously can lead to struck-by accidents due to failure of hazard recognition by construction equipment operators. Therefore, this study aimed to assess multiple hazard recognition abilities of construction equipment operators in dark environments. To this end, virtual reality-based experiments were designed and conducted to collect data on three metrics for multiple hazard recognition abilities: (i) initial recognition time (IRT); (ii) average recognition time per hazard (ART); (iii) the number of false alarms (NoFA). The effect of the number of hazards on multiple hazard recognition abilities in dark environments was analyzed using two statistical methods: (i) Friedman test; (ii) Spearman correlation analysis. The number of hazards has a significant effect on multiple hazard recognition abilities. The data groups for IRT and ART, categorized by the number of hazards, had statistically significant differences. In addition, the number of hazards have negative correlations with IRT and ART. Especially, multiple hazard recognition abilities were lowest when the number of hazards was extremely low (i.e., the number of hazards was 1). Based on these results, construction companies will be able to plan worker allocations that prevent struck-by accidents by increasing multiple hazard recognition abilities in dark environments on construction sites.

Program management presents unique challenges due to the complexity of interrelated projects and increased stakeholder engagement. While existing literature mainly focuses on project-level risk management, inter-project risks remain underexplored. This research addresses this gap by proposing a program risk analysis method that integrates project interdependence and stakeholder engagement. Leveraging social network analysis, the model enhances program risk management efficiency by identifying four types of inter-project risks and suggesting tailored response strategies. Through a case study of the Expo 2020 construction program, the effectiveness of the framework is demonstrated. This study enriches program and risk management literature, deepening our understanding of enhanced risk management in multi-project contexts.

BIM (Building Information Modeling) is touted for efficient building maintenance and operation. However, transitioning from construction to maintenance poses challenges in information transfer and definitive data before completion. Existing structures often lack BIM, demanding more modeling. Additionally, few maintenance staff are skilled in BIM tools.On the other hand, there are studies utilizing point clouds for maintenance. Since point cloud data can record the current situation in 3D, it has advantages such as easily representing valve positions of equipment compared to deformed BIM data.Attribute information uses the international standard COBie, which can record and manage data necessary for building asset management.Point cloud data is broken down into groups of objects necessary for maintenance management by referencing the Common Specification for Building Preservation. Each decomposed object is assigned a corresponding Uniclass number.In this system, the point cloud data, which represents the shape information of the building, is decomposed into objects based on the Common Specification. Using COBie, the building database is created and tasks related to the objects are organized. Each database and system is then connected using Uniclass.By implementing this system, even buildings completed can easily create BIM data from point clouds. Furthermore, since it complies with the international standard COBie, maintenance tasks can be performed in a standardized format, serving as a bridge to the maintenance management system.

While Building Information Modeling (BIM) is an important tool for digitization in the architecture industry, its introduction rate in the field of facility maintenance and management is still low. Accordingly, this study aims to spread BIM to this field. The introduction of BIM enables centralization of facility data that has been managed separately in two-dimensional data and allows analysis across data in three-dimensional space. This study includes three phases. Phase 1 is to create a BIM model of the head office building in Japan as an example, phase 2 is to link the BIM model with the building data, and phase 3 is to create an analysis environment based on the data-linked BIM created. The BIM model is linked to three sheets of data using Dynamo; data showing the seating ratio of the seats in the free-address office owned by the facility, the amount of electricity used, and the repair work history of the building. Finally, an analysis environment is created for using the BIM model with data linkage in actual facility maintenance and management operations. As the platform created in this study now makes it possible to analyze multiple sets of data in a three-dimensional environment, it is expected to provide multifaceted solutions through analysis across multiple datasets.

Universities own several campuses and many buildings within the campuses. During the operation & maintenance phase, some of the buildings have architectural components and MEPFs that must be maintained. In the authors' study, university campus Facility Manager uses paper drawings and paper documents for operations and maintenance of MEPFs, and building components, which are managed by human power. In this study, As-Built 3D model of school buildings, was developed by 3D scanning with MatterPort Pro2 camera. A digital twin of the school building was developed from integrating the As-Built 3D model with a COBie Sheet information that defines the building and facility components for FMr. This developed digital twin was used to verify the efficiency of conventional Facility Management (FM) operations. The specific procedures are as follows. (1) Conducted an interview survey on FM of conventional university campuses to organize the current operations. (2) The following building items, which are annual inspection items, were extracted from the conventional FM operations being performed, Speakers, fire alarms, fire doors, guide lights, air conditioning, and fire extinguishing equipment. (3) Since these items listed above are currently documented in different formats, the authors organized them into a database using COBie format. (4) The components of the organized COBie format and the As-Built 3Dmodel were integrated to complete the digital twin. (5) To verify the effectiveness of the digital twin, experiments were conducted on information search in current FM operations and workflows using the digital twin. (6) We also verified the effectiveness of the AS-Built 3D model by comparing between the As-Built 3D model and the BIM model. Finally, we discussed how process innovation through digitalization of FM operations contributes not only to the improvement of daily operations, but also to the productivity improvement of university management.

Smart factories represent the Fourth Industrial Revolution and related emerging technologies across all industries. Among those emerging technologies, digital twin (DTw) enables smart manufacturing, resembling the factory's physical components and functional services. Nevertheless, from the owner-operator perspective, there has been only a paucity of studies defining the functional services of 'facility' and 'equipment' for the smart factory. This fact has also encouraged the construction industry to expand its role into factory operations by supporting digitalized production equipment and facilities. In order to address this issue, this study uses the proposed list of three-level facility asset management (FAM) breakdown structure by Jin et al. [12] that is under review for possible publication. The FAM breakdown structure is then validated with function lists from previous studies and existing ISO standards. It selectively covers the areas of traditional construction management (CM), asset management (AM), facility management (FM), and production operation management (OM). A mapping table with practical information systems used in the four areas (CM, AM, FM, OM) is also analyzed to verify the proposed FAM breakdown structure. It is expected that the result of this study can be used as a standard function to develop a smart factory FAM digital twin for researchers and practitioners.

In this research, we present the implementation of a mixed-input neural network for daylight prediction in the architectural design process. This approach harnesses the advantages of both image and numerical inputs to construct a robust neural network model. The hybrid model consists of two branches, each handling in-depth information about the building. Consequently, this model can effectively accommodate a wide range of building layouts, incorporating additional information for enhanced predictions. The building data was created utilizing PlanFinder in Rhino Grasshopper, while simulation data were generated using Honeybee and Ladybug. Weather data were collected from three distinct localities in Vietnam: Ha Noi, Da Nang, and Ho Chi Minh City. The neural network demonstrates outstanding performance, achieving an R-squared (R2) value of 0.95 and the overall percentage difference in the testing dataset ranges from 0 to 20.7%.

As buildings become increasingly diverse in size and type, a continuous process of generating and processing various information within these structures occurs. This process is being addressed with the advancement of high-speed information and communication technology, which is being extensively applied in buildings. Such changes are institutionalized through the certification of high-speed information and communication buildings. This certification is crucial for ensuring the quality and connectivity of the essential communication infrastructure in modern buildings. Building Information Modeling (BIM) has established itself as an effective tool for the integrated management of design, construction, and operation in architectural projects. This research aims to refine and categorize the evaluation criteria suitable for the building certification based on high-speed information and communication criteria and to explore the applicability of BIM tools based on these criteria. By enhancing the substitution and connection of each library, the functionalities of BIM programs for a comprehensive review of information and communication facilities are expected to be applied in a more utilitarian direction.

With the modern workplace's increasing dependence on computer-based tasks, traditional lighting standards have been identified as insufficient for optimal occupant comfort and productivity. Therefore, this paper presents a comprehensive system framework designed to manage visual fatigue and cognitive performance within office environments. Classification and regression models using gradient boosting machine and random forest to predict visual fatigue and cognitive performance were developed based on data collected from 16 subjects in experiments. To this end, the proposed system consists of two modules: the first module predicts visual fatigue and cognitive performance levels using classification models, offering immediate feedback to occupants. The second module, targeted at facility managers, uses regression models and a genetic algorithm to identify optimal lighting settings, aiming to minimize visual fatigue and enhance cognitive performance. This system can help to manage visual fatigue and cognitive performance simultaneously, contributing to improvement of eye health and productivity.

To establish effective policies for reducing CO₂ emissions in the building sector, it is essential to analyze the feasibility of regulations. In this regard this study seeks to analyze the effectiveness of Energy Performance Certificate (EPC), Display Energy Certificate (DEC), and Minimum Energy Efficiency Standard (MEES) regulations implemented in the UK. In particular, it analyzes the effectiveness in terms of mandatory issuance of the building energy rating certificates (i.e., EPC and DEC) and the minimum energy performance regulations (i.e., MEES regulation) of the building. To this end, the effectiveness of the system implemented in the UK was analyzed using 31,915 EPC and 31,715 DEC data. The analysis found that the CO₂ emissions of properties due to the EPC and DEP issuance obligations decreased further in 2022 than in 2013. It was also found that starting in 2018 when the MEES regulation was implemented, the CO₂ emissions by property type continued to decrease. This is because property owners were highly motivated to improve building energy performance due to the implementation of the MEES regulation. Therefore, it is expected that the findings of this study will serve as important basic data for the policymakers of the government to develop more effective building energy performance improvement policies.

An economizer control is used for cooling a building by modulating outdoor air(OA) intake rate according to measured OA conditions. An OA enthalpy sensor can be faulty during the operating after installation. The sensor mainly is fault in the form of offset. It leads value differences between measured enthalpy and actual enthalpy. The enthalpy differences occurred by the faulty sensor may result in more OA intake or less OA intake than designed OA intake value. The unwanted amount of OA intake negatively affects cooling system performance, especially cooling energy consumption. Therefore, this study analyzed cooling system performance resulted from occurring the faulty sensor in economizer enthalpy control. To conduct the analysis, this study utilized the Fault model in EnergyPlus, a building energy simulation tool. As a result of the analysis, the faulty sensor with positive offset intaked less OA amount than the available OA amount. It lead more cooling energy consumed by cooling equipment such as chiller and circulation pump. On the other hand, the faulty sensor with negative offset intaked more unnecessary OA amount than the required OA amount. It also lead more cooling energy consumption in the cooling equipment. Based on the resultant analysis, this study suggests continuous maintenance and diagnosis for an enthalpy sensor used in the economizer system. It may allow proper operation control for the economizer system, and thus the maximum cooling energy saving can be achieved.

A data center stores and manages internet data. The data center is mainly comprised of IT equipment, cooling systems, and other components. IT equipment is used for storing and processing internet data, generating heat during use. If the heat generated by IT equipment is not removed, it can cause malfunctions, and cooling systems are used to remove this heat. Cooling systems account for more than 40% of the total energy consumption and reducing cooling energy can reduce the overall energy consumption of the data center. Therefore, analyzing the cooling energy consumption according to heat generation changes caused by IT equipment in the data center is necessary. This study analyzed the impact of heat generation changes in IT equipment on cooling energy consumption. Additionally, three different economizer control methods were applied to select the optimal economizer control method. To achieve this, a data center model with economizer systems applied was developed using data measured from IT equipment and cooling systems. As a result, as the operation rates of IT equipment increased from minimum to maximum, the annual energy consumption for each case increased by approximately 11.7%. The economizer analysis showed that the energy savings were greatest when dry bulb temperature control was applied, but it did not meet the operation environment of the IT equipment. Therefore, it was determined that economizer control to meet the operation environment of IT equipment is required to be enthalpy-based.

In the construction industry, there is a growing demand for improving productivity, and development of autonomous operation systems for construction machinery is progressing. Autonomous operation of construction machinery requires positioning information because construction must be carried out at planned locations. In this paper, we focused on Visual Simultaneous Localization and Mapping (Visual SLAM) as a method for obtaining location information for construction machinery and proposed an automated operation system using Visual SLAM. For automated driving, the indirect method based on ORB features is used in Visual SLAM, and processes such as mask processing for surrounding moving objects and measurement of initial positions using markers are performed. With the proposed system, it was confirmed that it is possible to perform automated operation in an experimental environment using the location information output by Visual SLAM. In addition, the experiment was conducted to verify the measurement accuracy when using Visual SLAM during construction work at actual construction sites. As a result, the measurement accuracy was less than 500 mm, which is a usable accuracy for actual construction. By using this system, it is possible to obtain the location information of construction machinery even in environments where GNSS cannot be used, and productivity at construction sites can be improved by performing automated operation.

Much labour is required in the labour-intensive construction sector for workers to do physically taxing jobs like plastering, paving, surfacing, material lifting, hauling, scaffolding, etc. Passive exoskeletons have been advocated and examined in previous studies to reduce physical demands, improve work efficiency, and prevent work-related musculoskeletal disorders in construction workers. This review study examined previous studies performed on passive exoskeletons in construction or other occupational domains with the aim of finding the working principles and design requirements of the passive exoskeletons, their applicability and usability in occupational settings, and the potential challenges in their adoption, thereby finding future research directions that can overcome those challenges. Three working principles were identified: muscle assistance, joint load reduction, and maintaining proper posture. The design requirements to achieve one or more of these working principles may have a few undesired effects on the usability of the passive exoskeletons, like discomfort, unnecessary weight of the passive exoskeleton, difficulty in operation, restricted range of motion of the joints supported, and the unaffordability of these exoskeletons by the workers. Passive exoskeletons were reported to have a range of positive effects, like reducing muscle effort and improving the endurance of the workers. The study concluded that there needs to be sufficient research on real construction workers in a real construction environment to convince the workers and managers to accept passive exoskeletons. To improve the usability of passive exoskeletons, fundamental changes in design are needed through further research so that the exoskeleton can support workers in multitasking rather than a single function. They also need to become more affordable, and the other undesired negative effects of passive exoskeletons should also be addressed.

South Korea has seen an increased demand for road maintenance, since they experienced a rapid industrialization in 1960-70s. Between 2019 and the end of 2022, the total national expenditure on road maintenance steadily rose from KRW 3.4 trillion to KRW 4.5 trillion. Roads, responsible for about 80% of the nation's transportation, significantly affect ride quality, safety and maintenance costs. Among the different perspectives, this study focuses on the prevalence of potholes. Over 24,000 pothole instances are reported on highways in the past five years, which raises concerns due to various direct and indirect effects on road maintenance and safety issues. Various methods, including vision-based, vibration-based, and 3D reconstruction-based techniques, have been proposed for pothole detection and inspection. Vision-based methods effectively count and measure pothole shapes but which are sensitive to lighting conditions. Vibration-based methods offer cost-effectiveness, although it may not provide precise pothole shape information. 3D reconstruction-based methods deliver accurate shape measurements, while it comes with higher costs. To establish an effective road maintenance system, prioritization criteria for potholes is required to be established and applied. These criteria may vary across countries or regions. For example, in the United States, potholes are classified based on depth into Low (<25mm deep), Moderate (25 to 50mm deep), and High (>50mm deep). In conclusion, this research addresses this research challenge of road damage and potholes in South Korea by exploring various pothole classification standards and utilizing advanced technology to develop an efficient road maintenance system. The outcome would benefit improved road infrastructure management and enhanced safety.

In this study, we aim to enhance the navigation method for autonomous systems, including various types of robots and UAVs. Previous work has made progress in enhancing the autonomous flight capabilities of UAVs through both simulation and real-world tests, yet it lacked detailed insights into the aspect of global route planning. To address this gap, we propose a novel method to automatically generate routes for autonomous robots using BIM and 3D city models. Specifically, we create a program that utilizes geometry and attribute information extracted from BIM and 3D City Model to compute the optimal route rapidly. The program automatically generates routes that optimize the efficiency of various autonomous robots by considering the conditions on the routes.

Human-robot collaboration (HRC) is an emerging form of work anticipated to improve construction productivity by integrating robotic capabilities with human expertise. With the expected transition towards tasks that demand more cognitive efforts for human workers, considering the cognitive status of each co-worker, such as task engagement and vigilance, can become crucial to achieve high-quality human performance during HRC, potentially contributing to a more productive HRC in construction. However, the potential cognitive changes of each co-worker have remained unclear during HRC, as studies have primarily focused on identifying general trends from aggregated cognitive responses of people, in which an individual's response can be overlooked. In this study, we examine the cognitive response of each co-worker during HRC for a construction task. We observed the cognitive responses of 18 people while they were experiencing different collaborating conditions, such as the robot's different movement speed, during a bricklaying task with an arm-type collaborative robot. For each participant, we analyzed electroencephalogram (EEG) signals to identify the changes in cognitive status by using a wearable EEG headset. The results present that the cognitive responses of almost all the participants were significantly and differently affected during HRC, impacting the estimated productivity of their human-robot teams. The findings of the study present the importance of considering each co-worker's potentially unique cognitive response as a way to achieve cognitive wellbeing while pursuing high productivity within human-robot teams, potentially contributing to overall productive HRC in construction.

The construction industry has witnessed a concerning rise in ladder-related accidents, necessitating the implementation of stricter safety measures. Recent statistics highlight a substantial number of accidents occurring while using ladders, emphasizing the mandatory need for preventative measures. While prior research has explored computer vision-based automatic monitoring for specific aspects such as ladder stability with and without outriggers, worker height, and helmet usage, this study extends existing frameworks by introducing a rule set for co-workers. The research methodology involves training a YOLOv5 model on a comprehensive dataset to detect both the worker on the ladder and the presence of co-workers in real time. The aim is to enable smooth integration of the detector into a mobile application, serving as a portable real-time monitoring tool for safety managers. This mobile application functions as a general safety tool, considering not only conventional risk factors but also ensuring the presence of a co-worker when a worker reaches a specific height. The application offers users an intuitive interface, utilizing the device's camera to identify and verify the presence of coworkers during ladder activities. By combining computer vision technology with mobile applications, this study presents an innovative approach to ladder safety that prioritizes real-time, on-site co-worker verification, thereby significantly reducing the risk of accidents in construction environments. With an overall mean average precision (mAP) of 97.5 percent, the trained model demonstrates its effectiveness in detecting unsafe worker behavior within a construction environment.

Previous approaches to 3D excavator pose estimation via synthetic data training utilized a single virtual excavator model, low polygon objects, relatively poor textures, and few background objects, which led to reduced accuracy when the resulting models were tested on differing excavator types and more complex backgrounds. To address these limitations, the authors present a realism-centric synthetization and labeling approach that synthesizes results with improved image quality, more detailed excavator models, additional excavator types, and complex background conditions. Additionally, the data generated includes dense pose labels and depth maps for the excavator models. Utilizing the realism-centric generation method, the authors achieved significantly greater image detail, excavator variety, and background complexity for potentially improved labeling accuracy. The dense pose labels, featuring fifty points instead of the conventional four to six, could allow inferences to be made from unclear excavator pose estimates. The synthesized depth maps could be utilized in a variety of DNN applications, including multi-modal data integration and object detection. Our next step involves training and testing DNN models that would quantify the degree of accuracy enhancement achieved by increased image quality, excavator diversity, and background complexity, helping lay the groundwork for broader application of synthetic models in construction robotics and automated project management.

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

The construction of 3D Printed Concrete (3DPC) structures, particularly in reinforced concrete, still poses challenges due to constraints in construction methods. Additionally, the unique mixture design of 3DPC typically results in noticeable drying shrinkage. Utilizing short fibers for fiber reinforcement is a reliable approach that may replace reinforcing steel and address the challenge of volume stability. In this study, polypropylene (PP) fibers and polyoxymethylene (POM) fibers were incorporated into the total volume of concrete at additional percentages of 0.5%, 1.0%, and 1.5% to printed the specimen. While ensuring printability, various experiment were conducted to evaluate compressive strength, flexural strength, anisotropy, and drying shrinkage,to ensure the impact of fiber type and content on the mechanical properties and drying shrinkage of 3DPC. The results indicate that 3DPC exhibits significant strength loss after fiber addition, with loss percentages approximately ranging from 5% to 55% for compressive strength and 9% to 57% for flexural strength. The extent of loss improves with increasing PP fiber content, while the strength of POM fibers continues to decline with increased usage. Furthermore, significant anisotropy is observed in 3DPC after fiber addition, with compressive strength relations appearing as X > Y ≈ Z in various directions, while flexural strength relations are demonstrated as Y ≈ Z > X. Concerning drying shrinkage, the addition of 1.0% POM fibers proves most effective in inhibiting drying shrinkage, reducing shrinkage by approximately 6% at the age of 56 days. In contrast, the presence of PP fibers, regardless of quantity, adversely affects drying shrinkage.

To enhance the investigation, analysis, and design of safe human-robot interactions (HRI), this study develops a comprehensive taxonomy of safety-related errors in HRI and examines the relationships between errors and the types and levels of HRI. Analyzing 262 HRI accident case reports, the research identifies and categorizes human and robot errors through qualitative analysis. The resulting taxonomy divides human errors into procedure, intrusion, operation, and situation awareness errors, and robot errors into system and safeguarding failures, operational errors, and design flaws. A network of human and robot errors was developed by applying Gephi to represent the human-robot error interactions. The results indicated that "misjudgment of the robot's operational status," "inadvertent activation of the robot," "working within an energized robotic cell without adequate safety measures," and "failure to deenergize/stop the robot" are among those most frequently linked to robot errors. "Inadequate lockout/tagout" and "absence of human detection and protective stop functions" stand out as the most frequent human-robot error interaction.

Construction hoists are essential equipment for vertical lifting of workers and materials on construction sites, and their efficient operation significantly impacts the success of construction projects. To optimize hoist operation, it is crucial to accurately understand the call situation on each floor (i.e., the external waiting state) and the internal state of the hoist. This study aims to use object detection technology to monitor the status of workers and materials waiting on each floor, as well as the boarding state inside the hoist in real-time. Subsequently, by utilizing the real-time gathered information, a model was developed to reduce the number of stops, thereby demonstrating the potential of object detection technology in reducing the hoist's transportation time. The research results show that it is possible to determine the number of workers, the types of materials, and the quantity of materials to board the hoist using object detection, and to derive an optimized route. Consequently, it demonstrates that the use of object detection can reduce the transportation time of the hoist, thereby improving its operational efficiency.

In recent years, the development of digital design methods and structural analysis has made it possible to design complex building shapes. However, the construction of such buildings requires advanced knowledge of production, interaction with actual craftsmen, and verification of construction methods through mock-ups. Despite this, however, design education in Japanese universities is often limited to proposals at the predesign and schematic design stages, and students have few opportunities to study the design development and construction stages. However, in recent years, computer numerical control (CNC) machine tools have become relatively inexpensive and can be handled easily, even by beginners. Therefore, practical training programs intended to familiarize students with manufacturing techniques using such machines are becoming increasingly popular at Japanese universities. This paper reports on the content of a practical training program intended to provide students with practical experience from production to design in the field of manufacturing by using CNC machine tools and other digital fabrication equipment to create a mock-up of a complex curved surface for a building and simulate the process of construction production in the design development and construction management. It is difficult to create curved surfaces using only digital fabrication, and manual processes, such as bending wooden boards along curved surfaces, are required. Therefore, students were required to combine digital fabrication knowledge with hands-on manufacturing skills, including hand-bending and experiential learning, which helped students think about the necessary processes for constructing the shapes they designed.

Addis Ababa, the capital of Ethiopia, has been lacking an adequate road network to ensure community access to social, political, and economic resources and to facilitate economic development. The road network coverage was about 22.5% in 2022, which is below the minimum international standard of 25%. To improve accessibility and mobility, the Addis Ababa City Roads Authority(AACRA) has engaged its own force crew (contractor), as well as local and foreign road contractors, in the construction of urban roads. However, these road construction projects are rarely completed within the estimated time and cost, along with a variety of other issues that also need to be addressed. This study aims to explore the application of lean construction for improving the performance of urban road construction projects in Addis Ababa City. A survey of road construction stakeholders was carried out to evaluate their perspectives on the importance of project performance indicators, with the goal of identifying key factors affecting road construction project performance. First, a list of performance indicators was prepared based on a review of lean construction literature, and a total of 38 identified factors were grouped into six performance indicator categories. Cross-tabulation analysis of the stakeholder perspectives was then carried out, and it was found that the indicators that affect the performance of urban road infrastructure projects (in descending order of importance) were: time, quality, cost, risk, safety, and sustainability. Through this analysis it was concluded that right-of-way issues, delay to finish, inflation, contractor capacity, and scope change with change order are also major factors that affect the performance of urban roads construction projects. Clarification of these factors will provide AACRA with useful information on what aspects of lean construction should be prioritized when evaluating future construction projects.

Prominent general contractors (GCs) in Japan have historically maintained dedicated research and development (R&D) institutes that conduct comprehensive studies on structural engineering, construction techniques, and environmental management technologies. These research endeavors have evolved over time, reflecting the prevailing conditions and trends in the construction industry during each era. We examined changes in R&D activities over the past decade by analyzing R&D descriptions and statistical data contained in securities reports issued by 14 leading GCs using natural language processing. Our analysis revealed that over the course of the decade, R&D activities transformed significantly due to market dynamics and macro-environmental factors. For instance, during the 2000s, a surge in demand for high-rise condominium buildings led to an increased presence of related terminology in the 2009 fiscal year (FY) securities reports. However, this trend had declined by FY 2019. Notably, in FY 2019, there was an observable increase in R&D efforts concerning wood and cross-laminated timber applications. This can be attributed to the enforcement of laws and standardization measures that facilitated the proliferation of wood-based construction techniques in the 2010s. Throughout the 2010s, the primary concern of the Japanese construction industry was optimizing production processes to meet escalating domestic construction demands. A comparative analysis between 2009 and 2019 indicates a shift in focus, with fewer references to product innovation and a more pronounced emphasis on process innovation.

Official Development Assistance (ODA) is widely known as an essential source for socioeconomics of development of developing countries, especially in the infrastructure sector. Since 2023, the use of BIM is regarded as a mandatory task for public-funded projects including ODA projects in Vietnam. However, ODA projects require a coordination in project management and implementation between the lenders and the borrowers which are different in BIM standards and/or guidelines. This obviously causes barriers for the implementation of ODA projects. This study aims to deal with the problem by identifying key barriers for BIM implementation in Japan ODA projects in Vietnam. To serve the purpose, data from non-BIM ODA projects is firstly analysed to identify problems that can be handled by BIM. Next, problems obtained from BIM-applied projects were considered to identify needed aspects for BIM application in ODA projects in Vietnam. Finally, the incompatibility between Vietnam's BIM guidelines and related regulations is investigated to identify barriers to BIM implementation in ODA projects in Vietnam. Findings showed that the key barriers include problems related to BIM content, stages of applying BIM to project, BIM cost, BIM guidance documents, and the capability of executing agencies.

Reducing the environmental impact in the construction industry is essential for a sustainable future, and life cycle assessment (LCA) should be effectively conducted to reduce the environmental impact. The construction industry is one of the fields that emits a large amount of Greenhouse Gas (GHG). It is also characterized by many material inputs and a one-off single production. Therefore, it took a lot of effort to evaluate all the input materials, and it was difficult to implement a detailed LCA. There is need to solve these problems and to establish a fair and reliable evaluation method. In order to solve this problem, it is proposed to establish a common rule for calculating environmental loads of buildings, such as carbon dioxide emissions. In addition, by effectively utilizing the Inventory Database for Environmental Analysis (IDEA) database, which is an inventory database developed in Japan. It can evaluate not only carbon dioxide but also various environmental substances, and analyze how the environmental impact is correlated with each building and its constituent materials. Furthermore, by analyzing the actual buildings of 83 projects, the differences in the tendency of building type and materiall was clarified. A database was constructed to help reduce the environmental impact during the early stages of construction project and for different types of buildings.

Digital transformation has become a pivotal focus in the Architecture, Engineering, and Construction (AEC) industry, driven by an urgent need to enhance productivity and optimize resource management. This transformation plays an essential role throughout the entire project lifecycle, from the early stages of conception to the final phases of completion. The paper underscores the critical importance of aligning digital transformation initiatives with the broader business strategies of AEC organizations. This alignment is key to gaining a competitive edge and fostering sustainable growth within the industry. The paper introduces a comprehensive and adaptable strategic framework for digital transformation. This framework is designed to be flexible, allowing AEC organizations to tailor digital transformation strategies to meet their specific needs and objectives. The framework not only addresses the technological aspects but also considers the cultural and operational shifts required for successful implementation. Moreover, the paper delves into various aspects of digital transformation, such as data management, workflow automation, and the integration of emerging technologies like AI and IoT in AEC processes. It discusses the potential barriers to digital adoption and offers strategies to overcome these challenges. This paper serves as an in-depth guide for AEC organizations looking to seamlessly integrate digital technologies into their business models. It provides valuable insights and methodologies that are crucial for any entity in the AEC industry striving to thrive in an increasingly digitalized world, making it a must-read for leaders and decision-makers within the industry.

Koto, a brand specializing in fittings, commercializes fittings and doorknobs that were devised through residential design. The purpose of this experiment is to create different distances and textures in the space. This report describes KOTO's methods for commercializing fittings and doorknobs.

Achieving sustainable futures requires the construction industry to employ digitalization processes, appropriate procurement methods and innovative technologies. However, sustainable technologies in the built environment are often ignored and under-used by clients and users of buildings and facilities, meaning the benefits of sustainable technologies can be missed. This paper provides reflections of one such technology as experienced by the author: a digital toilet installed in a hotel. Through an autoethnographic approach, the paper mobilises socio-technical systems thinking to examine and analyze the digital technology in-situ to identify factors leading to a negative experience from the authors' perspective. The socio-technical systems analysis identifies several issues to explain its' non-functioning status: these revolving around people; goals; culture; technology; processes/procedures and buildings/infrastructure. The analysis provides a framework for both retrospective review and upgrading of existing technologies and an assistive tool to assist in the planning, design and installation of new sustainable technologies in the future. A discussion explores the issues further in respect to construction project industries and their role in addressing the issues identified. It is contended that achieving sustainable futures requires both predictive analysis of new technologies in the built environment and retrospective review and adaptation of technologies already in our built environment.

For reusable Temporary Structure Elements such as scaffolding and temporary supports, quality control tasks are currently carried out through visual inspections by quality management workers and subjective judgments. Regarding quality tests based on the KOSHA(Korea Occupational Safety & Health Agency) system, only three pieces are sampled regardless of the quantity received at the site. On the other hand, although there is ongoing technological research on an automatic quality inspection of temporary structure elements, relevant stakeholders' introduction of such systems is hindered by issues such as cost. Therefore, this study aims to review a business model for introducing a quantitative and automated quality inspection system for reusable temporary structure elements. The study intends to propose application methods for each component according to a template and establish the business model by conducting interviews and collecting basic data for each template component. The results of this research are expected to serve as a foundation for implementation and expanding the adoption of quality management for temporary structure elements using smart technologies in the future.

The construction sector, in relation to human's residential issues, serves as the cornerstone of societal systems, economic security, the national economy, individuals and households, economic activity, national safety, management, business, and government. Also, the essential sectors, such as electric power generation, transmission, and distribution, and natural gas distribution, water, sewage, and stormwater systems, and so on, form the heart of our social system. These essential sectors of our society provide core goods and services for continuous public health services, construction services, government operations, multiple businesses, and national and economic safety. Therefore, those systems are the cornerstone of our society, and strengthening their security and resilience is of utmost significance. However, the existing framework for assessing and evaluating the problems with regard to those systems lacks efficient methods and mechanisms. The main objective of this research is to define correlations between different infrastructures within an economic system to strengthen the resilience and security of the construction sector. This study will be conducted to identify physical relationships between different industries within an economic system and to define deterministic relationships through the values of interconnectedness and interdependency. In addition, this research attempts to complete a probabilistic estimation of economic impacts using historical economic data and to develop an assessment model that can be used in the future to measure economic impact in terms of the construction sector. In case of loss in the construction sector due to exogenous factors, identifying which critical infrastructures or sectors will be the most affected will help minimize the risks and property damages. Furthermore, improving the resiliency of the construction sector will help speed recovery from or resistance to unpredictable external elements.

High-energy manufacturing processes, including laser welding, are actively being adopted not only in precision machinery industries but also in the shipbuilding and construction sectors. Laser welding, in particular, is gaining prominence in the industry due to its faster welding speed and reduced distortion compared to conventional arc welding methods. Integration of automated welding systems is anticipated to address challenges in shipbuilding and construction industries, which are currently facing a shortage of skilled labor. For successful implementation of automated welding systems, it is essential to predict and design for the post-welding effects, such as residual deformation and stresses. However, in the case of high-energy manufacturing like laser welding, the welding bead morphology differs from that of arc welding, and the heat load conditions applied during simulation are distinct. To facilitate accurate simulation predictions, the development of a suitable heat source for predicting welding bead morphology in high-energy manufacturing processes is crucial. The Block-dumping method is proposed for rapid simulation and on-site application, with the shape of the welding bead being imperative for its effectiveness. In this study, data on the welding bead morphology of Nickel-based steel was obtained. Using Deep Learning techniques, we successfully predicted the bead morphology and confirmed minimal discrepancies when compared to actual results. This outcome allows for the simulation of welding under untested conditions, offering practical applicability in the field. Additionally, we present a heat source model (heat load condition) to ensure a highly accurate interpretation of the results.

Recently, the demand for building demolition in Korea has been increasing due to the rising number of old buildings and diversification of the types of buildings subject to construction demolition, and the related market size has been continuously growing. On the other hand, the laws, systems, and safety management related to building demolition are not implemented vigorously enough, so safety accidents frequently occur during the demolition process. In this study, we introduce the case of the collapse of a demolition building in Gwangju Metropolitan City in 2021, and we analyze the cause of the safety accident with the survey report published by the Korean government. Also, this study consists of institutional aspects of the demolition construction process in Korea and practical aspects such as sub-contracts, which are two main problems. Although Korean construction-related laws prohibit re-subcontracting in principle, illegal re-subcontracting is prevalent in building demolition and the supervision of building demolition is poor. Also, the dismantling plan does not function effectively as a practical checklist at the construction site due to complicated procedures and many requirements. In conclusion, for building demolition to be carried out more safely and efficiently, it is needed to reasonably improve related practices and systems in Korea.

This study focuses on a scheme for accepting migrant workers at Japanese construction sites and its impact on the industry. Recently, a severe shortage in the construction workforce has made the industry accept migrant workers through "Technical Intern Training (TIT)" program and "Specified Skilled Worker (SSW)" system in Japan. A new status of residence, SSW has been created to accept foreign nationals who work in jobs that require considerable knowledge of or experience in specified industry fields, and construction is one of them. However, many SSWs had already been working in Japan as TIT trainees, implying that the TIT program was the practical pathway to becoming a SSW. Therefore, this study aims to comprehensively understand the operational realities of the Japanese TIT program in construction companies. Data were collected using a literature survey and semi-structured interviews. The literature survey was a macro perspective, mainly about analyzing the statistical data on the Japanese government, with the status of residence, profession, and nationality, to ensure the transition and full picture of migrant workers in Japan. By contrast, interview surveys focused on micro situations such as the challenges faced by companies that accept migrant workers. This study showed that workforce shortages had lasted for more than 30 years and were still a chronic issue and that migrant workers were indispensable in the construction industry.

Traditional safety training methods in construction, such as toolbox meetings and classroom sessions, fall short of addressing the challenges faced by workers, especially migrant workers hindered by barriers. The compliance-driven nature of safety management practices is identified as a core issue, often disconnected from the realities of the jobsite. To tackle these limitations, we propose iSAFE-Education, an approach harnessing Industry 4.0 technologies. By integrating Virtual Reality (VR) and Metaverse environments into safety training, this platform immerses workers in authentic jobsite contexts using 360-degree panorama images. Our method provides virtual walkthroughs, enabling workers to familiarize themselves with site-specific features and safety protocols. Additionally, iSAFE-Education facilitates site assessment and safety information sharing among project participants within the immersive jobsite environment. This paper highlights the importance of hazard information delivery and positions this solution as an answer for enhancing jobsite safety in contemporary construction settings.

In the construction industry, professionals are required to have advanced problem-solving skills to adeptly handle the dynamic challenges inherent to project execution. These skills are crucial, as they enable professionals to effectively navigate the complexities and unpredictability of construction projects, ensuring timely and cost-effective completion. This paper explores an innovative approach to enhance the problem-solving skills of construction students through the integration of conversational AI-based serious games into their educational curriculum. The objective of this research was acquired by following three phases: hazard interaction, problem identification, and AI-guided text-based communication. This approach creates an engaging learning environment, simulating real-world construction challenges and problems, focusing on the excavation phase of a construction project as a case study for students to interact with and communicate with the Conversational AI agent through text-based prompts. In the future, the proposed study can be used to evaluate how AI agents can help enhance problem-solving skills by promoting emotional engagement among participants. This research sheds light on the potential of integrating conversational AI in education, providing valuable insights for educators designing construction management training programs by underscoring the importance of engagement in real-world problem-solving scenarios.

Personalized learning is a critical factor in optimizing performance on construction sites. Traditional pedagogical methods often adhere to a one-size-fits-all approach, failing to provide the nuanced adaptation required to cater to diverse knowledge needs, roles, and learning preferences. While advancements in technology have led to improvements in personalized learning within construction education, the crucial connection between instructors' roles and training enviornment to personalized learning success remains largely unexplored. To address these gaps, this research proposes a novel learning approach utilizing multi-agent, context-specific AI agents within construction virtual environments. This study aims to pioneer an innovative approach leveraging the Large Language Model's capabilities with prompt engineering to make domain-specific conversations. Through the integration of AI-driven conversations in a realistic 3D environment, users will interact with domain-specific agents, receiving personalized safety guidance and information. The system's performance is assessed using the five evaluation criteria including learnability, interaction, communication, relevancy and visualization. The results revealed that the proposed approach has the potential to significantly enhance safety learning in the construction industry, which may lead to improve practices and reduction in accidents on diverse construction sites.

The construction industry faces the challenge of providing effective, engaging, and rule-specific safety learning. Traditional methodologies exhibit limited adaptability to technological advancement and struggle to deliver optimal learning experiences. Recently, there has been widespread adoption of information retrieval and ontology-based chatbots, as well as content delivery methods, for safety learning and education. However, existing information and content retrieval methods often struggle with accessing and presenting relevant safety learning materials efficiently. Additionally, the rigid and complex structures of ontology-based approaches pose obstacles in accommodating dynamic content and scaling for large datasets. They require more computational resources for ontology management. To address these limitations, this paper introduces iSafe Chatbot, a novel framework for construction safety learning. Leveraging Natural Language Processing (NLP) and Large Language Model (LLM), iSafe Chatbot aids safety learning by dynamically retrieving and interpreting relevant Occupational Safety and Health Administration (OSHA) rules from the comprehensive safety regulation database. When a user submits a query, iSafe Chatbot identifies relevant regulations and employs LLM techniques to provide clear explanations with practical examples. Furthermore, based on the user's query and context, iSafe Chatbot recommends training video content from video database, enhancing comprehension and engagement. Through advanced NLP, LLM, and video content delivery, iSafe Chatbot promises to revolutionize safety learning in construction, providing an effective, engaging, and rule-specific experience. Preliminary tests have demonstrated the potential of the iSafe Chatbot. This framework addresses challenges in accessing safety materials and aims to enhance knowledge and adherence to safety protocols within the industry.

This study examines the transitions of expertise, the challenges of changing professional occupation and the process of adaptation after the occupational change from architectural designers/engineers (A/E) to CMRs (Construction Managers) in Japan. To this goal, a framework of expertise for CMR and Kenchikushi is obtained through a literature study. Then, case studies are conducted and the collected five cases are coded to examine the transition of expertise. Furthermore, the adaptation process after the change of professional occupation is modeled using the TEM (Trajectory Equifinality Model).

In the realm of Architecture, Engineering, and Construction (AEC) education, various factors play a crucial role in shaping students' acceptance of the learning environments facilitated by visualization technologies, such as virtual reality (VR). Works on leveraging the heterogeneous educational information (i.e., pedagogical data, student performance data, and student survey data) to identify essential factors influencing students' learning experience and performance in virtual environments are still insufficient. This research proposed KnowLearn, an interactive learning assistant system, to integrate an educational knowledge graph (KG) and a locally deployed large language model (LLM) to generate real-time personalized learning recommendations. As the knowledge base of KnowLearn, the educational KG accommodated multi-faceted educational information from twelve perspectives, such as the teaching content, students' academic performance, and their perceived confidence in a specific course from the AEC discipline. A heterogeneous graph attention network (HAN) was utilized to infer the latent information in the KG and, thus, identified the perceived confidence, intention to use, and performance in a relevant quiz as the top three indicators that significantly influenced students' learning outcomes. Based on the information preserved in the KG and learned from the HAN model, the LLM enhanced the personalization of recommendations concerning adopting virtual learning environments while protecting students' privacy. The proposed KnowLearn system is expected to feasibly provide enhanced recommendations on the teaching module design for educators from the AEC domain.

Tatami serves as a multi-functional flooring and furniture material in traditional Japanese houses, and Westerners use it with suitable arrangements. Some choose to sleep on tatami for health reasons, while others adopt the lifestyle of 'floor living' to solve the spatial problems in cities. In any case, people use tatami for practical reasons rather than due to a cultural preference. I surveyed 750 respondents, including 463 non-Japanese people, and conducted both quantitative and qualitative analysis. I also interviewed European vendors and users of tatami during 2022-2023. The results showed that non-Japanese customers, especially Europeans, wanted authentic tatami mats made of natural materials such as igusa and rice straw to ensure health and sustainability. On the other hand, Japan's tatami industry is shifting towards artificial, or alternative, tatami-like mats. Few authentic tatami rooms exist in Japanese homes nowadays, and producers of tatami materials are almost extinct in Japan. Chinese farmers who supply almost 80% of igusa to the Japanese market may also discontinue their trade because the production is time-consuming and yields low profit. This paper discusses the possibilities of continuously producing and selling tatami in and outside Japan.

Herein, the cardanol-based flame retardant containing epoxide group to form the chemical bond with hydroxyl group in wood substrate was synthesized. It was confirmed that this cardanol based derivative can replace bisphenol A diglycidyl ether monomer in the epoxy-amine crosslinking reaction, and flame retardancy and adhesion property were investigated for the application of building construction.

Construction is like a symbol of social development. Buildings and public infrastructures are required to support people's lives and a country's development. With the increasing construction, the waste of construction increases as well. Demolition wastes from existing buildings or urban renewal plans also increase the waste quantity. Construction and demolition wastes (CDW) are majorly inorganic materials and have high recycling potential. However, the recycling rate of CDW is not 100% and needs more methods to raise the rate. In Taiwan, the CDW has two authorities, one is CDW waste managed by the Ministry of Environment (MoE), and the other is construction spoil soils managed by the National Land Management Agency, Ministry of Interior. In 2022, the CDW waste is 2.12 million tons, and construction spoil soils are around 43 million m³. In this study, the current status of CDW in Taiwan was reviewed and material flow analysis was applied to link the materials sources to CDW. The results showed that 35% of waste concrete flowed back to the source materials and 65% was disposed of or used as secondary materials. However, 95% of waste steel was used back as raw materials and only 5% was disposed of or used in other materials. To increase the recycling of CDW, the priority method is to force the material separation onsite. Mixing CDW would increase the cost of recycling and decrease the market competition ability. New technology to collect and recycle the CDW as a new material is also recommended. Finally, a new law, the Resources Recycling Promotion Law, is coming in this year, 2024, which is expected to turn the waste into resources in Taiwan.

Private Finance Initiative (PFI) involves long-term contracts where private entities invest in the construction and maintenance of street lighting facilities. The current implementation of PFI Smart Street Lighting Systems faces challenges in the fulfillment process, including discrepancies in quantities, coordination of power variations, delays in the deployment of smart systems, and issues with performance indicator scoring. These challenges disrupt the smooth execution of contractual obligations. Nevertheless, the adoption of intelligent systems in street lighting presents significant advantages in reducing energy consumption, extending the lifespan of fixtures, and enhancing maintenance efficiency. This study aims to analyze an ongoing project, applying the Fuzzy Analytic Hierarchy Process (FAHP) to identify crucial PFI indicators and their weights. The study explores areas of improvement in the project compared to traditional street lighting, aiming to provide solutions to the mentioned challenges. The results indicate that indicators such as PS3 (Lighting Service Continuity) with a weight of 0.384% and PS4 (Smooth Operation of the Smart Street Lightings Management System) with a weight of 0.274% have the highest impact on service performance. Additionally, the project involves replacing 162,000 streetlights, resulting in a yearly energy consumption reduction of approximately 70%, a decrease in monthly maintenance time from an average of 48 hours to 15 hours, and an expected reduction of 900,000 tons in carbon emissions during the project period. Value for Money (VfM) analysis suggests an annual reduction in government expenditures of NTD 66 million. This reveals that implementing PFI model is more advantageous than traditional street lighting procurement, as it allows the government to leverage contractor financing and alleviate the initial high costs of streetlight replacement, thereby reducing the overall costs of streetlight establishment and maintenance.

Managing investments in renewable energy (RE) in developing countries is essential for reducing environmental pollution, meeting the growing energy demand, and avoiding the risk of stranded assets. Establishing Public-Private Partnerships (PPPs) is necessary to address budgetary and technical issues in developing countries. PPPs recover investments through long-term operations. Risks from external political, social, and economic environments during both the construction and operational phases of PPP projects affect the stability of investment recovery. Although various support systems are in place to mitigate investment risks for investors, these systems can pose risks to the public sector. Therefore, this study identifies common risks, including construction and operational risks, as well as political, financial, and social risks, for sustainable renewable energy PPP operations. Interpretive Structural Modeling (ISM) and MICMAC (Matrix Impact Cross-Reference Multiplication Applied to a Classification) analyses were conducted to understand the interrelationships among these risks. The ISM and MICMAC analysis results showed that construction phase risks have high dependence power and driving power. In contrast, operational phase risks exhibit low driving power but high dependence power. This indicates that managing construction phase risks is effective for the sustainable operation of renewable energy PPPs. Based on the analyzed ISM and MICMAC results, preventive measures for sustainable operations of renewable energy PPPs were proposed.

With the advancement of smart construction technologies, researches are being conducted on technologies to monitor construction site conditions in real-time. Particularly, with the advancement of communication technologies, it has become possible to transmit and receive information quickly and accurately even in environments with difficult communication such as construction sites. These communication technologies are utilized for real-time management of construction site information and safety management based on worker location etc. Therefore, in this study, a system is aimed to be constructed a system that can monitor concrete strength using the maturity method and transmit it wirelessly to a strength monitoring system, targeting Korean construction sites. As a result of the case application, the concrete maturity temperature was measured using sensor, and it was confirmed that the temperature data was properly transmitted to the concrete estimating system through Bluetooth Low Energy(BLE) transmission. It is anticipated that this study will contribute to the activation of smart construction technologies at construction sites, automation of safety and quality management, and improvement in construction accuracy.

About 70% of the data generated on building construction sites consists of unstructured data, such as text, photos, videos, etc. However, the text data, which constitutes the largest proportion of unstructured data, has been restrictively utilized. When using standardized data to evaluate safety performance, there are a few difficulties in addressing issues such as lack of data, omissions, and errors. This copes with limitations on the practical evaluation of safety performance on building construction sites. Despite generating extensive text-centric documents, the previous researches on evaluating safety performance levels using unstructured data are still in its infancy. This study proposes a framework for evaluating the safety performance by preprocessing and refining text-based construction supervision documents. In this framework, relevant keywords related to safety performance are extracted from supervision documents, tokenized, and analyzed for association rules among keywords. Based on the results of the association rule analysis, keywords are selected, and the unsatisfactory or satisfactory level of safety performance is quantified using logistic regression analysis, considering the frequency of their occurrence. While the proposed framework focuses on quantifying the safety performance levels of construction sites, it can be expanded to implement integrated performance diagnostics on-site by linking with tools that evaluate diverse performance levels. This extension will allow for a comprehensive assessment of on-site performance. Furthermore, the framework can serve as a tool supporting practical and proactive inspections and responses of safety managers by utilizing unstructured data alongside the traditional approach focused on standardized data for safety performance assessment.

Given the widespread use of intelligent surveillance cameras at construction sites, recent studies have introduced vision-based deep learning approaches. These studies have focused on enhancing the performance of vision-based excavator activity recognition to automatically monitor productivity metrics such as activity time and work cycle. However, acquiring a large amount of training data, i.e., videos captured from actual construction sites, is necessary for developing a vision-based excavator activity recognition model. Yet, complexities of dynamic working environments and security concerns at construction sites pose limitations on obtaining such videos from various surveillance camera locations. Consequently, this leads to performance degradation in excavator activity recognition models, reducing the accuracy and efficiency of heavy equipment productivity analysis. To address these limitations, this study aimed to conduct sensitivity analysis of excavator activity recognition performance based on surveillance camera location, utilizing synthetic videos generated from a game-engine-based virtual environment (Unreal Engine). Various scenarios for surveillance camera placement were devised, considering horizontal distance (20m, 30m, and 50m), vertical height (3m, 6m, and 10m), and horizontal angle (0° for front view, 90° for side view, and 180° for backside view). Performance analysis employed a 3D ResNet-18 model with transfer learning, yielding approximately 90.6% accuracy. Main findings revealed that horizontal distance significantly impacted model performance. Overall accuracy decreased with increasing distance (76.8% for 20m, 60.6% for 30m, and 35.3% for 50m). Particularly, videos with a 20m horizontal distance (close distance) exhibited accuracy above 80% in most scenarios. Moreover, accuracy trends in scenarios varied with vertical height and horizontal angle. At 0° (front view), accuracy mostly decreased with increasing height, while accuracy increased at 90° (side view) with increasing height. In addition, limited feature extraction for excavator activity recognition was found at 180° (backside view) due to occlusion of the excavator's bucket and arm. Based on these results, future studies should focus on enhancing the performance of vision-based recognition models by determining optimal surveillance camera locations at construction sites, utilizing deep learning algorithms for video super resolution, and establishing large training datasets using synthetic videos generated from game-engine-based virtual environments.

Recently, construction sites have faced significant challenges due to arbitrary changes and poor communication between general contractors and subcontractors. This study proposes a technological solution by integrating Unmanned Ground Vehicles (UGVs) into the existing workflow of apartment construction. By analyzing current processes, we identified a scenario where UGVs, equipped with LiDAR (Light Detection and Ranging) systems, can generate and provide real-time 3D models of construction sites. These models can be linked with extended reality (XR) technology or office PCs for intuitive comparisons between digital and actual site conditions as a digital twin of the construction site. The study suggests an improved construction process that enhances contractors' understanding and on-site efficiency and enables managers to monitor progress effectively. To address challenging terrain on construction sites, a caterpillar driven UGV was developed, equipped with stereo cameras, a LiDAR sensor for scanning and gathering environmental data, and an embedded PC for data processing. Utilizing SLAM (Simultaneous Localization and Mapping) technology, the UGV autonomously navigates and scans the site at night, minimizing disruptions. Additionally, an embedded system analyzes images from stereo cameras to assess the quality of construction, mapping the findings onto 3D models. This innovation allows site managers to efficiently verify construction quality and identify issues without manual inspections, significantly improving site management efficiency.

Leveraging large language models and safety accident report data has unique potential for analyzing construction accidents, including the classification of accident types, injured parts, and work processes, using unstructured free text accident scenarios. We previously proposed a novel approach that harnesses the power of fine-tuned Generative Pre-trained Transformer to classify 6 types of construction accidents (caught-in-between, cuts, falls, struck-by, trips, and other) with an accuracy of 82.33%. Furthermore, we proposed a novel methodology, saliency visualization, to discern which words are deemed important by black box models within a sentence associated with construction accidents. It helps understand how individual words in an input sentence affect the final output and seeks to make the model's prediction accuracy more understandable and interpretable for users. This involves deliberately altering the position of words within a sentence to reveal their specific roles in shaping the overall output. However, the validation of saliency visualization results remains insufficient and needs further analysis. In this context, this study aims to qualitatively validate the effectiveness of saliency visualization methods. In the exploration of saliency visualization, the elements with the highest importance scores were qualitatively validated against the construction accident risk factors (e.g., "the 4m pipe," "ear," "to extract staircase") emerging from Construction Safety Management's Integrated Information data scenarios provided by the Ministry of Land, Infrastructure, and Transport, Republic of Korea. Additionally, construction accident precursors (e.g., "grinding," "pipe," "slippery floor") identified from existing literature, which are early indicators or warning signs of potential accidents, were compared with the words with the highest importance scores of saliency visualization. We observed that the words from the saliency visualization are included in the pre-identified accident precursors and risk factors. This study highlights how employing saliency visualization enhances the interpretability of models based on large language processing, providing valuable insights into the underlying causes driving accident predictions.

In process plant construction, the implementation of design automation technologies is pivotal in reducing the timeframes associated with the design phase and in enabling the generation and evaluation of a variety of design alternatives, thereby facilitating the identification of optimal solutions. These technologies can play a crucial role in ensuring the successful delivery of projects. Previous research in the domain of design automation has primarily focused on parametric design in architectural contexts and on the automation of equipment layout and pipe routing within plant engineering, predominantly employing rule-based algorithms. Nevertheless, these studies are constrained by the limited flexibility of their models, which narrows the scope for generating alternative solutions and complicates the process of exploring comprehensive solutions using nonlinear optimization techniques as the number of design and engineering parameters increases. This research introduces a framework for automating plant design through the use of generative neural network models to overcome these challenges. The framework is applicable to the layout problems of process plants, covering the equipment necessary for production processes and the facilities for essential resources and their interconnections. The development of the proposed Neural-network (NN) based Generative Design Model unfolds in four stages: (a) Rule-based Model Development: This initial phase involves the development of rule-based models for layout generation and evaluation, where the generation model produces layouts based on predefined parameters, and the evaluation model assesses these layouts using various performance metrics. (b) Neural Network Model Development: This phase transitions towards neural network models, establishing a NN-based layout generation model utilizing Generative Adversarial Network (GAN)-based methods and a NN-based layout evaluation model. (c) Model Optimization: The third phase is dedicated to optimizing the models through Bayesian Optimization, aiming to extend the exploration space beyond the limitations of rule-based models. (d) Inverse Design Model Development: The concluding phase employs an inverse design method to merge the generative and evaluative networks, resulting in a model that outputs layout designs to meet specific performance objectives. This study aims to augment the efficiency and effectiveness of the design process in process plant construction, transcending the limitations of conventional rule-based approaches and contributing to the achievement of successful project outcomes.

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.

As the construction industry has recently transitioned to smart construction, the importance of project management using Building Information Modeling(BIM) is increasing. BIM and Construction Management(CM) share integrated management of various information that may arise in a project, allowing BIM to be utilized in various ways in CM. In this study, an Analytic Hierarchy Process(AHP) survey was conducted targeting BIM users at CM companies. The items of the AHP survey were divided into company-level factors and project-stage factors of BIM to measure utilization. This study analyzes the importance of BIM utilization factors used by CM companies through an AHP survey of BIM users and identifies the current status of the most utilized BIM. As a result of the AHP survey of company-level factors, the BIM application project showed the highest importance for BIM activation in CM. Furthermore, as a result of the AHP survey of factors by project stage, constructability review, interference review, and design change management showed the highest importance in that order. This implies that for BIM to be activated, CM companies need to increase the number of BIM-applied projects. Additionally, besides the factors most commonly used in current practice, there is a need to examine whether there are fewer factors utilizing BIM in a variety of applications. This study is expected to contribute to the activation and introduction of BIM in future construction project management.

The importance of housing construction and its solutions have been argued in the past decades; the increase in housing maintenance and refurbishment will also have significant implications for the UK and South Korea economy as the residential sector contributes almost a third of total UK construction output and 79% of those are low-income households in South Korea who are living in poor or serious condition. New technologies, including building information modelling (BIM), 3D scanning survey, 3D modeling and modular construction are essential in order to alleviate these problems. Therefore, the research reviews a 3D scanning and modelling in housing based on Scan-to-BIM concept and investigates applicable decision support and appraisal tools to enhance the practicality of housing information modelling. As a result, the housing sector in both UK and South Korea may benefit from their adoption as they make it possible to construct quicker, cheaper and safer buildings.

Virtual reality (VR) is increasingly utilized in the construction industry for diverse applications. Immersive virtual reality (IVR) offers practical experiences and educational opportunities for workers, enhancing productivity and safety. Efforts to optimize IVR involve analyzing biometric responses to monitor concentration, assess learning efficiency, and deliver personalized content. However, IVR faces challenges such as high production costs and prolonged production periods. Additionally, integrating biometric response recording into IVR experiences requires separate modules, further extending production timelines. To address these challenges, an integrated platform is necessary to streamline IVR production, user experience, and biometric response setup and recording. This study introduces such a platform designed to enhance the efficacy of IVR experiences through real-time biometric response analysis. The proposed platform comprises three main processes: (i) IVR content production using Unity; (ii) biometric response definition; and (iii) IVR content experience accompanied by generated logs for biometric responses. Firstly, IVR content production using Unity involves the development of IVR environments and scenarios. The platform incorporates diverse 3D models, including urban landscapes, building elements, and furniture, as the basis for IVR environments. Scenarios are constructed by integrating events into these environments, triggered by conditions such as reaching specific locations, the passage of time, or user interactions. Upon event activation, participants are presented with description UIs, quiz UIs, or route guidance, facilitating engagement and progression through interaction. Secondly, biometric responses encompass eye tracking and EEG. Eye tracking captures pupil diameter and fixation status on Areas of Interest (AOI), defined during IVR content production. EEG recording options include signals from each channel by default, as well as frequency-specific signals and EEG metrics such as attention, stress, fatigue, valence, and arousal. The platform supports the addition of new EEG metrics, enhancing customization and recording capabilities. Lastly, IVR content can be experienced alongside generated logs for biometric responses. The dataset enables monitoring and evaluation of participants' learning performance during IVR experiences, with the potential to enhance worker safety and productivity through immersive practical training and education.

Flood risk maps are used in urban flooding to understand the spatial extent and depth of inundation damage. To construct these maps, hydrodynamic modeling capable of simulating flood waves is necessary. Flood waves are typically fast, and inundation patterns can significantly vary depending on the terrain, making it essential to accurately represent the terrain of the flood source in flood wave analysis. Recently, methods using UAVs for terrain data construction through Structure-from-Motion or LiDAR have been utilized. These methods are crucial for UAV operations, and thus, still require a lot of time and manpower, and are limited when UAV operations are not possible. Therefore, for efficient nationwide monitoring, this study developed a model that can automatically generate terrain data by estimating depth information from a single image using c-GAN (Conditional Generative Adversarial Networks) and BBDM (Brownian Bridge Diffusion Model). The training, utilization, and validation datasets employed images from the ISPRS (2018) and directly aerial photographed image sets from five locations in the territory of the Republic of Korea. Compared to the ground truth of the test data set, it is considered sufficiently usable as terrain data for flood wave analysis, capable of generating highly accurate and precise terrain data with high reproducibility.

As extreme weather events such as heavy snowfall and typhoon become more frequent, climate change significantly impacts across various worldwide industries. With demands for dealing with this phenomenon, continuous achievements in safety diagnosis have been announced for large structures. Conversely, in agricultural infrastructures having lower risk to human life, there is lack of established safety diagnosis methods. However, considering expansion of high-value smart farm, the importance of plastic greenhouse cannot be overlooked. Therefore, this study aimed to develop on-site diagnosis technique for structural safety of steel structure greenhouse. To build an analysis model, we generated point cloud data of on-site greenhouse using a camera with LiDAR sensor. Subsequently, we extracted points corresponding to pipes using a pre-trained semantic segmentation model, achieving a pipe segmentation accuracy of 78.1%. These points were then converted into 3D frame model, with a location coordinate error of 5.4 cm for nine reference points, as measured by an on-site survey. In FEM structural analysis, nonlinearity of pipe connection was reflected. The loads were determined based on expected wind speed and snow depth in Korea. The structural safety of on-site model was diagnosed more vulnerable with 10.3% higher maximum axial stress, compared with standard model. Through this research, we expect the quantitative safety diagnosis of predicting greenhouse collapse risk. In addition, this technique will enable localized reinforcement strategies within the structure.

This research is centered on the adoption and evolution of Building Information Modeling Execution Plans (BEPs) and Employer's Information Requirements (EIRs) within the Japanese construction sector. Presently, these pivotal documents have not been comprehensively integrated into the Japanese industry, lacking a uniform standard. Addressing this gap, our study investigates the development of an automated system designed to generate optimal BEPs and EIRs, informed by project summaries and survey data. The system's development leverages insights from successful international BEP and EIR models, adapting these to align with the specific requirements of Japanese construction projects. It is tailored to facilitate key processes, including the assessment of BIM-capable personnel and the elucidation of BIM objectives within these projects. The objective of this research is to formulate actionable guidelines and tools that advance the implementation and effectiveness of BIM in Japan. By streamlining the generation of BEPs and EIRs, the system is expected to enrich BIM comprehension and application in the national construction landscape. This initiative not only serves the immediate needs of the local industry but also harmonizes global BIM methodologies with Japanese practices. In sum, this study contributes significantly to the refinement of BIM practices in Japan, promoting a more knowledgeable and efficient approach to construction project management.

Quality management work at construction sites requires a lot of time and effort in Excel-based document preparation and approval in addition to conducting quality tests. Because Excel-based quality management tasks are divided into quality test report printing and test progress, test report documentation, test report printout and approval, and management ledger preparation. This divided processes increase work time and reduce efficiency. Accordingly, a cloud-based construction site quality management system (Q-BOX) was developed to improve the productivity of quality management work. The purpose of this study is to analyze the productivity of Q-BOX, which is in the early stages of construction site introduction.

Nomura Real Estate is not just an investor but is actively involved in project promotion. Before construction begins, we review all drawings and point out areas that may lead to defects, check process charts and propose more rational processes, inform how to manage cost increases and decreases due to changes during construction, and provide advice from the perspective of quality, process and cost management by entering the project to enhance property value. We also provide advice from the perspectives of quality, process, and cost management by entering projects to improve property value. Depending on the project, we also work with third parties such as general contractors, manufacturers, and academic experts to propose products and new technologies that are not available locally. We have built a promotion system in which teams are formed centered around staff from the head office, with the addition of local staff from each country and third parties for each project. In recent years, we have been working on activities (knowledge exchange, manuals, internal forums, etc.) to horizontally expand our knowledge by providing opportunities for local staff to interact with each other, rather than keeping the knowledge gained through overseas business at the head office or in each country. This cycle of knowledge accumulation, deployment, and application enhances the quality of our projects, which in turn leads to the trust of our partners in each country.

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.

Computer vision techniques have been widely employed in automated construction management to enhance safety and prevent accidents at construction sites. However, previous research in the field of vision-based approaches has often overlooked small-sized construction tools. These tools present unique challenges in data collection due to their diverse shapes and sizes, as well as in improving model performance to accurately detect and classify them. To address these challenges, this study aimed to enhance the performance of vision-based classifiers for small-sized construction tools, including bucket, cord reel, hammer, and tacker, by leveraging synthetic images generated from a 3D virtual environment. Three classifiers were developed using the YOLOv8 algorithm, each differing in the composition of the training dataset: (i) 'Real-4000', trained on 4,000 authentic images collected through web crawling methods (1,000 images per object); (ii) 'Hybrid-4000', consisting of 2,000 authentic images and 2,000 synthetic images; and (iii) 'Hybrid-8000', incorporating 4,000 authentic images and 4,000 synthetic images. To validate the performance of the classifiers, 144 directly-captured images for each object were collected from real construction sites as the test dataset. The mean Average Precision at an IoU threshold of 0.5 (mAP_0.5) for the classifiers was 79.6%, 90.8%, and 94.8%, respectively, with the 'Hybrid-8000' model demonstrating the highest performance. Notably, for objects with significant shape variations, the use of synthetic images led to the enhanced performance of the vision-based classifiers. Moreover, the practical applicability of the proposed classifiers was validated through confidence scores, particularly between the 'Hybrid-4000' and 'Hybrid-8000' models. Statistical analysis using t-tests indicated that the performance of the 'Hybrid-4000' model would either matched or exceeded that of the 'Hybrid-8000'model based on confidence scores. Thus, employing the 'Hybrid-4000' model may be preferable in terms of data collection efficiency and processing time, contributing to enhanced safety and real-time automation and robotics in construction practices.

This resarch showed that the factors contributing to the success or failure of international joint ventures (IJVs), such as those between Japanese and American engineering companies, are influenced by information sharing and consensus building within the IJV. Based on the available information about such IJV projects, the factors were evaluated by the following statical method. However, the mechanism is not simple and includes many mediating and interaction effects between explanatory variables due to the uncertainty of the environment surrounding the project. Based on existing research on risk management in Project management area, it was revealed that the analyzed factors behind IJV success and failure based on the analysis model that integrates the above perspectives and incorporates typical risk items for projects as pointed out by PM-related academic and practionors societies. It was demonstrated that it can be explained quantitatively by applying the Multivariate analysis method. This analysis method could be expanded to the future evaluation of the projects risk item during the planning stage of the projects.

Recent advances in artificial intelligence technology have led to active research aimed at systematically managing the productivity and environmental impact of major management targets such as heavy equipment at construction sites. However, challenges arise due to phenomena like partial occlusions, resulting from the dynamic working environment of construction sites (e.g., equipment overlapping, obstruction by structures), which impose practical constraints on precisely monitoring heavy equipment. To address these challenges, this study aims to enhance automated multi-object tracking (MOT) in scenarios involving long-term occlusions across consecutive frames for heavy construction equipment. To achieve this, two methodologies are employed to address long-term occlusions at construction sites: (i) tracking-by-detection and (ii) video inpainting with generative adversarial networks (GANs). Firstly, this study proposes integrating FairMOT with a tracking-by-detection algorithm like ByteTrack or SMILEtrack, demonstrating the robustness of re-identification (Re-ID) in occlusion scenarios. This method maintains previously assigned IDs when heavy equipment is temporarily obscured and then reappears, analyzing location, appearance, or motion characteristics across consecutive frames. Secondly, adopting video inpainting with GAN algorithms such as ProPainter is proposed, demonstrating robustness in removing objects other than the target object (e.g., excavator) during the video preprocessing and filling removed areas using information from surrounding pixels or other frames. This approach addresses long-term occlusion issues by focusing on a single object rather than multiple objects. Through these proposed approaches, improvements in the efficiency and accuracy of detection, tracking, and activity recognition for multiple heavy equipment are expected, mitigating MOT challenges caused by occlusions in dynamic construction site environments. Consequently, these approaches are anticipated to play a significant role in systematically managing heavy equipment productivity, environmental impact, and worker safety through the development of advanced construction and management systems.

BIM, as a means of integrating information into the entire lifecycle of construction, greatly enhances productivity in the AEC (Architecture, Engineering, and Construction) field. With the advent of the Fourth Industrial Revolution, new-generation technologies such as AI and automation have also entered the researchers' field of vision. Nowadays, there is an increasing number of studies combining these two powerful tools for applications in the AEC domain, particularly focusing on their deep and proactive integration into construction project scheduling. This study aims to systematically review the current research status of AI technology in construction project scheduling based on Building Information Modeling (BIM). Through meticulous selection, we ultimately identified 46 peer-reviewed articles as the subject of our investigation. Building upon these sources, we delve into the following inquiries: firstly, we analyze which specific AI algorithms and technologies have been widely researched and practically applied in BIM-based construction project scheduling. Secondly, we examine the challenges and limitations of AI application in construction project scheduling within the BIM environment. Lastly, we explore strategies for further advancing AI technology in supporting construction project scheduling with BIM, aiming to better meet the demands of the construction industry. In terms of technological application, we observe that while decision-making was the primary focus of AI technology in the past, automation now occupies a more significant position in construction project scheduling. Looking ahead, we anticipate that advanced technologies such as deep learning and genetic algorithms will play a more substantial role in this field, offering more efficient and accurate solutions for construction project scheduling. This paper systematically delineates the current research status of AI in construction project scheduling within the BIM environment, providing not only technical guidance for innovation in current construction project management but also valuable insights into the future development directions of AI technology in project scheduling.

This paper proposes the development of an advanced Risk Management System (RMS) using Risk-Based Methodologies (RBM) specifically tailored for addressing construction defects in industrial plants. Urbanization and industrialization demand robust frameworks to handle the complexities and safety concerns in construction projects. Traditional risk management often overlooks critical aspects such as persistent construction defects. This paper discusses the development of an innovative Risk Management System (RMS) that integrates Risk-Based Methodologies (RBM) specifically for construction defect mitigation in industrial settings. The study centers around the implementation of Risk-Based Inspection (RBI) techniques, tailored to enhance traditional risk management systems. This includes developing a specialized risk assessment tool alongside an online management platform, designed to provide continuous monitoring and comprehensive management of construction risks. The proposed system-RBE-i (Risk-Based Execution for Installation)-focuses on identifying, evaluating, and mitigating risks effectively, utilizing a systematic approach that integrates seamlessly into existing construction workflows. The RBE-i system's core lies in its ability to conduct thorough risk analyses and real-time data provision. It uses digital technologies to improve communication, operational efficiency, and decision-making processes across construction projects. By applying these methodologies, the system enhances safety and ensures more efficient project execution by preemptively identifying potential risks and addressing them promptly. Field applications of RBE-i have demonstrated its effectiveness in significantly reducing construction defects, thus validating its potential as a transformative tool in construction risk management. The system sets new industry standards by shifting from reactive to proactive risk management practices, ultimately leading to safer, more reliable, and cost-effective construction operations. In conclusion, the RMS developed through this study not only addresses the pressing needs of construction risk management but also proposes a paradigm shift towards more proactive, structured, and technology-driven practices. The successful integration of the RBE-i system across various pilot projects illustrates its significant potential to improve overall project outcomes, making it an invaluable addition to the field of construction management.

Since 2017, South Korea has been the first country in the world to implement a national certification system for Zero Energy Buildings (ZEB). This system aims to maximize the energy efficiency of buildings to reduce greenhouse gas emissions and reduce energy consumption in the building sector using renewable energy. To achieve this goal, the ZEB certification system classifies green buildings into five grades based on the energy independence rate. However, the current ZEB certification system based on the energy independence rate is only considered a requirement for building completion, losing its original intent. This study aims to highlight the problems and limitations of the ZEB certification system based on the energy independence rate and to propose an operational plan for the system that can genuinely reduce energy consumption in the building sector. For this, the actual energy consumption and the renewable energy production referenced during the certification of 10 ZEB-certified buildings were quantified and compared with the energy independence rate. The total energy consumption, energy production, life cycle cost, performance coefficient of equipment, and other key indicators were analyzed to evaluate the actual effects of ZEB certification on energy savings. As a result, the simple energy independence rate-based ZEB certification was judged to be inconsistent with the original intent of the system. The ZEB certification system needs to be re-established to reflect the design of systems that can activate and utilize energy savings and renewable energy usage in buildings. Additionally, improvements in the management and inspection systems are necessary to determine how much they contribute to actual reductions in greenhouse gas emissions and energy consumption post-certification.

In recent years, the building industry has seen a fundamental transition due to Digitalization Transformation (DX), with the aim of improving efficiency, productivity, sustainability, and cost-effectiveness. In particular, literature has significantly emphasized Smart Buildings (SBs), which are expected to grow in the global marketplace in the coming years. The most noticeable benefits include energy efficiency, increased occupant comfort and productivity, and a reduction in the building's impact on the environment. Most importantly, the shift to SBs has resulted in major changes to how traditional business practices are carried out. The Facility Asset Management (FAM) domain is one key area undergoing considerable changes to meet the needs of managing functional SBs. Despite this shifting landscape, the changes and prospective extensions to the business areas of FAM in the context of SBs remain largely unexplored. Thus, to address this limitation, this paper aims to investigate the potential changes (i.e., either the addition of a new function or the expansion of an existing function) of the FAM domain from the context of SBs. To achieve this objective, • First, based on a generic model of FAM proposed by Jin et al. (2024), a three-level hierarchical classification of FAM business functions for a conventional building is proposed. • Second, the concept of SBs is thoroughly discussed, including its drivers, features, enablers, and improvement areas. • Finally, a new FAM business function for SB is proposed, aligning with the distinct characteristics of SBs. As there are no established functional taxonomies of FAM, the comprehensive breakdown of FAM business functions presented in this study can be used as a standardized functional breakdown of the FAM domain. Moreover, it can also be used to facilitate robust and integrated information management practices throughout the whole lifecycle of SB facilities.

Greenhouses require various control systems to create an optimal environment, and from an architectural engineering perspective, the uniformity of the internal environment is crucial for crop growth. However, greenhouses are structurally exposed to external weather conditions, leading to a high probability of variations in temperature, humidity, CO2 levels, lighting, etc., across different zones within the greenhouse. Such non-uniformity can impact the growth rate, quality, and yield of crops, highlighting the necessity of maintaining a consistent environment within the greenhouse. To address this, experiments utilizing Computational Fluid Dynamics (CFD) simulations were conducted targeting greenhouses in Pocheon, South Korea, focusing on the central heating and cooling systems to propose an optimal design considering the uniformity of internal temperatures. Subsequently, validation was performed using measurements from temperature and humidity sensors within the greenhouses. The heating and cooling systems operate based on indoor temperatures, activating cooling when indoor temperatures exceed the set cooling temperature in summer and heating when temperatures fall below the set heating temperature in winter. A standard greenhouse model was set as case 1, and experiments were conducted by adjusting the position and spacing of the fabric ducts of the heating and cooling systems, resulting in six categorized cases. Variations in temperature and humidity distribution were observed among the cases, and quantitative analysis provided optimal positions and spacing for the fabric ducts. The results of this study can serve as foundational data for developing environmental control solutions for agricultural facilities.

As climate change escalates extreme weather events, the structural durability of plastic greenhouses, constituting 90% of Korea's facility agriculture, emerges as a critical issue. These greenhouses are pivotal for year-round crop cultivation and high-quality agricultural production. In 2021, collapses caused around US$2 million in damages, mainly due to heavy snowfall and strong winds, accounting for 97% of incidents. The Korean Ministry of Agriculture responded by disseminating disaster-resistant standardized designs, yet more robust standards are needed. Current designs rely on elastic analysis, but plastic greenhouses display nonlinear behavior due to factors like residual stress and local buckling. Our study employs a refined plastic hinge method and finite element analysis to analyze structures, considering progressive yielding. We conducted loading tests using scale down models of plastic greenhouses in accordance with similitude laws. Based on these tests, the deformation of models under different load conditions was measured and compared with the deformation of greenhouse using our nonlinear structural analysis. This study will contribute to the development of reliable design criteria for plastic greenhouses in response to climate extremes such as heavy snowfall and typhoons. In addition, by identifying the deformation characteristics of plastic greenhouses due to loads, it can contribute to establishing usability standards for greenhouses, and reinforcement measures for vulnerable areas which are easily deformed under load can be considered.

this paper describes optimization efforts throughout the process from design, delivery and construction to operations in two CLT (Cross Laminated Timber) building projects. CLT gives us an opportunity to change our mindset and examine issues concurrently instead of working separately by phase or by profession. The author, as project manager, led the interdisciplinary discussions.

Concrete arch dams, unlike conventional concrete gravity dams, have thin arch-shaped cross sections and must be designed considering a three-dimensional shape. In particular, double-curvature arch dams, which have arch-shaped vertical and horizontal sections, require careful consideration during design due to their unique shape. Although stress analysis is complex, and various factors need to be considered during the design, these dams offer economic advantages as they require less material. Consequently, numerous double-curvature arch dams have been constructed worldwide, and ongoing research focuses on optimizing their shapes. In this study, an efficient optimization algorithm was developed for the shape optimization of concrete arch dams with double-curvature using genetic algorithms and improved population initializing technique. The developed technique utilized domain knowledge in the field of arch dams to generate an excellent initial population. To assess the relevance of domain knowledge, an investigation was conducted on the accumulated knowledge and empirical formulas from literature. Two pieces of domain knowledge can be gleaned from the iterative structural design experiences associated with arch dams. First, it concerns the thickness of the central cantilever of an arch dam. For minimum tensile stress, it is best to make the thickness as thin as possible at the dam crest and gradually become thicker as it goes down. The second aspect concerns the sliding stability of the arch dam, which depends on the central angle of the horizontal section. This angel is important for stability because the plane arch serves to transfer the hydraulic load from the reservoir to both abutments. Also, preliminary design formulas for arch dams from a manual written by the United States Bureau of Reclamation (USBR) were used. On the other hand, since domain knowledge is based on engineering experiences and data from existing dams, its usability should be verified by comparing it with the results of design optimization performed by classic genetic algorithms. To validate the performance of the optimization algorithm with the improved population initialization technique, a test site with an existing dam was selected, and algorithmic application tests were conducted. Stress analysis is performed for each design iteration, evaluating constraints and calculating fitness as the objective function. The results confirmed that the algorithm developed in this study exhibits superior performance in terms of average fitness and convergence rate compared to classic genetic algorithms.

Reinforced concrete (RC) , a major contributor to resource depletion and harmful emissions, fuels research on optimizing its design. Optimizing RC structures is challenging due to the mix of discrete and continuous variables, hindering traditional differentiation-based methods. Thus, this study aims to optimize RC structures cost-effectively using deep reinforcement learning. When the Agent selects design variables, Environment checks design criteria based on KDS 14-20 code (South Korea) and calculates reward. The Agent updates its Neural Network with this reward. Target for optimization is a simply supported doubly RC beam, with design variables including cross-section dimensions, sizes and quantities of tension and compression reinforcement, and size of stirrups. We used 200,000 training sets and 336 test sets, each with live load, dead load, beam length variables. To exclude labeled data, multiple training iterations were conducted. In the initial training, the reward was the ratio of maximum possible cost at beam length to the designed structure's cost. Next iterations used the ratio of optimal values by the previous Agent to the current Agent as the reward. Training ended when the difference between optimal values from the previous and current Agent was within 1% for test data. Brute Force Algorithm was applied to the test set to calculate the actual cost-optimal design for validation. Results showed within 10% difference from actual optimal cost, indicating successful deep reinforcement learning application without labeled data. This study benefits the rapid and accurate calculation of optimized designs and construction processes in Building Information Modeling (BIM) applications.

Since the commercial operation of Kori Unit 1 in 1978, South Korea has been at the forefront of nuclear power plant construction for nearly 40 years. South Korea have constructed 27 nuclear power plants domestically and an additional 4 plants in the UAE. Leveraging this extensive experience, the country has developed and operated a nuclear power plant construction management system with a focuse on procedures and processes development, along with a user-friendly approach. The initial nuclear power plant construction management system operated was NPCS (Nuclear Power Plant Construction System), which was based on drawing inquiries. Subsequently, a system centered on construction project management, known as NPCMS (Nuclear Power Plant Construction Management System), was established and operated. In 2009, through the export of nuclear power plant construction to the UAE, research on advancing construction management led to the implementation of INPCMS(Integrated Nuclear Power Construction Management System), which is functionally enhanced. Over time, in response to changes in the construction environment, demands from international operators, and alignment with global standards, South Korea is in the process of redeveloping its INPCMS. This aims to establish a global nuclear power plant construction management system, outlining key features and future directions.

India ranks fourth in the world in terms of construction investment (2021), and the construction industry is expected to grow even further in the future due to the influx of people into urban areas. However, considering the state of construction sites near Delhi, there are concerns that the industry will no longer be able to meet the increasingly sophisticated demands of the construction market (quality, construction schedule, safety, etc.). This study will grasp the actual situation of management of building construction sites and consider the problems.

This research presents a joining system that changes an irregularly shaped material into a member that can be joined and disassembled as a single unit by attaching 3D printed joints. In extreme environments, including extraterrestrial environments, it takes much time and costs to supply materials from Earth. In addition, when living and working in such environments for long periods, a technology enabling the use of locally accessible materials or elements brought to the site for different purposes is essential to realize the construction based on the idea of In-Situ Resource Utilization (ISRU). This research proposes a joining system inspired by the traditional Japanese wooden joinery Kigumi. By integrating Kigumi's excellent features, such as high construction performance, disassemble performance, and mechanical performance, with 3D printing and 3D scanning technologies, the proposed joining system aims to enable irregular-shaped elements to be assembled and disassembled from one another without any fasteners (e.g., bolts, nails, and adhesive). Prototypes of the proposed joints, which apply the Japanese Koshikake-Ari-Tsugi technique, were printed by an additive manufacturing-type desktop 3D printer, then the investigation focused on determining the optimal clearance for the joint. Based on the results, a simple mockup was constructed. Its constructability and mechanical performance were examined. The findings revealed that the joints applying the traditional Japanese Kigumi were printable by the additive manufacturing-type desktop 3D-printer with proper clearance settings, and the proposed joining system shows high performance in construction, including disassembly. The findings provide insights into the feasibility of desktop 3D printing construction in extreme environments.

The construction industry is renowned for its dynamic and intricate characteristics, which demand proficient leadership skills for successful project management. However, the existing training platforms within this sector often overlook the significance of soft skills in leadership development. These platforms primarily focus on safety, work processes, and technical modules, leaving a noticeable gap in preparing future leaders, especially students in the construction domain, for the complex challenges they will encounter in their professional careers. It is crucial to recognize that effective leadership in construction projects requires not only technical expertise but also the ability to communicate effectively, collaborate with diverse stakeholders, and navigate complex relationships. These soft skills are critical for managing teams, resolving conflicts, and driving successful project outcomes. In addition, the construction sector has been slow in adopting and harnessing the potential of advanced emerging technologies such as virtual reality, artificial intelligence, to enhance the soft skills of future leaders. Therefore, there is a need for a platform where students can practice complex situations and conversations in a safe and repeatable training environment. To address these challenges, this study proposes a pioneering approach by integrating conversational AI techniques using large language models (LLMs) within virtual worlds. Although LLMs like ChatGPT possess extensive knowledge across various domains, their responses may lack relevance in specific contexts. Prompt engineering techniques are utilized to ensure more accurate and effective responses, tailored to the specific requirements of the targeted users. This involves designing and refining the input prompts given to the language model to guide its response generation. By carefully crafting the prompts and providing context-specific instructions, the model can generate responses that are more relevant and aligned with the desired outcomes of the training program. The proposed system offers interactive engagement to students by simulating diverse construction site roles through conversational AI based agents. Students can face realistic challenges that test and enhance their soft skills in a practical context. They can engage in conversations with AI-based avatars representing different construction site roles, such as machine operators, laborers, and site managers. These avatars are equipped with AI capabilities to respond dynamically to user interactions, allowing students to practice their communication and negotiation skills in realistic scenarios. Additionally, the introduction of AI instructors can provide guidance, feedback, and coaching tailored to the individual needs of each student, enhancing the effectiveness of the training program. The AI instructors can provide immediate feedback and guidance, helping students improve their decision-making and problem-solving abilities. The proposed immersive learning environment is expected to significantly enhance leadership competencies of students, such as communication, decision-making and conflict resolution in the practical context. This study highlights the benefits of utilizing conversational AI in educational settings to prepare construction students for real-world leadership roles. By providing hands-on, practical experience in dealing with site-specific challenges, students can develop the necessary skills and confidence to excel in their future roles.

As global imperatives shift toward sustainability and carbon neutrality, the construction industry faces an urgent need to address its environmental impact, particularly within steel construction. Despite the increasing adoption of sustainable practices, a detailed understanding of the entire lifecycle emissions of structural steel, especially within the rapidly evolving Chinese market, remains a significant gap. This study introduces a comprehensive life-cycle assessment (LCA) approach to map the carbon footprint of structural steel construction, with a focus on Chinese structural steel as a case study. By adopting a cradle-to-cradle perspective, the research aims to highlight and address the environmental impact across the entire lifecycle of steel used in construction. Specifically, this study will 1) develop a detailed LCA model that encapsulates the environmental impacts of structural steel from production, use, and recycling phases, 2) dentify and analyze carbon hotspots and inefficiencies within the lifecycle of Chinese structural steel, and 3) evaluate and suggest strategies for stakeholders to minimize carbon emissions, moving towards carbon-neutral steel construction. Leveraging a process-based LCA framework, this study captures the material, energy, and emissions flows associated with the lifecycle of structural steel, including steel production, fabrication, transportation, construction, and recycling, in the context of Chinese construction practices. The model is enriched with data from current Chinese steel construction projects, ensuring its accuracy and applicability. Through systematic analysis, the study pinpoints critical phases where carbon emissions can be significantly reduced. Preliminary Results show significant carbon emission sources within the production, fabrication, and transportation phases of Chinese structural steel. These insights are crucial for devising targeted reduction strategies, such as improving production and fabrication efficiency, optimizing logistics, and enhancing material recyclability. The anticipated impact of this research is multi-faceted: providing a robust framework for assessing and managing the carbon footprint of steel construction, guiding industry and policy-makers towards sustainable practices, and setting a precedent for carbon management in steel construction worldwide. This research marks a significant step towards achieving carbon neutrality in steel construction, with a particular focus on Chinese structural steel. Through a comprehensive LCA model, this study offers a deep dive into the lifecycle emissions of steel construction, paving the way for actionable strategies to reduce the environmental impact, contributing to the global endeavor towards carbon-neutral construction.