An estimated investment gap of $176 billion needs to be filled over the next ten years to improve America's inland waterway transportation systems. Many of these infrastructure systems are now beyond their original 50-year design life and are often behind in maintenance due to funding constraints. Therefore, long-term maintenance strategies (i.e., asset management (AM) strategies) are needed to optimize investments across these waterway systems to improve their condition. Two common AM strategies include policy-driven maintenance and performance-driven maintenance. Currently, limited research exists on selecting the optimal AM approach for managing inland waterway transportation assets. Therefore, the goal of this study is to provide a decision model that can be used to select the optimal alternative between the two AM approaches by considering key uncertainties such as asset condition, asset test results, and asset failure. We achieve this goal by addressing the decision problem as a single-criterion problem, which calculates each alternative's expected value and certain equivalence using allocated monetary values to determine the recommended alternative for optimally maintaining navigable waterways. The decision model considers estimated and predicted values based on the current state of the infrastructure. This research concludes that the performance-based approach is the optimal alternative based on the expected value obtained from the analysis. This research sets the stage for further studies on fiscal constraints that will effectively optimize these assets condition.

Design-bid-build (DBB) is the most common project delivery method among highway projects. State Highway Agencies (SHAs) usually apply a low-bid approach to select contractors for their DBB projects. In this approach, the Federal Highway Agency suggests SHAs heighten contractors' competition to lower bid prices. However, these attempts may become ineffective due to collusive bidding arrangements among certain contractors. One common strategy is the rotation of winning bidders of a group of contractors who bid on many of the same projects. These arrangements may also be specific to a particular region or vary in time. Despite the practices' adverse effects on bidding outcomes, an effective model to detect red-flag bidding patterns is lacking. This study fills the gap by proposing a novel framework that utilizes pattern mining techniques and statistical tests for unusual pattern detection. A case study with historical data from an SHA is conducted to illustrate the proposed framework.

State highway agencies (SHAs) typically apply a competitive procurement procedure to select contractors for their design-bid-build projects. Since the level of competition affects construction bid prices and project outcomes, the Federal Highway Agency (FHWA) suggests SHAs seek ways to improve competition among contractors continuously. However, they rarely conduct an empirical assessment of the current competition level necessary to identify room for improvement. Besides the number of bidders on a project, other factors such as winning or losing rates among the contractors in previous projects can also indicate the degree of competition; only a few contractors may have won the majority of the projects in a specific region. However, few studies have investigated such factors. This paper proposes a network analysis-based approach to evaluating contractor competition levels of highway projects using historical bid tabulation data. The proposed method provides insights into overall competition levels, the determination of competitive contractors, and winning rate distribution among contractors.

As a project delivery method, Design-Build (DB) has provided owner, architect, and contractor groups with a process of early design and rapid construction for the past three decades. Although there are many benefits to using standard DB, dissatisfaction has arisen due to limitations to innovate, limited owner involvement during design, and often lengthy procurement. Progressive Design-Build (PDB) has become an appealing alternative providing benefits not seen with standard DB. This paper investigates how PDB impacts a project and how it compares against standard DB; it also presents a proposed framework for evaluating the owner's responsibility and assessment of a project, which we named the "Four Pillars of Project Success". The four pillars are defined with respect to an owner's responsibility and assessment of a project, including project predictability, project risk, project schedule, and project cost. We conducted a literature review, examined several public project case studies, analyzed PDB project information collected by the Design-Build Institute of America (DBIA), and held stakeholder interviews with owners, contractors, and architects who have used both PDB and standard DB. This paper offers insight into PDB's structure and outcomes so an owner group can make an informed decision when considering PDB as their next construction contracting method.

Infrastructure procurement has been a major engagement route between China and Africa. This contributes immensely to the gradual infrastructure development seen on the continent. However, maturing discourse purports that these infrastructure collaborations lack intentionality in the continuous development of strategic guidelines and policies for effective implementation despite their uniqueness and criticality. This study proposes that an efficient approach to policy recommendations is through the political and economic analysis (PEA) of these partnerships using public-private partnership (PPP) optics. Unquestionably, these partnerships are representative of the concept of diplomatic transnational public-private partnership (DT-PPP) where infrastructure is procured through the collaboration of public (African governments) and private sector (Chinese state-owned corporations) who provide the managerial, financial, and technical resources for the project implementation. Given the quest for sustainable win-win, this study identifies strategies towards the realization of win-win in the implementation (i.e enablers of win-win) such that fairness and co-benefit, as well as interests, will be achieved. Thus, based on the PEA framework, case scenarios from Ghana and Nigeria using expert interviews identify the criticalities and best practices for the realization of these enablers at the development phase. Findings indicate more effort is required of the public sector (African host countries) in terms of people, structure/institutions, and the implementation processes. Recommendations include improvement of environmental management structures, contract administration procedures, external stakeholders/local community engagement mechanisms, knowledge and technology transfer procedures, and sector-based project operation and maintenance culture and systems. Additionally, actors must have emotional intelligence, good problem-solving abilities, and overall ensure cordial relationships for continued bilateral cooperation.

In recent years, Owner, Architects, and Contractor are increasingly collaborating with each other from pre construction phase in construction projects, which is called Early Contractor Involvement (ECI). In Japan, the ECI method has been introduced in several public building projects since 2015. The purpose of this study is mainly to clarify the characteristics of the ECI method in Japan and to compare the contract clauses of the ECI method in the UK and the USA. The results of the survey are as follows. (1) the ECI method was supposed to make it possible to achieve appropriate quality, cost, and construction period by reflecting Contractor's technology and know-how in the design documents and specifications. (2) According to the database, there were 27 cases of the ECI method in Japan from 2015 to 2021, of which 13 cases for which bidding information could be obtained had a variety of technical proposals, mainly VE proposals, depending on the project characteristics. (3), Japan's ECI method has very much in common with SBC + PCSA in the UK. On the other hand, ECI Method in Japan differs from in the UK in that Owner, Architect, and Contractor enter into a partnership agreement, which is similar to ConsensusDocs CD541 in the USA. (4) The ECI method in Japan has the following problems: Owner depends on Contractor for cost control, the division of roles among project members is complicated, and more work from Owner than the DBB method are required.

Change Orders generally impact cost and schedule performance of highway projects. However, highway projects that do not have any change orders also face cost growth and schedule delays. This study seeks to determine the cost and schedule performance of Texas DOT projects by collecting project data for 120 highway projects completed between 2016 to 2020. For the study, we selected project data that has zero or negative change orders which were then grouped and analyzed based on their Project Types i.e., maintenance works; structural works; restoration and rehabilitation works; and safety works. The study found that performance of Maintenance and Safety type projects had less cost and schedule growth among the data analyzed. Statistical tests also found that even though the projects have no change orders, Rehabilitation and Restoration type projects experienced significant schedule growth compared to others. However, the data did not show any significant cost and schedule growth for the projects when statistical tests were performed on overall data. The study concluded that highway projects are experiencing schedule growth even though the projects had no change orders. Results from the study can help planners, engineers, and administrators to gain better insight on how different types of highway projects are performing in terms of cost and schedule and eventually derive appropriate solutions to minimize cost and schedule growth in such projects.

Claims and disputes are major causes of cost and schedule overruns in the construction business. In order to manage claims and disputes effectively, it is necessary to analyze various types of contract documents punctually and accurately. Since volume of such documents is so vast, analyzing them in a timely manner is practically very challenging. Recently developed approaches such as artificial intelligence (AI), machine learning algorithms, and natural language processing (NLP) have been applied to various topics in the field of construction contract and claim management. Based on the systematic literature review, this paper analyzed the goals, methodologies, and application results of such approaches. AI methods applied to construction contract management are classified into several categories. This study identified possibilities and limitations of the application of such approaches. This study contributes to providing the directions for how such approaches should be applied to contract management for future studies, which will eventually lead to more effective management of claims and disputes.

Construction bid documents contain various errors or discrepancies giving rise to uncertainties. The errors/discrepancies/ambiguities in the bid document, if not identified and clarified before the bid, may cause dispute and conflict between the contracting parties. Given the fact that bid document is a major resource in estimating construction costs, inaccurate information in bid document can result in over/under estimating. Thus, any questions from bidders related to the errors in the bid document should be clarified by employers before bid submission. This study aims to examine the pre-bid queries, i.e., pre-bid request for information (RFI), from state DoTs of the United States to investigate error types most frequently encountered in bid documents. For the study, around 200 pre-bids RFI were collected from state DoTs and were classified into several error types (e.g., coordination error, errors in drawings). The analysis of the data showed that errors in bill of quantities is the most frequent error in the bid documents followed by errors in drawing. The study findings addressed uncertainty types in construction bid documents that should be checked during a bid process, and, in a broader sense, it will contribute to advancing the construction management body of knowledge by clarifying and classifying bid risk factors at an early stage of construction projects.

Improving long-term performance in highway projects is an imperative goal for public administrations. Project delivery and procurement methods might provide an opportunity to align design and construction processes with this goal. Previous studies have explored whether project delivery methods impact the long-term performance of highway projects. However, these studies did not focus specifically on how core elements within the procurement might relate to long-term performance. Thus, this research aims to fill this gap by exploring to what extent and how long-term evaluation criteria are considered in design-build best-value procurement of highway projects. To this end, content analysis was conducted on 100 projects procured between 2009 and 2019 by 19 DOTs across the U.S. The analysis of 365 evaluation criteria found that (1) roughly 11% of them related to long-term performance. (2) The weight given to these criteria in the overall technical proposal was lower than 30%. (3) Sixty-five percent (65%) of long-term evaluation criteria focused on design while 15% related to materials and technology, respectively. The results of this study are a first steppingstone to initiate a deep exploration of the relationship between procurement practices and actual project performance. Currently, with sustainability and life cycle assessments being top concerns in infrastructure projects, this line of research might be of particular interest to DOTs and highway agencies across the U.S. and worldwide.

Public-private partnerships (PPP) projects are becoming popular in both developed and developing countries due to their ability to access new financing sources and transfer certain project risks to the private sector. PPP has been an active research area where the concept of Critical Success Factors (CSF) is often discussed by researchers. This study aims to identify the CSFs for various PPP infrastructure projects that have been explored in previous CSF studies. This article reviewed the literature about CSF in PPP projects from the years 2002 to 2021, compared the findings of studies regarding the identified CSFs, and consolidated the CSFs that can be applied to various PPP infrastructure projects. The results showed that dominant research focused on general infrastructure, where CSFs can be applied to all infrastructure sectors rather than any specific sector. The most identified CSFs from the study are favorable and efficient legal frameworks, appropriate risk allocation and sharing, a robust and reliable private consortium, a competitive and transparent procurement process, and political support and stability. The findings from the study can provide an overview of CSFs that are relevant to specific PPP infrastructure sectors like building infrastructure, transportation, water, etc. as well as for general infrastructure. In addition, the results can also be used for further empirical analysis.

Construction workers are engaged in many activities that may expose them to serious hazards, such as falling, unguarded machinery, or being struck by heavy construction equipment. Despite extensive research in building information modeling (BIM) for safety management, current approaches, detecting safety issues after design completion, may limit the opportunities to prevent predictable and potential accidents when decisions of building materials and systems are made. In this respect, this research proposes a proactive approach to detecting safety issues from the early design phase. This research aims to explore accident precursors and integrate them into BIM for tracking safety hazards during the design development process. Accident precursors can be identified from construction incident reports published by OSHA using a text mining technique. Through BIM-integrated accident precursors, construction safety hazards can be identified during the design phase. The results will contribute to supporting a successful transition from the design stage to the construction stage that considers a safe construction workplace. This will advance the body of knowledge about construction safety management by elucidating a hypothesis that safety hazards can be detected during the design phase involving decisions about materials, building elements, and equipment. In addition, the proactive approach will help the Architecture, Engineering and Construction (AEC) industry eliminate occupational safety hazards before near-miss situations appear on construction sites.

In the era of the 4th Industrial Revolution, smart technology being considered to improve productivity breakthroughs is in the spotlight as a means to replace traditional construction technology in the construction industry. However, various problems are occurring in construction sites using smart technology and causing negative impacts on construction projects. Therefore, the objective of this study is to identify risk factors that occur when smart technologies are used in construction projects. To achieve this purpose, this study investigated the difficulties at construction projects using smart technology, and risk factors were derived based on site surveys and literature. The risk factors were measured by experts, and then a total of 19 risk factors was derived by exploratory factor analysis. As a result, risks were classified as 5 factors, the institutional factor is the most difficult response, and the government needs anticipative system improvement and a long-term plan. The research findings provide practical implications for construction experts trying to apply smart technology in construction sites and construction policy-makers to revitalize smart technology.

Conflicts in public construction projects become more serious and complex so that they have a negative effect on performance of projecets. Conflicts in public construction projects are defined as expanding in complaints. This study analyzes the relationship structure and effect on performance between complaints and conflicts. First of all, 219 survey data collected from industry experts were used to derive complaints arising from the project and to understand the characteristics of each complaint. In the case of environmental damages, rather than environmental damage during construction, harmful substances or effects that can occur in completed facilities cause complaints from local residents, and opposition from environmental groups has a great effect on time and cost increase. As for safety damage, civil complaints related to prevention and countermeasures for safety accidents occur frequently, and additional construction affects cost increases. Through this study, it is possible to understand the serious complaints that are prone to conflict in public construction projects, their frequency, and the performance of the project.

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

Because construction work is conducted outdoors, construction workers are affected by harmful environmental factor. Especially, Particulate Matter (PM₁₀) is one of the harmful environmental factors with a diameter of 10㎍/m³ or less. When PM₁₀ is inhaled by human, it can cause fatal impact on the human. Contrary to the various analyses of health impact on PM₁₀, the research on the relationship between construction accidents and PM₁₀ are few. Therefore, this study aims to conduct the relative frequency analysis which find out the correlation between construction accidents and PM₁₀, and the modified PM₁₀ grade is suggested to expect accidents probability caused by PM₁₀ in the construction industry. This study is conducted by four steps. i) Establishment of the database; ii) Classification of data; iii) Analysis of the Relative Frequency of accidents in the construction industry by PM₁₀ concentration; iv) Modified PM₁₀ groups to classify the impact of PM₁₀ on accident. In terms of frequency analysis, the most accidents were occurred in the average concentration of PM₁₀ (32㎍/m³). However, we found that the relative frequency of accident was increased as the concentration of PM₁₀ increased. This means the higher PM₁₀ concentration can cause more accidents during construction. In addition, PM₁₀ concentration was divided as 6 groups by the WHO, but the modified PM₁₀ grade by the relative frequency on accident was suggested as 3 groups.

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.

Recently, interest in the precast concrete (PC) construction method has been increasing. The PC construction process consists of i) design, ii) production, iii) transportation, and iv) installation. A PC field manager at the site submits a shipment request form to the factory one to three days before the installation of the PC component. Numerous matters should be considered in writing a shipment request form. Incorrect shipment request forms may cause standby resources, waste of resources, premature work conclusion, or excessive work. These issues can lead to an increase in construction costs, replanning of PC component installation, or rework. In order to prevent such problems, PC component installation should be simulated based on the shipment request form. Accordingly, this study aims to identify factors influencing the operability of shipment request forms for PC construction. To this end, this study derived factors influencing i) initiation of the activity, ii) addition or deletion of activities, and iii) an increase or decrease in the activity execution time. As a result, this study identified flow, the features of PC components, condition of PC components, unloading location, installation location, input equipment and labor, number of anchors, number of supports, weather, strike, and accident. Further studies should verify the factors derived in this study based on focus group interviews.

Among all industries, the construction industry still remains a traditional one with low productivity due to its labor-intensive and field-dependent production system, its supplier-oriented industrial structure, and the disruption of the information flow between participants. In addition, the construction industry in South Korea has recently been required to transform itself according to social trends such as aging, the reduction of skilled workers, and the shortening of working hours, and the disaster and death rates in the industry, which are more than twice as high as those in other industries, are making it more necessary to solve chronic safety problems. Therefore, the purpose of this study is to grasp the actual condition of safety management on construction sites in South Korea and analyze cases of K-smart technology utilization for preventing safety accidents on construction sites. The study investigated and analyzed the following. First, construction sites in South Korea were analyzed by type of safety accident, by type of construction, and by construction contract amount. Second, the current status of accidents on small-sized construction sites with a high fatal accident rate and cases of safety accidents on construction sites were investigated. The results of the study are expected to contribute to the dissemination and spread of smart safety technology for not only identifying major factors in safety accidents that occur on construction sites but also preventing workers from suffering accidents.

In an attempt to disseminate modernized Korean traditional housing (Hanok), a ten-year research project was initiated in 2010 by the Korean Government to reduce the construction cost, improve the facility performance, and automate the Hanok construction industry. To meet these objectives, various research areas, including public policies, planning methods, design standards, new building materials, construction standards, maintenance procedures, advanced project management tools, and integrated IT applications have been developed. In addition, comprehensive technologies developed were applied to the ten pilot Hanok buildings to validate the real-world performance as part of the research project. To further facilitate the digital transformation of the Hanok industry by using the research results, it is required to disseminate the developed technologies in an automated and standardized manner. In particular, it is crucial to systematize and manage the interoperability of various technical data and accumulated historical data for different business functions, especially within the highly fragmented industry. In this context, this paper proposes an ontology-based Hanok information dissemination platform to enable industry-wide automated knowledge and information sharing. The system architecture, standardized historical database, and advanced analytics based on ontology web language (OWL) for the Hanok industrialization platform are introduced.

A high-performing Earned Value Management System (EVMS) can influence project success and help stakeholders meet project objectives. Although EVMS processes are well-supported by technical guidelines and standards, project managers often face challenges related to the project culture, team, resources, and business practices that make up the project environment within which an EVMS is being used. A comprehensive literature review revealed a lack of a data-driven and consistent assessment frameworks that can gauge the environment surrounding EVMS implementation. This paper will discuss the EVMS environment of construction and environmental projects, and examine its impact on cost performance. The authors used a multi-method approach to identify 27 environment factors that make up the EVMS environment, assessing them on 18 construction and environmental projects worth over $2 billion of total cost. Research methods employed include: (1) a literature review of more than 300 references; (2) a survey of 294 respondents; and (3) remote research charrettes with more than 60 participating expert practitioners. Culture (one of the identified environment categories) was found to be relatively more important in terms of its impact on the EVMS environment, followed by people, practices, and resources. These exploratory results show statistically significant differences in cost performance between completed projects with either a good or poor environment, for the sample projects. Key environment factors are outlined, and guidance is provided to practitioners around how to set up an effective EVMS environment in a construction or environmental project to inform decision-making and support achieving the project cost objectives successfully.

The project performances can be measured in terms of meeting the project schedule, budget, and conformance to functional and technical specifications. Numerous studies have been conducted to examine the causes and effects of change orders for both vertical and horizontal construction, respectively. However, these studies mainly focus on a single project type, so this paper examines the impact of change order for cost growth and schedule overruns using four different building types to close the gap in the change order research area. A total of 211 building projects are collected from four building types: healthcare, residential, office, and education. Statistical analyses using ANOVA tests and linear regression models are used to examine the created metric $CO/day on the cost and schedule impacts. The results found that mean $CO/day values were not statistically different among building types, and that the sum of change orders is a statistically significant predictor of $CO/day. The results will help project stakeholders mitigate the negative change orders effects can be a challenge for project managers and researchers alike.

Although issuing change orders is a common practice in the construction phase of any project, non-structural utility subcontractors are struggling and seek to find a way to reduce change orders. Therefore, this paper presents the analysis results on change orders to cultivate possible suggestions and solutions on how to reduce or minimize change orders in mechanical, electrical, and plumbing (MEP) works. Change orders in non-structural utility works are analyzed based on six categories such as rerouting and change of location, changes in weight, rejected design by Office of Statewide Health Planning and Development, District Structural Engineer, or the Structural Engineer of Record, unforeseen conditions, changed equipment, and owner-initiated change. The analysis findings showed that rerouting and changing location is the most significant cause, followed by unforeseen conditions. The results not only contribute to the existing body of knowledge on change order research area, but also help MEP contractors reduce the time and cost of change orders.

The resource constrained scheduling problem (RCSP) constitutes one of the most challenging problems in Project Management, as it combines multiple parameters, contradicting objectives (project completion within certain deadlines, resource allocation within resource availability margins and with reduced fluctuations), strict constraints (precedence constraints between activities), while its complexity grows with the increase in the number of activities being executed. Due to the large solution space size, this work investigates the application of Genetic Algorithms to approximate the optimal resource alolocation and obtain optimal trade-offs between different project goals. This analysis uses the cost of exceeding the daily resource availability, the cost from the day-by-day resource movement in and out of the site and the cost for using resources day-by-day, to form the objective cost function. The model is applied in different case studies: 1 project consisting of 10 activities, 4 repetitive projects consisting of 40 activities in total and 16 repetitive projects consisting of 160 activities in total, in order to evaluate the effectiveness of the algorithm in different-size solution spaces and under alternative optimization criteria by examining the quality of the solution and the required computational time. The case studies 2 & 3 have been developed by building upon the recurrence of the unit/sub-project (10 activities), meaning that the initial problem is multiplied four and sixteen times respectively. The evaluation results indicate that the proposed model can efficiently provide reliable solutions with respect to the individual goals assigned in every case study regardless of the project scale.

Recently, the Korean government has been actively promoting the smart city as their strategic agenda. However, to build smart cities that are greener, the authors believe it is essential to rapidly transit conventional energy sources such as fossil fuels to renewable energy. Although there is a big potential for Offshore wind in Korea, there has not been a full-scale commercial offshore wind farm until today. Since Korea is relatively a new market compared to the EU, it can be risky for developers. The authors will introduce risk management best practices in the offshore wind industry applicable to the Korean environment. This paper will mainly introduce an offshore wind project size of 99 MW. The project is expecting a Finance Close (FC) in Q3 2022, so the project team has prepared a risk register with over 150 risks and levers throughout the project lifecycle. Overall risks include risks with Development Expenditure (DEVEX) impact, a Capital Expenditure (CAPEX) impact, and an Operating Expenditure (OPEX) impact. Based on the identified risks, a more qualitative assessment of Cost and Schedule Impact was conducted. In conclusion, a Monte Carlo simulation was performed to propose a quantitative risk assessment to evaluate a benchmark contingency of the project cost.

Defects are the risk factors in the construction process of buildings. They cause damage, delaying the construction duration. They especially cause adverse effects on the top-down construction method. This study analyzed the degree of construction delay induced by each work type, focusing on defects in the top-down method. Then, we derived construction delay induction coefficient from different work types in order by using the severity of construction delay per defect and the occurrence probability of defect; this assessment model measures the impact of defects on construction delay for each work type. Furthermore, by comparing each work type based on the defect frequency and the construction delay induction coefficient, we found work types that need to be administered attentively. We identified that plastering work was easy to overlook, requiring caution in defect management. This study provides an efficient defect management system suitable for the buildings that are built using the top-down construction method.

Although stochastic programming and feedback control approaches could efficiently mitigate the overdue risks caused by inherent uncertainties in ground conditions, the lack of formal representations of planners' rationales for resource allocation still prevents planners from applying these approaches due to the inability to consider comprehensive resource allocation policies for hard rock tunnel projects. To overcome the limitations, the authors developed an ontology that represents the project duration estimation rationales, considering the impacts of ground conditions, excavation methods, project states, resources (i.e., given equipment fleet), and resource allocation policies (RAPs). This ontology consists of 5 main classes with 22 subclasses. It enables planners to explicitly and comprehensively represent the necessary information to rapidly and consistently estimate the excavation durations during construction. 10 rule sets (i.e., policies) are considered and categorized into two types: non-progress-related and progress-related policies. In order to provide simplified information about the remaining durations of phases for progress-related policies, the ontology also represents encoding principles. The estimation of excavation schedules is carried out based on a hypothetical example considering two types of policies. The estimation results reveal the feasibility, potential for flexibility, and comprehensiveness of the developed ontology. Further research to improve the duration estimation methodology is warranted.

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

Civil projects are associated with many uncertainties because they involve a long duration, many resources, a large area, and many supply chains. Therefore, the price of a civil project is not simply proportional to the quantity and unit price of the item but has a variable value, including uncertainty risk. This study investigates the influence of the uncertainty factors in the pre-bid clarification document on bid price formation during the project bidding phase. To this end, civil projects from the California Department of Transportation (Caltrans) were used as research data. This study randomly selected fifty sample data from each of twelve counties from 2008-to 2020: six hundred. The authors observed that each project sample had 0 to n query cases due to uncertainty. Then, this study examined the project uncertainty cases and categorized them into the following four uncertainty factors: 'conflict' (UF1), 'impossibility' (UF2), 'lack' (UF3), and 'missing' (UF4). Under the extracting process, the cases are classified into four uncertainty factors. With the project not containing any uncertainty factors as a control group, the project containing these uncertainty factors was designated as an experimental group. After comparing the bidder's price, the experimental group's bid price was higher than the control group's. This result suggests that uncertainty factors in bid documents induce bidders to set a high bid price as a defense against uncertainty.

Risk identification for bridge projects is a knowledge-based and labor-intensive task involving several procedures and stakeholders. Presently, risk information of bridge projects is unstructured and stored in different sources and formats, hindering knowledge sharing, reuse, and automation of the risk identification process. Consequently, there is a need to develop structured and formalized risk information for bridge projects to aid effective risk identification and automation of the risk management processes to ensure project success. This study proposes a semantic risk breakdown structure (SRBS) to support risk identification for bridge projects. SRBS is a searchable hierarchical risk breakdown structure (RBS) developed with python programming language based on a semantic modeling approach. The proposed SRBS for risk identification of bridge projects consists of a 4-level tree structure with 11 categories of risks and 116 potential risks associated with bridge projects. The contributions of this paper are threefold. Firstly, this study fills the gap in knowledge by presenting a formalized risk breakdown structure that could enhance the risk identification of bridge projects. Secondly, the proposed SRBS can assist in the creation of a risk database to support the automation of the risk identification process for bridge projects to reduce manual efforts. Lastly, the proposed SRBS can be used as a risk ontology that could aid the development of an artificial intelligence-based integrated risk management system for construction projects.

Existing web-based cost databases have proved invaluable for construction cost estimating. These databases have been utilized to compute approximate cost estimates using assembly rates, unit rates, and etc. These web-based databases can be used independently with traditional cost estimation methods (manual methods) or used to support BIM-based cost estimating platforms. However, these databases are rigid, costly, and require a lot of manual inputs to reflect recent trends in prices or prices relative to a construction project's location. To address this gap, this study integrated deep learning techniques with web-based price analysis to develop a database that incorporates a project's location cost estimating standards and current cost trends in generating a cost estimate. The proposed method was tested in a case study project in Lagos, Nigeria. A cost estimate was successfully generated. Comparison of the experimental results with results using current industry standards showed that the proposed method achieved a 98.16% accuracy. The results showed that the proposed method was successful in generating approximate cost estimates irrespective of project's location.

In road work zones, pedestrian workers' habituated inattention to warning alarms from construction vehicles can lead to fatal accidents. Previous studies have theorized that human factors such as personality traits may affect workers' inattentiveness to workplace hazards. However, there has been no study that directly examined how road construction workers' personality traits affect their attention to warning alarms within a work zone and the likelihood of engagement in a struck-by accident. This study examines how workers' sensation-seeking (especially boredom susceptibility) is related to inattention to warning alarms while performing a task in road work zones. An experiment with actual road construction workers was conducted using a virtual road construction environment. Workers' attention to repeatedly presented warning alarms was measured using eye-tracking sensors. In response to workers' frequent inattentive behaviors, a virtual accident was simulated. Results revealed a significant association between boredom susceptibility and workers' engagement in the virtual accident, a consequence of inattentiveness to warning alarms. The findings suggest that workers' personality traits predispose them to tune out warning alarms and become vulnerable to accidents in road work zones. The findings of this study can be used to develop targeted interventions aimed at preventing workers' inattention to repeatedly exposed workplace hazards, thereby contributing to reducing fatal accidents in road work zones.

The robustness of a supply chain (i.e., the ability to cope with external and internal disruptions and disturbances) becomes more critical in ensuring the success of a construction project because the supply chain of today's construction project includes more and diverse suppliers. Previous studies indicate that topological features of the supply chain critically affect its robustness, but there is still a great challenge in characterizing and quantifying the impact of network topological features on its robustness. In this context, this study aims to identify network measures that characterize topological features of the supply chain and evaluate their impact on the robustness of the supply chain. Network centrality measures that are commonly used in assessing topological features in social network analysis are identified. Their validity in capturing the impact on the robustness of the supply chain was evaluated through an experiment using randomly generated networks and their simulations. Among those network centrality measures, the PageRank centrality and its standard deviation are found to have the strongest association with the robustness of the network, with a positive correlation coefficient of 0.6 at the node level and 0.74 at the network level. The findings in this study allows for the evaluation of the supply chain network's robustness based only on its topological design, thereby enabling practitioners to better design a robust supply chain and easily identify vulnerable links in their supply chains.

Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

Residential heating systems in South Korea are largely based on the use of ondol pipelines. Heat is transferred to the floor by passing hot water through a metal or plastic pipe buried within the concrete of the floor. Consequently, it is difficult to clean the inside of these pipes after installation. Over time, foreign substances such as scale accumulate in the pipe when the ondol heating method is used for an extended period. Therefore, in the past, pipes were cleaned by removing foreign substances attached to the inside surfaces of the pipes using high-pressure water or by disassembling the pipes and removing foreign substances with chemical agents. Recently, a method for removing foreign substances through the cavitation effect of ultrasound has been proposed. This idea might lead to the development of new technologies for cleaning pipe interiors. Consequently, this study investigated the use of ultrasound to clean pipes embedded in concrete. In this study, devices that generated ultrasonic waves with various frequencies and directions were prepared. After preparing arbitrarily contaminated pipes, the appropriate frequency, output strength, and output direction for each foreign substance were determined through repeated experiments. The results of this experiment could provide important information for future methods of cleaning the interior of ondol piping systems.

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

Robot-based layout automation has been recently promoted for the purpose of improving productivity and quality. Marking robots have various functional demands to secure marking precision and environmental adaptability. In particular, in order to automate marking work of building structure, correction of the marking line through position recognition of rebars placed is required. Because the rebars must maintain a constant cover thickness from the formwork surface, if the rebars are out of planned position, the rebar or marking line need to be corrected to secure the cover thickness. Thus, the marking robot for structural work needs to have the function for determining the position correction of the rebar or the marking line. In order to judge the correction of marking line, it is required to measure the distance between the planned marking line and the rebar placed. Therefore, this study proposes an algorithm that can measure the distance between the planned line and the rebar, and correct marking line for the automatic operation of the marking robot. The results of this study will be utilized as a core function for unmanned operation of the marking robot and contribute to securing precise marking by reflecting construction errors.

Robots for construction sites, although not deeply widespread, are finding applications in the duties of project monitoring, material movement, documentation, security, and simple repetitive construction-related tasks. A significant shortcoming in the use of robots is the complexity involved in programming and re-programming an automation routine. Robotic programming is not an expected skill set of the traditional construction industry professional. Therefore, this research seeks to deliver a low-cost approach toward re-programming that does not involve a programmer's skill set. The researchers in this study examined an approach toward programming a terrestrial-based drone so that it follows a taped path. By doing so, if an alternative path is required, programmers would not be needed to re-program any part of the automated routine. Changing the path of the drone simply requires removing the tape and placing a different path - ideally simplifying the process and quickly allowing practitioners to implement a new automated routine. Python programming scripts were used with a DJI Robomaster EP Core drone, and a terrain navigation assessment was conducted. The study examined the pass/fail rates for a series of trial run over different terrains. The analysis of this data along with video recording for each trial run allowed the researchers to conclude that the accuracy of the tape follow technique was predictable on each of the terrain surfaces. The accuracy and predictability inform a non-coding construction practitioner of the optimal placement of the taped path. This paper further presents limitations and suggestions for some possible extended research options for this study.

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

Industrial robotic arms are widely adopted in numerous industries for manufacturing automation under factory settings, which eliminates the limitations of manual labor and provides significant productivity and quality benefits. The U.S. modular construction industry, despite having similar controlled factory environments, still heavily relies on manual labor. Thus, this study investigates the U.S., Canada, and Europe-based leading modular construction companies and research labs implementing industrial robotic arms for manufacturing automation. The investigation mainly considered the current research scope, industry state, and constraints, as well as identifying the types and specifications of the robotic arms in use. First, the study investigated well-recognized modular building associations, the Modular Building Institute (MBI), and renowned architecture design magazine, Dezeen to gather industry updates. The authors discovered one university lab and a few companies that adopted Switzerland-based robotic arms, ABB. Researching ABB robotics led to the discovery of ABB's competitor, Germany-based KUKA robotic arms. Consequently, research extended to the companies and labs adopting KUKA models. In total, this study has identified seven modular companies and four research labs. All companies employed robotic arms and gantry robot combinations in a production-line-like system for partial automation, and some adopted design standardization for optimization. The common goal among the labs was to achieve greater flexibility and full automation with robotic arms. This study will help companies better implement robotic arm automation by providing recommendations from investigating its current industry status.

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

Construction is a labor-intensive industry that heavily relies on skilled construction workers. However, the aging of the workforce is rapidly growing, and the shortage of skilled workers is intensifying. The application of construction robotics technology can solve the problem of workforce shortage and guarantee construction productivity, safety, and quality improvement. This study presents a framework of the functional analysis for construction processes and work tasks that classifies and analyzes processes and work tasks for construction robotics design. The framework presents the functional analysis process, which analyzes workers' attributes and identifies functions of construction robotics.

Safety accidents are of concern during construction projects, even given the recent innovations in digital technologies. These projects remain focused on overcoming specific and limited applications on construction sites. For this reason, the development of an inclusive safety management system has become crucial. This study aims to build a Big Data platform to inform decisions on how to proactively eliminate worker hazards on construction sites. The platform consists of about 100,000 real records and a real-time monitored database featuring various safety indices, such as workers' altitudes, heart rates, and fatigability during construction, which are determined through various wearable devices. The data types are customized and integrated by a research team in accordance with the characteristics of a specific project using hypertext transfer protocol (HTTP). The results can be helpful as efficient tools to ensure successful safety management in complex construction situations. This study is expected to provide three significant contributions to the field, including real-time fatigability analysis and tracking of workers on-site; providing early GPS-based warnings to workers who might be accessing dangerous spaces or places; and monitoring the workers' health indices, based on details from 100,000 cases.

As energy-saving techniques based on human behavior patterns have recently become an issue, the occupant-centered control system is adopted for estimating personal preference of indoor environment and optimizing environmental comfort and energy consumption. Accordingly, IoT devices have been used to collect indoor environmental quality (IEQ) data and personal data. However, the need to safely collect and manage data has been emerged due to cybersecurity issues. Therefore, this paper aims to present a framework that can safely transmit occupant-centered data collected from IoT to a private blockchain server using Hyperledger fabric. In the case study, the minimum value product of the mobile application and smartwatch application was developed to evaluate the usability of the proposed blockchain-based occupant-centered data collection framework. The results showed that the proposed framework could collect data safely and hassle-free in the daily life of occupants. In addition, the performance of the blockchain server was evaluated in terms of latency and throughput when ten people in a single office participated in the proposed data collection framework. Future works will further apply the proposed data collection framework to the building management system to automatically collect occupant data and be used in the HVAC system to reduce building energy consumption without security issues.

Agent-based modeling (ABM), as a relatively new simulation technique, has recently gained in popularity in the civil engineering domain due to its uniquely advantageous features. Among many civil engineering applications, ABM has been applied to facility operation and management, such as energy consumption management, as well as the enhancement of maintenance and repair processes. The former studies used ABM to manage energy consumption through simulating human energy-related behaviors and their interactions with facilities, as well as electrical, heating, and cooling systems and appliances, while the latter used ABM to enhance maintenance process through facilitating coordination, negotiation, and decision making between facility managers, service providers, and repair workers. The present study aims to provide a short qualitative review on the most recent applications of ABM in the above-mentioned areas. Based on the review and follow-up analysis, the study identifies the advantages, disadvantages, and limitations of ABM applications to facility operation and management, and offers several potential future research topics in the hope of filling the existing literature gaps.

Central heating, ventilation and air conditioning (HVAC) is one of the largest consumers of energy in the residential sector. This project explores the use of multiple HVAC units and/or Zoning in a single residence to reduce energy loads. The energy consumption data of a detached single-family home using two HVAC units, one primary for the main house and a secondary HVAC for a casita, was collected for the same month for two consecutive years, along with details related to the outdoor temperature and the square footage being air-conditioned by each HVAC. A regression algorithm was trained using the above details to find the relation between the parameters. Next, based on the occupancy and usage patterns, the HVAC was redesigned assuming more area under the secondary HVAC unit. The trained algorithm was then used to make energy usage predictions for the revised primary HVAC area, with the assumption that the secondary HVAC unit was turned off. The results were compared with existing energy usage data. It was determined that there were significant energy savings in the second scenario. It is expected that this study and its findings will help future research projects explore more ideas as alternatives to central HVAC, in improving the economic viability of existing options, and in developing a savings calculation tool that will help consumers make informed decisions on their best alternatives to central HVAC.

Although traditionally perceived as being a visualization and asset management resource, the relatively rapid rate of improvement of computing power, coupled with the proliferation of cloud and edge computing and the IoT has seen the expanded functionality of modern Digital Twins (DTs). These technologies, when applied to buildings, are now providing users with the ability to analyse and predict their energy consumption, implement building controls and identify faults quickly and efficiently, while preserving acceptable comfort and well-being levels. Furthermore, when these building DTs are linked together to form a community DT, entirely new and novel energy management techniques, such as demand side management, demand response, flexibility and local energy markets can be unlocked and analysed in detail, creating circularity in the economy and making ordinary building occupants active participants in the energy market. Through the EU Horizon 2020 funded TwinERGY project, three different levels of DT (consumer - building - community) are being created to support the creation of local energy markets while optimising building performance for real-time occupant preferences and requirements for their building and community. The aim of this research work is to demonstrate the development of this new, interrelated, multi-level DT that can be used as a decision-making tool, helping to determine optimal scenarios simultaneously at consumer, building and community level, while enhancing and successfully supporting the community's management plan implementation.

For a commercial building, property managers play an important role in maximizing the benefit by reducing cost and increasing revenue in the operation and maintenance phase of the building. However, most of property managers are spending their time in monitoring facility managers who have little impact on cost reduction and maximization of operating profit. The industry practitioners have difficutlty in increasing the efficiency of thier work due to this work environment. In addition, both property managers(PMr) and facility managers(FMr) are dependent on the paper drawings and manuals, which can worsen the inefficiency and human errors are inevitable. This study aims to contribute to improvement of the current practice by developing a novel algorithm that autmatically links the facility information with 3D model, which can provide an efficient property management for commercial buildings.

HVAC systems play a critical role in reducing energy consumption in buildings. Integrating occupants' thermal comfort evaluation into HVAC control strategies is believed to reduce building energy consumption while minimizing their thermal discomfort. Advanced technologies, such as visual sensors and deep learning, enable the recognition of occupants' discomfort-related actions, thus making it possible to estimate their thermal discomfort. Unfortunately, it remains unclear how accurate a deep learning-based classifier is to recognize occupants' discomfort-related actions in a working environment. Therefore, this research evaluates the classification performance of occupants' discomfort-related actions while sitting at a computer desk. To achieve this objective, this study collected RGB video data on nine college students' cold discomfort-related actions and then trained a deep learning-based classifier using the collected data. The classification results are threefold. First, the trained classifier has an average accuracy of 93.9% for classifying six cold discomfort-related actions. Second, each discomfort-related action is recognized with more than 85% accuracy. Third, classification errors are mostly observed among similar discomfort-related actions. These results indicate that using human action data will enable facility managers to estimate occupants' thermal discomfort and, in turn, adjust the operational settings of HVAC systems to improve the energy efficiency of buildings in conjunction with their thermal comfort levels.

Hurricanes and tornadoes are the most destructive natural disasters in some central and southern states. Thus, storm shelters, which can provide emergency protections for low-rise building residents, are becoming popular nowadays. Both FEMA and ICC have published a series of manuals on storm shelter design. However, the authors found that the materials for related products in the market are heavyweight and hard to deliver and install; renovations are necessary. The authors' previous studies found that lightweight and high-performance composite materials can withstand extreme wind pressure, but some building codes are designated in wind-borne debris areas. In these areas, wind debris can reach greater than 100 mph speed. In addition, the impact damage on the composite materials is an increasing safety issue in many engineering fields; some can cause catastrophic results. Therefore, studying composite structures subjected to wind debris impact is essential. The finite element models are set up using the software Abaqus 2.0 to conduct the simulations to observe the impact resistance behavior of the carbon fiber composite sandwich panels. The selected wood debris models meet the FEMA requirements. The outcome of this study is then employed in future lab tests and compared with other material models.

With climate change and the global population growth, the frequency and scope of wildfires are constantly increasing, which threatened people's lives and property. For example, according to California Department of Forestry and Fire Protection, in 2020, a total of 9,917 incidents related to wildfires were reported in California, with an estimated burned area of 4,257,863 acres, resulting in 33 fatalities and 10,488 structures damaged or destroyed. At the same time, the ongoing development of technology provides new tools to simulate and analyze the spread of wildfires. How to use new technology to reduce the losses caused by wildfire is an important research topic. A potentially feasible strategy is to simulate and analyze the spread of wildfires through computing technology to explore the impact of different factors (such as weather, terrain, etc.) on the spread of wildfires, figure out how to take preemptive/responsive measures to minimize potential losses caused by wildfires, and as a result achieve better management support of wildfires. In preparation for pursuing these goals, the authors used a powerful computing framework, Spark, developed by the Commonwealth Scientific and Industrial Research Organization (CSIRO), to study the effects of different weather factors (wind speed, wind direction, air temperature, and relative humidity) on the spread of wildfires. The test results showed that wind is a key factor in determining the spread of wildfires. A stable weather condition (stable wind and air conditions) is beneficial to limit the spread of wildfires. Joint consideration of weather factors and environmental obstacles can help limit the threat of wildfires.

The construction industry is demanding, dynamic, and complex making it difficult for workers to recognize hazards. The nature of construction tasks exposes workers to several critical risk factors, such as a high rate of exertion and fatigue. Recent studies suggest that fatigue may impact hazard recognition in the construction industry. However, most studies rely on subjective measures when assessing the relationship between physical fatigue and hazard recognition, limiting such studies' efficacy. Thus, this study examined the relationship between physical fatigue and hazard recognition using a controlled experiment. Worker fatigue levels were captured using physiological data and a subjective exertion scale. The findings confirmed that physical exertion plays a significant role in hazard recognition skills (p < 0.05). This research contributes to theory and practice by providing a process for objectively assessing the influence of physical fatigue on worker safety and providing construction professionals with some critical insight needed to improve workplace safety.

Hazard recognition is considered as one of the pre-requisites for effective hazard management and injury prevention. However, in complex and changing environments, construction workers are often unable to identify all possible hazards that can occur in the jobsite. Therefore, identification of factors that impact hazard recognition in the work environment is necessary to reduce safety incidents as well as to develop strategies that can improve worker's hazard recognition performance. This study identified factors/problems that impact worker's hazard recognition abilities and suggested some potential technologies that can mitigate such problems. Literature reviews of journal articles and published reports related to hazard recognition studies were conducted to identify the factors. The study found out that the major factor responsible for affecting worker's hazard recognition abilities were human-related. Industry factors, Organizational factors and Physical factors of the site were the other factors identified from the study that impact worker's hazard recognition performances. The findings from the study can help site personnel recognize areas where effective measures can be directed towards worksite safety of workers while working in complex construction environments.

Manual material handling is the one of the leading causes for musculoskeletal disorders (MSDs) and lower back discomfort. According to a study, construction formworkers suffer greater rates of muscular injuries and related illness due to manual activities. However, there is still a paucity of information on MSD, preventive posture issues, and corresponding solutions for construction aluminum formworkers. As a result, MSD and disregard of worker health and safety continue to exist at construction sites. Although preventive measures and strategies have been studied in previous research, we believe it is imperative to shed light on this problem through this study. This study aims to 1) implement a simple and cost-effective elevated bench to reduce MSDs, and 2) determine the rapid upper limbs assessment (RULA) and Ovako working posture analyzing system (OWAS) action catagory of workers in different postures to assess their MSD conditions and obtain an optimal position and posture using the Jack human modeling software and simulation tool. The study findings reveal a considerable reduction in MSD discomfort and which posture is acceptable in post-intervention instances.Thus results provide inexpensive and simple ergonomic interventions with favorable RULA and OWAS ratings that can be applied at construction sites. This study demonstrates workstation ergonomic intervention cases that can aid in understanding the urgency of applying existing research strategies into practice.

The novel coronavirus pandemic has had a significant impact on society and everyday life. The pandemic imposed a global shutdown leading to many challenges such as the suspension of academic programs at universities. The result of this suspension contributed to the rapid overnight migration of educational activities from traditional face-to-face learning to a virtual environment which until then was unfamiliar to both instructors and students. This study identified the experiences faced by built environment higher education instructors in KwaZulu-Natal, South Africa during this sudden switch to online teaching and learning. This pilot study employed a quantitative research approach to survey instructor experiences on online teaching and learning during a global pandemic. The data was computed and analyzed using IBM Statistical Package for Social Sciences (SPSS) version 27. Descriptive statistics were used to analyze the data collected. The study sample comprised of 20 higher education instructors in the region of the KwaZulu Natal province in South Africa. Findings from the study revealed that instructors faced adaptive challenges with rapidly having to redesign and remodel the mode of academic course delivery and assessments to suit an online platform. Additionally, instructors observed that students faced technological challenges such as connectivity and navigating the online learning management system platforms. The challenges identified by instructors and students can be effectively transformed to opportunities for future learning under the 'new normal'.

A paradigm shift in teaching modular construction in higher education and K-12 is proposed as a means to increase the future adoption of the modular construction technique. To this effect, a new education module is presented to STEM educators. This education module is based on LEGOs and directed towards educators in the architecture, engineering, and construction (AEC) industry. The main objectives of the education module are to increase interest and knowledge of modular construction, acknowledge the benefits of using 3D design with 4D scheduling, and create a simulating hands-on educational opportunity. The education module is designed to allow participants to experience a hands-on simulation of modular construction and stick-built construction through building a LEGO project. Participants are challenged to find the advantages and disadvantages in both construction systems first-hand and record their findings. Results are presented from the preliminary testing of this education model on a group of construction management students at the University of Nevada, Las Vegas. Overall, the survey results showed that the LEGO education module was successful at achieving the project's three main objectives: 1) increasing the participants' interest and knowledge of modular construction through an interactive project; 2) increasing the participants' understanding of the benefits of 3D design with 4D scheduling over the use of 2D drawings; and 3) creating a simulating hands-on educational opportunity to help participants compare modular construction to stick-built construction. In the end, this proposed a new LEGO education module addressing the problems identified from this study with more participants.

A large number of employees shifted to Work from home (WFH) due to the COVID-19 pandemic, including the construction workforce. The changes in workload and productivity due to WFH impact the work performance and economic outputs of companies. However, there are mixed results about the impacts of WFH on workload and productivity. In particular, limited studies focused on specific types of work of different occupations in the construction workforce. This study aims to explore the impacts of WFH on workload and productivity considering different types of work for the construction workforce in the U.S. After identifying three main occupations and five types of work, an online survey (N = 69) was distributed. Descriptive analysis showed that participants had less workload (0.82 hours/week) and lower productivity (9.69%) during WFH. Three occupations had varied changes due to the different types of work. Analysis of Variance (ANOVA) indicated that there was no significant difference in workload, while productivity was decreased during WFH. In particular, the productivity of project-related work and communication and documentation decreased significantly. Overall, participants finished 2.85% less workload per week during WFH. The findings provide an insight into WFH in the construction workforce, which improves future remote or hybrid work arrangements in the construction industry.

COVID-19 pandemic forces college education to be rapidly switched from face-to-face education into remote education. Two inconsistent findings exist in previous study about remote learning. First, studies before COVID-19 pandemic found remote learning is an effective method, which provided students with higher achievement and improved their work-life balance. However, studies showed remote learning during COVID-19 pandemic is not as effective as expected because of technical issues, lack of motivations and even mental health issues. Second, findings from studies about remote learning impacts on workload and productivity during COVID-19 are also inconsistent. Therefore, this study aims to quantitatively measure college students' workload and productivity during COVID-19 of different types of tasks to provide a comprehensive and latest evaluation on remote learning. The findings of this study show remote learning slightly increases college students' total listening and speaking tasks workload, total reading and writing tasks workload. Furthermore, phone call, in-person meeting, online meeting and email workload increase significantly in remote learning. However, productivity for both listening and speaking, reading and writing tasks decreases after remote learning but no significant changes of productivity are found.

Construction safety education will continue to attract the interests of construction educators, researchers, and industry professionals due to its immense influence on accident reduction and prevention. A well-educated workforce with a thorough understanding of safety requirements and procedures is needed to develop and apply effective safety and health programs as well as devise strategic means of preventing injuries, illnesses, and fatalities on construction projects. The objective of this research is to evaluate construction safety education in the curriculum of construction programs in the United States. An analysis of construction safety courses across accredited construction programs in the U.S. is conducted to synthesize important details and common themes. A nationwide characterization of the safety courses presented followed by an assessment selected a few programs as a pilot study. Critical elements of the courses such as course titles, course year, credit hours, topics covered, and alignment with professional certification or outreach training courses are characterized. Findings from the study reveal the similarities and variations that exist among safety courses taught in different construction programs in the U.S. These findings could result from several influencing factors, which could be the subject of further investigations geared toward improving safety education in construction programs.

The construction industry is considered the most hazardous industry globally. Therefore, safety education is crucial for raising the safety awareness of construction workers working at construction sites and creating a safe working environment. However, the current safety education method and tools cannot provide trainees with realistic and practical experiences that might help better safety awareness in practice. A metaverse, a real-time network of 3D virtual worlds focused on social connection, was created for more interactive communication, collaboration, and coordination between users. Several previous studies have noted that the metaverse has excellent potential for improved safety education performance, but its required functions and practical applications have not been thoroughly researched. In order to fill the research gap, this paper reviewed the potential benefits of a metaverse based on the current research and suggested its application for safety education purposes. This paper scrutinized the metaverse's key functions, particularly its information and knowledge sharing function and reality capture function. Then, the authors created a metaverse prototype based on the two key functions described above. The main contribution of this paper is reviewing the potential benefits of a metaverse for safety education. A realistic and feasible metaverse platform should be developed in future studies, and its impact on safety education should be quantitatively verified.

Accidents at construction sites in Korea account for more than half of all industrial accidents. To solve this problem, a policy to strengthen safety education was implemented to ensure the safety of workers. However, it was analyzed that there is a high possibility of accidents because workers did not receive proper safety information for each risk factor due to general lecture-style education. In addition, statistics show that the accident status of workers with fewer years of period is high, indicating that a customized information delivery method needs to be proposed for unskilled workers with fewer years of period. Research on the importance of education has been conducted, but no information delivery method has been identified. For unskilled workers to effectively receive safety information, appropriate delivery formats (text, photos, illustrations, 4D-BIM, 360-based panorama, video, animation) were analyzed, and a new method of education was proposed. If customized safety information is provided according to this proposal, effective information delivery to unskilled workers will be possible, and it is expected to be verified in various ways.

Cost overruns, schedule delays, and a shortage of skilled labor are common problems the construction industry is currently experiencing. Modularization and standardization strategies have the potential to resolve the various problems mentioned above and have been applied for various construction applications for a long time. However, the level of modularization remains low, and modular construction projects have not been getting the full benefits. Thus, this review investigated the cutting-edge technologies currently being utilized to develop the modular construction field. For this paper, qualified research papers were identified using predetermined keywords from previous related research papers. Identified literature was then filtered and analyzed. According to the included reviews, several technologies are being developed for modular construction. For example, automated design and monitoring systems for modularization were developed. In addition, research labs are utilizing robotic arms for modular construction to achieve a high level of completion in the construction industry, as is seen in the manufacturing industry. Despite these efforts, more research and development are necessary because some automation technologies still require manual activities. Thus, there is great potential for further development of modularization techniques, and further research is recommended to achieve high levels of modularization.

The U.S. Department of Energy conducts the Solar Decathlon competition as a student-based achievement that encourages sustainable design with energy efficiency and solar energy technologies. In the 2020 competition, the University of Nevada, Las Vegas (UNLV) team designed, fabricated, and constructed a net-zero modular house that applies innovative and highly efficient building technologies. This paper focused on the lessons learned during the early phases of this ongoing modular project. The research methodology included obtaining feedback from key project participants using a well-structured questionnaire. The results showed that the major items/challenges in the project's planning phase included selecting the modular size, planning the construction system, planning the materials and procurement, estimating costs and duration, selecting a fabricator, collaboration and communication, safety, and planning module transportation. These findings will help modular practitioners and future Solar Decathlon competition participants better understand how and what factors they should consider most during the early phases through the lessons learned.

The building production system has been analysed by the dichotomy "employer-contractor" relationship, which failed to take into account of the role and function of multiple stakeholders within the life-cycle supply chain. This is further observed in the current conflict resolution model, which, in my argument, struggles to contribute to industrialize the building production and achieve better efficiency and effectiveness as expected. The purpose of this paper is to critically assess the issues of current programme-based conflict resolution model, and discuss alternative models how they can be modelled and applied to the construction projects. The conclusions of findings are; First, the current model is framed around the contracts and dispute resolutions based on the legal concept of "claimant and respondent" where one party(s) advances a claim once and the other(s) objects, as such it fails to reflect the nature of construction projects where multiple stakeholders are involved concurrently and for a long period of life-cycle of buildings. Second, an alternative is "Six-stakeholders model" which represents the multiple stakeholders and clarifies the flow of obligation-liability-monetary relationships among participants for a long period of life-cycle of buildings. Further, with reference to both historical and recent cases, a reflection and insight into pros and cons of programming method is added, especially as to why this method is considered to have become a mandate of the modern construction management, and how academics and practitioners should deal with it more cautiously and prudently.

Industrialized and modular construction is a growing construction technique that can transfer a large portion of the construction process to off-site fabrication yards. This method of construction often involves the fabrication, pre-assembly, and transportation of massive and long volumetric modules. The module weight keeps increasing as the modules become more complete (with infill) to minimize the work at the site and, as higher productivity can be achieved at the fabrication shop. Thus, a volumetric module delivery gets more challenging and risky. Despite its importance, past research paid relatively insufficient attention to the problem related to the lifting of heavy modules. This can be a complex and time-consuming problem with multiple lifting for transportation-and-installation operations both in fabrication yard and jobsite, and require complex crane operations (sometimes, more than one crane) due to crane load capacity and load balance/stability. This study investigates this problem by focusing on the structural perspective of lifting such long volumetric modules through simulation studies. Various scenarios of lifting a weighty module from the top using four lifting cables attached to crane hooks (either a single crane or double crane) are simulated in SAP software. The simulations account for various factors pertaining to structural indices, e.g., bending stress and deflection, to identify a proper method of module lifting from a structural point of view. The method can identify differences in structural indices allowing identification of structural efficiency and safety levels during lifting, which further allows the selection of the number of cranes and location of lifting points.

Modularization in a high-rise building is different from a small building, as it is exposed to more lateral forces like wind and earthquakes. The integrity, robustness, and overall stability of the modules and their performance is based on the joining techniques and strong structural systems. High lateral stiff construction structures like concrete shear walls and frames, braced steel frames, and steel moment frames are used for the stability of high-rise modular buildings. Similarly, high-rise stick-built buildings have concrete cores and perimeter frames for lateral load strength and stiffness. Methods for general steel-concrete connections are available in many works of literature. However, there are few modular-related papers describing this connection system in modular buildings. This paper aims to review the various research and practice adopted for steel-to-concrete connections in construction and compare the methods between stick-built buildings and modular buildings. The literature review shows that the practice of steel module-to-concrete core connection in high-rise modular buildings is like outrigger beams-to-concrete core connection in stick-built framed buildings. This paper concludes that further studies are needed in developing proper guidelines for a steel module-to-concrete core connection system in high-rise modular buildings.

Modular building is a fast-growing construction method, mainly due to its ability to drastically reduce the amount of time it takes to construct a building and produce higher-quality buildings at a more consistent rate. However, while modular construction is relatively safer than traditional construction methods, workers are still exposed to hazards that lead to injuries and fatalities, and these hazards could be controlled using emerging smart technologies. Currently, limited information is available at the intersection of modular construction, safety risk, and smart safety technologies. This paper aims to investigate what aspects of modular construction are most dangerous for its workers, highlight specific risks in its processes, and propose ways to utilize smart technologies to mitigate these safety risks. Findings from the archival analysis of accident reports in Occupational Safety and Health Administration (OSHA) Fatality and Catastrophe Investigation Summaries indicate that 114 significant injuries were reported between 2002 and 2021, of which 67 were fatalities. About 72% of fatalities occurred during the installation phase, while 57% were caused by crushing and 85% of crash-related incidents were caused by jack failure/slippage. IoT-enabled wearable sensing devices, computer vision, smart safety harness, and Augment and Virtual Reality were identified as potential solutions for mitigating identified safety risks. The present study contributes to knowledge by identifying important safety trends, critical safety risk factors and proposing practical emerging methods for controlling these risks.

Benchmarking is an important tool to assess the performance of capital projects in the construction industry. Incorporating cost-related metrics into a benchmarking system requires an effective cost normalization process to enable meaningful comparisons among projects that were executed at different locations and times. Projects in the downstream and chemicals sector have unique characteristics compared to other types of construction projects, they require a distinctive cost normalization framework to be developed to benchmark their absolute cost performance. The purpose of this study is to develop such a framework to be used for the case of benchmarking the downstream and chemical projects for their performance assessment. The research team started with a review of existing cost normalization methodologies adopted in benchmarking systems and conducted 7 interviews to identify the current cost normalization practices used by industrial professionals. A panel of 12 experts was then convened and it held 6 review sessions to accomplish the framework development. The cost normalization framework for benchmarking downstream and chemical projects was established as a three-step procedure and it adopts a 4-element cost breakdown structure to accommodate projects submitted by both owners and contractors. It also incorporated 5 published cost indexes that are compatible with downstream and chemical projects and they were embedded into 2 options to complete the normalization process. The framework was then pilot-tested on 4 completed projects to validate its functional practicality and the downstream and chemical use case in the benchmarking system.

Recently, there has been a steady decrease in the proportion of the construction sector among Korean engineering firms. Thus it is essential for Korean engineering firms in the construction sector, which lack experience in overseas ventures, to identify and improve their competitiveness for successful overseas expansion. Therefore, in this study, changes in Korean engineering firms' capabilities for the last decade were analyzed to promote entry into overseas road and water resource engineering markets. Competency factors that require urgent improvement were derived based on Importance-Performance Analysis (IPA) as a tool for quantitative measurement. As a result, the factor that shows low performance compared to the importance is an overall understanding of the target country in the road and water resource areas. Knowledge of regulatory issues on design, the ability of time management software, and knowledge of the regulatory problems on construction safety are also insufficient. This study can be used as a research methodology to identify competitiveness that Korean engineering firms have to strengthen when they advance into overseas markets in roads, water resources, and other areas.

Construction projects are large-scale projects that require extensive construction costs and resources. Especially, scheduling is considered as one of the essential issues for project success. However, the schedule and resource management are challenging to conduct in high-tech construction projects including complex design of MEP and architectural finishing which has to be constructed within a limited workspace and duration. In order to deal with such a problem, this study suggests resource and sequence optimization using constraint programming in construction projects. The optimization model consists of two modules. The first module is the data structure of the schedule model, which consists of parameters for optimization such as labor, task, workspace, and the work interference rate. The second module is the optimization module, which is for optimizing resources and sequences based on Constraint Programming (CP) methodology. For model validation, actual data of plumbing works were collected from a construction project using a five-minute rate (FMR) method. By comparing actual data and optimized results, this study shows the possibility of reducing the duration of plumbing works in construction projects. This study shows decreased overall project duration by eliminating work interference by optimizing resources and sequences within limited workspaces.

Some regions and states, such as Wyoming, have harsh weather conditions, forcing most transportation projects to be completed under tight schedules. However, construction projects are not only delayed by weather conditions, but also delayed by other factors such as contractor's competency, resource availability, coordination issues, and safety. Also, the construction method, geographical location of the projects, and inability to follow baseline schedules accurately due to the contractor's resource allocation are also reasons for the delay. This paper discusses the main reasons for the delay in the public transportation projects based on Daily Work Reports (DWRs) from five different transportation projects of the last three years in Wyoming. This paper focuses on the inconsistencies of the contractor's schedules to complete the project according to the baseline schedule. First, the authors collected DWRs and baseline schedules from the Wyoming Department of Transportation (WYDOT). Second, the DWR data are compared against the baseline schedules to determine the reasons for delaying their significance. Finally, the paper presents the recommendations to mitigate the effects of delays on public transportation projects as well as to improve the documentation process of DWR data.

Highway Rest Areas are envisioned to provide an accessible space for rest and parking for travelers, especially those driving a long distance. In addition, modern highway Rest Areas provide many amenities to highway users, including wifi service, picnic tables, litter barrels, running water, public telephones, and sometimes even free coffee. Various studies were conducted in the domain of Rest Area facility design and their operating costs in different states; however, limited studies were conducted on the maintenance costs of these facilities. Therefore, this study's main objective is to compute the annual maintenance cost of Rest Areas in the state of Nevada. This study also analyzes the main cost categories of the maintenance works. The raw cost data of Nevada Rest Area maintenance from 1990 to 2012 were collected from the Nevada Department of Transportation (NDOT). Results show that the maintenance cost fluctuated over the study period; the maintenance cost decreased from 1991 to 2004 and then increased until 2012. The primary cost categories of maintenance work are labor, equipment, and material costs. Among these, labor cost was the largest category with 56 percent of the total maintenance cost, followed by equipment cost and material cost. The findings of this study may help NDOT and other transportation agencies plan their budget for future Rest Area maintenance activities.

The objective of this study is to analyze the productivity, efficiency, and factors affecting the productivity of a spray task from an international airport project. The study is focused on the productivity analysis of the Subcontractor whose job was to supply and apply sprayed-applied fire-resistive material (SFRM) on steel members to achieve the necessary fire ratings on the building structures of the Hamad International Airport, Qatar. The study analyzed the productivity of the four sprayer teams who completed the task at three locations and three areas of the airport. The study found that the productivity of the individual team observed during the SFRM spray task was not only different but was also observed different when they worked at varying floor heights where different factors affecting productivity were predominant. The study found that the efficiency was lowest (47.32%) when the spray team had to work at second-floor heights and factors affecting productivity such as limited accessibility for material movement and lifting, site congestion, lack of continuity of operation due to priority areas and frequent re-handling of machines and tools were present. Besides, the factors such as adverse weather conditions and sub-trades interference affected productivity at all locations. The findings show that productivity depends on multiple factors and those factors need to be identified and addressed to improve productivity. The findings also show that the estimated efficiency was hard to achieve but possible since Team 4 had 97% efficiency on the first floor of the airport.

In the United States, the Safety Rest Areas (SRAs) were introduced as highway roadside infrastructures in the early 1900s. The State Departments of Transportation (DOTs) operate/maintain their SRAs using different methods. The Washington DOT used the in-house workforce method for over 20 years, whereas some states moved to Performance-Based Contracting (PBC) from the in-house workforce to save cost primarily. Several existing studies claimed that using the PBC approach saved costs on several highway assets. Thus, the principal objective of this study is to compute and compare the unit operating/maintenance cost of SRAs using the in-house workforce method (in Washington state) with the PBC approach (in other states). The findings of this study show that the average annual cost using the PBC approach was much more than the average annual cost using the in-house workforce approach. The findings also show that in Washington state, the 'Labor Cost' category was a key expenditure, which is statistically higher than other categories. The 'Labor Cost' was followed by the 'Other Services', and then 'Materials and Supplies' and 'Equipment.' The study's findings indicated that outsourcing does not always save costs for agencies. The study findings may help transportation construction/maintenance professionals select a cost-effective approach for their future planning.

The COVID-19 pandemic has unprecedented impacts on different sectors around the globe. The effects observed in developing countries are even more severe. Some projects stopped while many have cost and time overrun issues. This paper conducted a case study on the COVID-19's effects on a hotel construction project in Nepal. The study reviewed the literature on COVID-19 and its impact on construction sectors and conducted a semi-structured interview with the project's personnel. The interview response was analysed and the contributing factors that impacted the project and its performance were identified. The paper found financial, operational, contractual, safety, and risk management issues in the hotel project. Overall, the project cost increased by 32% where the material cost increased by 35% and labor cost increased by 28%. This research discusses causes, measures, and provides a broad perspective of the problems, significant challenges, and opportunities associated with the effects of COVID-19 on the construction industry. The Owner as well as the Contractors incurred added costs because of COVID-19. The paper identified contributing factors and presented the challenges which could be used as opportunities to minimize unforeseen impacts of the pandemics in near future. The lesson learned from this case study was that the labor cost and materials cost could have been minimized if the Owner and the Contractor had established alternative resources such as using locally available labor, materials, and alternative suppliers.

Various lean design and construction methods such as target value design, pull planning, value stream mapping have successfully transformed the commercial building construction industry into achieving improved productivity, higher design and construction quality, and meeting the target values of construction projects. Considering the significant advantages of lean, the accelerated dissemination and adoption of lean methods and tools for construction is highly desirable. Currently, the lean design and construction body of knowledge is imparted primarily through publications and conferences. However, one of the most effective ways to impart this soft knowledge is through getting students and trainees involved in hands-on participatory games, which can quickly help them truly understand the concept and apply it to real-world problems. The COVID-19 Pandemic has raised an urgent need of developing virtual games that can be played simultaneously from various locations over the Internet, but these virtual games should be as effective as in-person games. This research develops an online 3D simulation game for Target Value Design that is as effective as in-person games or possibly better in terms of knowledge capture and retention and enjoyable environment and experience. The virtual game is tested on volunteers using feedback from pre-and post- simulation surveys to evaluate its efficacy.

In this paper, different types of traditional project delivery methods in the construction industry were explored and a comparative analysis against Integrated Project Delivery (IPD) were performed. The advantages of IPD method for all parties, owner/engineer/architect/general contractor, were explored by reviewing the most recent literature. The literature suggests that IPD method should be the dominating project delivery method and diluting the conventional methods such as Design-Bid-Build due to more collaborative and mutually beneficial ways of doing construction; IPD is newer and a more comprehensive method to capture the intrinsic values of project collaboration. This paper presents a comparison of the commonly used methods of project delivery, Design-bid-build, CMAR, & Design-Build and addresses their advantages and disadvantages in differing project scopes and sizes. Several industry leaders with experience in the four types of project delivery addressed were surveyed. The survey results show an overwhelming desire for future projects to go toward IPD from the contractor/owner/RDP. The biggest obstacle facing a project from using IPD appears to be trust.

Prefabrication is a construction technique that is increasingly being applied to different building components due to its many benefits, including higher quality and lower waste. Despite these advantages, there are challenges in execution of these components on projects, due to transportation logistics, skilled labor requirements, and project management techniques. This paper investigates the current landscape of prefabricated pipe spools and potential solutions for minimizing these challenges. The scope of this research includes a proposed workflow, to standardize implementation of these components. Semi-structured interviews were conducted with industry professionals to assess current industry practices and the validity of the proposed workflow. Findings of this paper indicate that greater integration between design, fabrication and transportation is required to minimize inefficiencies when implementing prefabricated pipe spools on projects.

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.

This paper introduces a study of concrete structures with a broken tire and a flat tire as a complete overhaul. The materials used to make concrete in this study are solid aggregate, cement, sand, flat tire, broken wheel, drinking water, and Ordinary Portland Cement. A total of 6 main compounds were thrown into solid cylinders and replaced by 0% as a controller followed by 5% and 10%. The cylinder pressure test of the concrete is done by applying the same pressure to the cylinders until a failure occurs. The results of the pressure test show that by applying 5% aggregation the pressure decreases. In Crumb wheel joints, the compression force decreases constantly as the percentage change increases. Therefore, the crumb wheel is not recommended for use as a complete replacement due to its compressive church power.

Roadway lighting is an integral element of a highway system. Luminaires on roadways provide viewing conditions for drivers and pedestrians during nighttime in order to improve safety. It is time-consuming and labor-intensive to manually measure roadway illuminance at predetermined spots with a handheld illuminance meter. To improve the efficiency of illuminance measurement, a remote-control electrical cart and a drone were utilized to carry an illuminance meter for the measurements. The measurements were performed on the marked grid points along the pavement. To measure the illuminance manually, one person measures illuminance at each grid point with the handheld meter and another person records the illuminance value. To measure the illuminance with the remote-control cart, the illuminance meter is attached to the cart and it measures illuminance values continuously as the cart moves along the grid lines. With the drone, the meter records the illuminance continuously as the drone carries the meter and flies along the grid line. Because the drone can fly at different heights, the measurements can be done at different altitudes. The illuminance measurements using the cart and the drone are described in detail and compared with manual measurements in this paper. It is shown through this study that automated measurements can greatly improve the efficiency of roadway illuminance data measurements.

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

This paper introduces alert fatigue and presents methods to measure alert fatigue by using physiological signals. Alert fatigue is a phenomenon that which an individual is constantly exposed to frequent alarms and becomes desensitized to them. Blind spots are one leading cause of struck-by accidents, which is one most common causes of fatal accidents on construction sites. To reduce such accidents, construction equipment is equipped with an alarm system. However, the frequent alarm is inevitable due to the dynamic nature of construction sites and the situation can lead to alert fatigue. This paper introduces alert fatigue and proposes methods to use physiological signals such as electroencephalography, electrodermal activity, and event-related potential for the measurement of alert fatigue. Specifically, this paper presents how raw data from the physiological sensors measuring such signals can be processed to measure alert fatigue. By comparing the processed physiological data to behavioral data, validity of the measurement is tested. Using preliminary experimental results, this paper validates that physiological signals can be useful to measure alert fatigue. The findings of this study can contribute to investigating alert fatigue, which will lead to lowering the struck-by accidents caused by blind spots.

Current MEP designs are mostly applied by 2D-based design methods and tend to focus on simple modeling or geometry information expression such as converting 2D-written drawings into 3D modeling without taking advantage of the strength of BIM application. To increase the demand for BIM-based MEP design, geometric information, and property information of each member of the 3D model must be conveniently linked from the phase of the Design Development (DD) to the phase of Construction Document (CD). To conveniently implement a detailed model at each phase, the detailed level of each member of the 3D model must be specific, and an automatic generation of objects at each phase and automatic detailing module for each LOD are required. However, South Korea's guidelines have comprehensive standards for the degree of MEP modeling details for each design phase, and the application of each design phase is ambiguous. Furthermore, in practice, detailed levels of each phase are input manually. Therefore, this paper summarized the detailed standards of MEP modeling for each design phase through interviews with MEP design companies and related literature research. In addition, items that enable auto-detailing with DYNAMO were selected using the checklist for each design phase, and the types of detailed methods were presented. Auto-detailing items considering the detailed level of each phase were classified by members. If a DYNAMO algorithm is produced that automates selected auto-detailing items in this paper, the time and costs required for modeling construction will be reduced, and the demand for MEP design will increase.

Since Mechanical, Electrical, and Plumbing(MEP) routing is a repetitive and experience-centered process that requires considerable time and human resources, if automated, design errors can be prevented and the previously required time and human resources can be reduced. Although research on automatic routing has been conducted in many industries, the MEP routing in AEC projects has yet to be identified due to the complexity of system configuration, distributed expertise, and various constraints. Therefore, the purpose of this study is to identify the target subjects for MEP routing automation and the constraints of each subject. The MEP design checklist provided by a CM company and existing literature review were conducted, and target subjects and constraints were identified through process observation and in-depth expert interviews for five days by visiting a MEP design company. The target subjects were largely divided into six categories: air conditioning plumbing, air conditioning duct, restroom sanitary plumbing, heating plumbing, and diagram. The findings from interviews show that work reduction and error reduction has the greatest effect on air conditioning plumbing while the level of difficulty is the highest in air conditioning duct and restroom sanitary plumbing. Major constraints for each subject include preventing cold drafts on air conditioning pipes, deviation in ventilation volume in air conditioning ducts, routing order on restroom sanitary plumbing, and separation distance from the wall on heating plumbing. In this way, subjects and constraints identified in this study can be used for MEP automatic routing.

Tack coat is a thin layer of asphalt between the existing pavement and asphalt overlay. During construction, insufficient tack coat layering can later cause surface defects such as slippage, shoving, and rutting. This paper proposed a method for tack coat inspection improvement using an unmanned aerial vehicle (UAV) and deep learning neural network for automatic non-uniform assessment of the applied tack coat area. In this method, the drone-captured images are exploited for assessment using a combination of Mask R-CNN and Grey Level Co-occurrence Matrix (GLCM). Mask R-CNN is utilized to detect the tack coat region and segment the region of interest from the surroundings. GLCM is used to analyze the texture of the segmented region and measure the uniformity and non-uniformity of the tack coat on the existing pavements. The results of the field experiment showed both the intersection over union of Mask R-CNN and the non-uniformity measured by GLCM were promising with respect to their accuracy. The proposed method is automatic and cost-efficient, which would be of value to state Departments of Transportation for better management of their work in pavement construction and rehabilitation.

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

This study aims to review the use of graph databases in construction research. Based on the diagnosis of the current research status, a future research direction is proposed. The use of graph databases in construction research has been increasing because of the efficiency in expressing complex relations between entities in construction big data. However, no study has been conducted to review systematically the status quo of graph databases. This study analyzes 42 papers in total that deployed a graph model and graph database in construction research, both quantitatively and qualitatively. A keyword analysis, topic modeling, and qualitative content analysis were conducted. The review identified the research topics, types of data sources that compose a graph, and the graph database application methods and algorithms. Although the current research is still in a nascent stage, the graph database research has great potential to develop into an advanced stage, fused with artificial intelligence (AI) in the future, based on the active usage trends this study revealed.

This study presents the problems of data imbalance, varying difficulties across target objects, and small objects in construction object segmentation for far-field monitoring and utilize compound loss functions to address it. Construction site scenes of assembling scaffolds were analyzed to test the effectiveness of compound loss functions for five construction object classes---workers, hardhats, harnesses, straps, hooks. The challenging problem was mitigated by employing a focal and Jaccard loss terms in the original loss function of LinkNet segmentation model. The findings indicates the importance of the loss function design for model performance on construction site scenes for far-field monitoring.

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

Traditionally, the construction industry has shown low labor productivity and productivity growth. To improve labor productivity, it must first be accurately measured. The existing method uses work-sampling techniques through observation of workers' activities at certain time intervals on site. However, a disadvantage of this method is that the results may differ depending on the observer's judgment and may be inaccurate in the case of a large number of missed scenarios. Therefore, this study proposes a model to automate labor productivity measurement by monitoring workers' actions using a deep learning-based pose estimation method. The results are expected to contribute to productivity improvement on construction sites.

The aim of this study is to develop a Named Entity Recognition (NER) model to automatically identify construction-related organizations from news articles. This study collected news articles using web crawling technique and construction-related organizations were labeled within a total of 1,000 news articles. The Bidirectional Encoder Representations from Transformers (BERT) model was used to recognize clients, constructors, consultants, engineers, and others. As a pilot experiment of this study, the best average F1 score of NER was 0.692. The result of this study is expected to contribute to the establishment of international business strategies by collecting timely information and analyzing it automatically.

Environmental stressors considerably influence the health and safety of humans and must thus be continuously monitored to enhance the urban environments and associated safety. Environmental stressors typically act as stimuli and lead to behavioral changes that can be easily identified. These behavioral responses can thus be used as indicators to clarify people's perceptions of environmental stressors. Therefore, in this study, a framework for assessing environmental stressors based on human behavioral responses was developed. A preliminary experiment was conducted to investigate the feasibility of the framework. Human behavioral and physiological data were collected using wearable sensors, and a survey was performed to determine the psychological responses. Humans were noted to consistently exhibit changes in the movement and speed in the presence of physical environmental stressors, as physiological and psychological responses. The results demonstrated the potential of using behavioral responses as indicators of the human perceptions toward environmental stressors. The proposed framework can be used for urban environment monitoring to enhance the quality and safety.

Unsatisfactory urban walking environment stresses urban residents, and may cause mental illness and chronic diseases by reducing walking activities. Therefore, establishing a high-quality walking environment that can promote walking activities in urban residents has emerged as an important issue. The walking environment consists of various components, such as trees, stairs, streetlights, benches, signs, fences, and facilities, and it is essential to understand which components and their settings act as satisfiers or dissatisfiers for pedestrians, to create a better quality walking environment. Therefore, this study investigated pedestrian satisfaction and dissatisfaction as a function of various environmental components through a survey using walking environment images. The results revealed that most of the walking environment components except the braille block and treezone exhibited significant correlations with pedestrian satisfaction. Particularly, safety-related component (e.g., adjacent roads, parked cars, traffic cushions, and car separation), and landscape-related components (e.g., trees and green), as well as the material settings of landscape facilities (e.g., wooden fences, benches, stairs, and walkway surfaces) correlated with pedestrian satisfaction. The results of this study can contribute to the extraction of useful features to evaluate pedestrian satisfaction as a function of the walking environment. The research outcome is expected to assist in the effective arrangement of walking environment components and their settings, which will ultimately contribute to significantly satisfactory walking environment and encourage walking activities.

In the construction industry, fatal accidents related to scaffolds frequently occur. To prevent such accidents, scaffolds should be carefully monitored for their safety status. However, manual observation of scaffolds is time-consuming and labor-intensive. This paper proposes a method that automatically analyzes the installation status of scaffold joints based on images acquired from a Unmanned Aerial Vehicle (UAV). Using a deep learning-based object detection algorithm (YOLOv5), scaffold joints and joint components are detected. Based on the detection result, a two-stage rule-based classifier is used to analyze the joint installation status. Experimental results show that joints can be classified as safe or unsafe with 98.2 % and 85.7 % F1-scores, respectively. These results indicate that the proposed method can effectively analyze the joint installation status in UAV-acquired scaffold images.

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

In construction projects, it is common to exchange documents using email because of convenience. In this study, a method extracting and organizing block information automatically based on email was developed. This method is composed of document exchange and archiving processes, which are difficult to manage and vulnerable to loss. Therefore, this study aims to develop a solution that can automatically link email and block information. The block data components are designed to derive from email exchange and user-additional input information. Also, automatically generating blocks process including extraction and conversion of information was proposed. This solution can lead to promote the convenience of project document management in terms of identifying the document flow and preventing loss of information.

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

Occlusion is one of the most challenging problems for computer vision-based construction monitoring. Due to the intrinsic dynamics of construction scenes, vision-based technologies inevitably suffer from occlusions. Previous researchers have proposed the occlusion handling methods by leveraging the prior information from the sequential images. However, these methods cannot be employed for construction object detection in non-sequential images. As an alternative occlusion handling method, this study proposes a data augmentation-based framework that can enhance the detection performance under occlusions. The proposed approach is specially designed for rebar occlusions, the distinctive type of occlusions frequently happen during construction worker detection. In the proposed method, the artificial rebars are synthetically generated to emulate possible rebar occlusions in construction sites. In this regard, the proposed method enables the model to train a variety of occluded images, thereby improving the detection performance without requiring sequential information. The effectiveness of the proposed method is validated by showing that the proposed method outperforms the baseline model without augmentation. The outcomes demonstrate the great potential of the data augmentation techniques for occlusion handling that can be readily applied to typical object detectors without changing their model architecture.

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

Wildfire disasters in the United States impact lives and livelihoods by destroying private homes, businesses, community facilities, and infrastructure. Disaster victims suffer from damaged houses, inadequate shelters, inoperable civil infrastructure, and homelessness coupled with long-term recovery and reconstruction processes. Cities and their neighboring communities require an enormous commitment for a full recovery for as long as disaster recovery processes last. State, county, and municipal governments inherently have the responsibility to establish and provide governance and public services for the benefit and well being of community members. Municipal governments' comprehensive and emergency response plans are the artifacts of planning efforts that guide accomplishing those duties. Typically these plans include preparation and response to natural disasters, including wildfires. The standard wildfire planning includes and outlines (1) a wildfire hazard assessment, (2) response approaches to prevent human injury and minimize damage to physical property, and (3) near- and long-term recovery and reconstruction efforts. There is often a high level of detail in the assessment section, but the level of detail and specificity significantly lessons to general approaches in the long-term recovery subsection. This paper aims to document the extent of wildfire preparedness at the county level in general, focusing on the long-term recovery subsections of municipal plans. Based on the identified challenges, the researchers provide recommendations for better longer-term recovery and reconstruction opportunities: 1) building permit requirements, 2) exploration of the use of modular construction, 3) address through relief from legislative requirements, and 4) early, simple, funding, and the aid application process.

In the U.S., the state, local, tribe, and territorial governments seek funding from the federal government through the Public Assistance program to carry out these recovery works. In this paper historic public assistance data between 1998 and 2021 have been analyzed to derive several insights such as: types of disasters causing the most damage, states requiring more support, net present value of the federal expense etc. This paper has found that the states requiring more support from the federal government are not always the states suffering the maximum losses from the disasters. It has also found that the net present value of the federal expense between 1998 and 2020 to restore, repair, reconstruct, or replace disaster damaged roads and bridges across the U.S. is $15 billion in 2021 values. Moreover, this paper has tested the correlation between the states' public assistance funds requirements and the existing condition and performance of roads and bridges as revealed by the American Society of Civil Engineer's infrastructure grade card. It has found a weak correlation between these two. The outcomes of this paper can be used by the decision makers to analyze the viability of any possible alternative to the exiting public assistance program. The insights can also help in better decision making in pre-disaster preparation and post-disaster funds allocation.

Electric vehicles (EVs) have been growing to reduce energy consumption and greenhouse gas (GHG) emissions in the transportation sector. The increasing number of EVs requires adequate recharging infrastructure, and at the same time, adopts low- or zero-emission electricity production because the GHG emissions are highly dependent on primary sources of electricity production. Although previous research has studied solar photovoltaic (PV) -integrated EV charging stations, it is challenging to optimize spatial areas between where the charging stations are required and where the renewable energy sources (i.e., solar photovoltaic (PV)) are accessible. Therefore, the primary objective of this research is to support decisions of siting EV charging stations using a spatial data clustering method integrated with Geographic Information System (GIS). This research explores spatial relationships of PV power outputs (i.e., supply) and traffic flow (i.e., demand) and tests a community in the state of Indiana, USA for optimal sitting of EV charging stations. Under the assumption that EV charging stations should be placed where the potential electricity production and traffic flow are high to match supply and demand, this research identified three areas for installing EV charging stations powered by rooftop PV in the study area. The proposed strategies will drive the transition of existing energy infrastructure into decentralized power systems. This research will ultimately contribute to enhancing economic efficiency and environmental sustainability by enabling significant reductions in electricity distribution loss and GHG emissions driven by transportation energy.

The major objective of this research is to evaluate performance of improved intersections for response time to emergency vehicle preemption. Smart technologies have been introduced to civil infrastructure systems for resilient communities. The technologies need to evaluate their effectiveness and feasibility to confirm their introduction. This research focuses on the performance of emergency vehicle preemption, represented by response time, when smart intersections are introduced in a community. The response time is determined by not only intersections but also a number of factors such as traffic, distance, road conditions, and incident types. However, the evaluation of emergency response has often ignored factors related to emergency vehicle routes. In this respect, this research synthetically analyzes geospatial and incident data using each route of emergency vehicle and conducts before-and-after evaluations. The changes in performance are analyzed by the impact of smart intersections on response time through Bayesian regression models. The result provides measures of the project's performance. This study will contribute to the body of knowledge on modeling the impacts of technology application and integrating heterogeneous data sets. It will provide a way to confirm and prove the effectiveness of introducing smart technologies to our communities.

Active construction work zones will result in longer travel time and/or longer travel distances for road users because of reduced speed limits and/or detours. This results in increased fuel consumption and increased emissions of harmful gases such as Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Sulfur Oxides (SOx), which causes discomfort to the environment and road users around the work zone. The impact of such emissions should be considered while designing work zones or determining the number of days the roadway will be allowed to be closed partially or fully. This study develops a methodology to compute additional road user costs associated with such work zones. To achieve this goal, a) an extensive literature review is conducted, b) a framework to compute emission cost is developed, c) emission rates are computed for all counties (95) of the state of Tennessee, and d) a case study is conducted to demonstrate the use of the framework to estimate the additional impact of emission because of the work zone. For the case study conducted, the emission cost was computed to be $10,653.60 for the duration of the project. State DOTs can account for such road user costs while selecting contractors using A+B bidding. Accounting for such impact of emission will also indicate the agency's willingness to consider sustainability as a part of the business practices.

Mobility is an essential human need. Human survival and societal interaction depend on the ability to move people and goods. Efficient mobility systems are essential facilitators of economic development. Cities could not exist and global trade could not occur without systems to transport people and goods cheaply and efficiently. Rail has been considered as one of the important components of the transportation infrastructure required to service and improve the performance and productivity of an economy. In Nigeria, the rail infrastructure built by the colonial master several decades ago has been left in a state of total deterioration. This long neglect was occasioned by the failure of the government to pay adequate attention to infrastructure development. There is a vital and urgent need for rail infrastructure development in Nigeria. This study presents a systematic review of the evolution of rail, the current nature of railway infrastructure delivery in Nigeria, and offers possible suggestions on how to achieve an effective and sustainable rail infrastructure delivery in Nigeria. A thorough literature search of academic databases was conducted on current research trends on the subject of railway infrastructure by systematically reviewing selected published articles from reputable research domains. The analysis of the selected articles revealed the following among others (1) the existing railway infrastructure is in a state of mess and not sustainable, and (2), Government's investment/commitment in rail infrastructure seems inadequate compared to what is obtainable in other developed countries. Rail infrastructure development cannot be left to the Federal government of Nigeria to solve on its own; collaboration and participation are required. Government as a matter of priority should devote considerable attention to the development of rail infrastructure to harness the economic potential and transformation that sustainable rail infrastructural projects will provide.

It is no news that Nigeria's infrastructure challenge is enormous. In the global ranking, Nigeria ranked low in quantity and quality of its infrastructural provision which has a great impact on the ease of business transaction. Low investments in transportation have brought about the current infrastructural deficit. Recently, the Nigerian government has made effort to address at least to some extent the infrastructural deficit through Public-Private Partnership, but this has not yielded the desired result. Moreover, the sustainability issues relating to railway projects such as, emissions, noise pollution, ecosystem, and other environmental issues calls for urgent attention. Hence, this necessitated consideration on sustainability appraisal for the Chinese rail project in Nigeria. This study reviews sustainability of railway projects built by the Chinese firm in Nigeria with particular emphasis on the environmental and social impact of these projects. The study further identified issues and challenges in project implementation with a particular focus on civil dialogue and community engagements. A detailed literature search was conducted on railway projects and infrastructure by systematically reviewing selected published articles.The analysis of the selected articles identified sustainability issues and potential for improvement of Chinese railway projects and how they contribute to or inhibit competitiveness in the Nigerian railway market. From the literature searched, some of the projects constructed by Chinese firm revealed that there is economic and social impact of railway projects delivered by the Chinese firm in terms of capacity development and knowledge transfer potentiality. For instance, in the just concluded Lagos-Ibadan railway projects, the study gathered that the project brought about 5000 jobs and local staff were trained by the Chinese company, this will boost man power and local content capability. Also, it will significantly improve Nigeria's infrastructure and boost its economic development. The study suggests that Nigerian government should ensure and provide an enabling environment that is conducive for investment on the continent. Peace, improved security, and decent governance are the best conditions for sustainable transportation growth.

This paper presents an innovative way of integrating scheduling and project controls with the environmental impact of a construction project to track, monitor, and manage environmental emissions at the activity level. As a starting point, scheduling and project controls help monitor the status of a project to provide an assessment of the duration and sequence of activities. Additionally, project schedules can also reflect resource allocation and costs associated with various phases of a construction project. Owners, contractors and construction managers closely monitor tasks or activities on the critical path(s) and/or longest path(s) calculated through network based scheduling techniques. However, existing industry practices do not take into account environmental impact associated with each activity during the life cycle of a project. Although the environmental impact of a project may be tracked in various ways, that tracking is not tied to the project schedule and, as such, generally is not updated when schedules are revised. In this research, a Cradle to Gate approach is used to estimate environmental emissions associated with each activity of a sample project schedule. The research group has also investigated the potential determination of scenarios of lowest environmental emissions, just as project managers currently determine scenarios with lowest cost or time. This methodology can be scaled up for future work to develop a library of unit emissions associated with commonly used construction materials and equipment. This will be helpful for project owners, contractors, and construction managers to monitor, manage, and reduce the carbon footprint associated with various projects.

The use of the sustainability rating systems in infrastructure construction projects is not as common in comparison to building construction projects. While the sustainability rating systems share some commonalities, they differ from one another in certain ways. Thus, project teams cannot make reliable decisions when choosing the best sustainability rating tools for a given infrastructure projects. The Department of Transportation (DOT) in several states are developing its own rating system to address the infrastructure sustainability, but not in the case of California. Therefore, this paper presents the statistical results on the important sustainability determinants that affects the success of meeting sustainability goals of infrastructure construction projects. The authors conducted an online survey using the structured questionnaires. The categories considered include site, water/wastewater, energy, materials/resources, environmental, and others. The statistical analyses such as Kruskal-Wallis and ANOVA are conducted using a total of 25 valid and complete data out of 59 surveys collected. The results demonstrate several factors under each of six major sustainable categories have received higher ranks than other factors. The results also show that a statistically significant difference can be found from water, energy, and environmental categories against the other category based on the pairwise comparisons.

Heritage buildings (HBs) as structures with historical and architectural relevance that form an integral part of contemporary society. HBs deserve to be protected for as long as possible to retain their significance. Therefore, prioritizing HB maintenance management (HBMM) is pertinent. However, the decision-making process of HBMM can be relatively daunting. The decision-making challenge may be attributed to the multiple 'stakeholders' expectation and requirement which needs to be met. To this end, professionals in the built environment have identified the need to apply the strategic concept of facilities management (FM) in decision making. However, studies exploring the application of FM in decision-making seem lacking. To bridge this gap, this study focuses on developing a framework for strategic decision-making HBMM, which helps achieve HBMM sustainability. At the study's inception, relevant works of literature in the domains of HBMM and FM were conducted. This review helped identify contemporary maintenance practices and their applicability to HBMM. Afterward, a conceptual framework to aid decision-making in HBMM was developed. This framework integrated the concept of FM scope (people, place, process, and technology) while ensuring that decisions and plans were made at strategic, tactical, and operational levels. The conceptual framework presents a holistic guide for professionals in HBMM to ensure that decision processes and outcomes are practical and efficient. It also contributes to the existing body of knowledge on the integration of FM in HBMM. Furthermore, it will help achieve HB sustainability through an effective decision-making process.

As a part of the Smart Cities Mission, the Government of India in 2015 embarked upon the development of 100 existing cities as smart cities. In this study, the authors selected Ahmedabad city as the smart city development in India and presented project-level elements of the city based on the secondary data availability. At first, the authors focused on peer-reviewed articles, policy documents, and technical reports. Next, the authors collected the secondary data of project-level elements of the Ahmedabad city from the years 2015 to 2019. The findings show no significant improvement in the sewage system and waste collection as compared to the level of investment made in these sectors. The study showed that the water supply system outperformed revenue generation based on the government investment made in that sector. As a lesson learned, these findings indicate that significant improvement should be addressed in sewage management and waste collection. These study findings could help government officials, investors, developers, and city planners in making the appropriate decision before and during smart city execution. The lesson learned from this study could be used as a reference to improve revenue during the future smart city implication.

As environmental concerns grow, people are becoming more aware of energy efficiency, carbon reduction, and sustainable development. Leadership in Energy and Environmental Design (LEED) certification is currently the most widely recognized building environment assessment method connected to energy and the environment worldwide. This study explores trends for six factors (energy and atmosphere, materials and resources, indoor environmental quality, sustainable sites, water efficiency, and innovation in design) to assess four levels of LEED certification (Platinum, gold, silver, and certified) using 11,209 LEED projects in the United States. The study analyzes trends using scores of percentages of maximum points by certification level, ownership type, space type, and climate zones. With the interest in the Environmental, Social, and Governance (ESG) principle on the rise, this study contributes to a better understanding of the trends and future of LEED certification in the built environment sector.

Past research shows that the construction of new infrastructure accelerates economic growth in the region by attracting more people and commodities. However, the previous studies only considered large-scale infrastructures such as sea-cross bridges and channel tunnels. There is a paucity of literature on regional infrastructure and its impact on socio-economic indicators. This paper explores the impact of new bridge construction on the human population, particularly focusing on regional bridges constructed during the 2000s in the state of Georgia. The human population at a county level was selected as a single socio-economic factor to be evaluated. A total of 124 cases were investigated as to whether the emergence of a new bridge affected the population change. The interrupted time series analysis was used to statistically examine the significance of population change due to the construction by treating each new bridge as an intervention event. The results show that, out of the 124 cases, the population of 67 cases significantly increased after the bridge construction, while the population of 57 cases was not affected by the construction at a significance level of 0.05. The 124 cases were also analyzed by route type, functional class, and traffic volume, but the results revealed, unlike large-scale infrastructure, that no clear evidence was found that a new bridge would bring an increase in the human population at a county level.

In addressing the goal for sustainability in the construction industry, the very materials used for construction and the methods utilized to implement said materials must be analyzed. Specifically, some traditional residential construction materials consist of wood, steel, and concrete. Because these materials vary in their levels of sustainability, there is a need to develop and explore new or other materials that can be used for residential construction. The primary purpose of this paper is to provide a review of interlocking earthen masonry units (IEMU) as an alternative option for residential building construction. This is in an effort to explore the variables impacting their existing and potential applications as sustainable materials and a method for residential building construction. IEMU's are then examined under the triple bottom line (TBL) sustainability framework which includes analyzing the environmental, economic, and social sustainability of IEMU's. The findings of this review may lead to further progression in the development of a framework for evaluating U.S. stakeholder adoption of IEMU's and potential implementation in U.S. residential construction.

Evaluating infrastructure's impact on social equity is an emerging area of research in transportation construction engineering. Transportation agencies have been trying to include sustainable development. The three components of sustainable development are environmental protection, social equity, and economic development. Although social equity is one of the essential components of sustainable development, most transportation agencies do not consider this component. The research publications in this area are limited. The principal objective of this study is to synthesize existing studies related to the impact of transportation infrastructures on social equity. This study will also identify social equity indicators, the correlation between social equity and transportation infrastructures and their services, and the impact of transportation infrastructures' on social equity. In addition, this study will identify current issues of social equity and will provide some recommendations. This synthesis study revealed that transportation infrastructures impacted social equity in various ways. Some effects are positive, such as new job creation on the market. Other effects are adverse, such as diminishing socio-economic and environmental degradation. Studies also showed that the current practices evaluated infrastructures' impact on a case-by-case basis. The authors recommend adopting a multi-disciplinary holistic for assessing infrastructure's effects on social equity. The multi-disciplinary fields of study include civil engineers, construction engineers/managers, public policy researchers, environmentalists, and social scientists.

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.

Information sharing is the main purpose of realizing interoperability between the application domains of Geographic Information System (GIS) and Building Information Modeling (BIM). This paper presents and describes the workflow of BIM-GIS interoperability for highway traffic information sharing. An innovative and automatic Dynamo process was presented to transfer the shapes and attributes of the shapefile from GIS to BIM. On the basis of the transformed BIM model, the detailed traffic data was added and expressed in the form of families and sheets to expand traffic information. Then, the shapes of the model were swept as solid geometries in the BIM environment applying Dynamo. The expanded BIM model was transferred back to the GIS system using the Industry Foundation Classes (IFC) scheme. The mutual communication between BIM and GIS was achieved based on Dynamo and IFC. This paper provides a convenient and feasible way to realize BIM-GIS interoperability for highway traffic information sharing according to the characteristics of highways in terms of graphic expression and model creation.

In 2020, it was estimated that more than 2.4 million households in South Korea are over 30 years old. That is, more than 40% of all houses in Korea are old and that they require proper rehabilitation. The two options to improve poor living conditions are reconstruction and remodeling. Compared to reconstruction, remodeling has advantages in terms of the construction period, cost, and environmental impact. As such, the current Korean regulations are more favorable for remodeling than reconstruction. Typically, several candidate floor plans are presented in the early stages of an apartment remodeling project. Extracting information about bearing walls and other structural elements from the multiple plans to compare those plans quantitatively is one of the essential tasks during the early stage of a project. To cope with this task, an automated data extraction method for walls and slabs from before and after remodeling plans is developed. Through the developed program, load-bearing walls, non-bearing walls, slabs, and weight changes after remodeling can be analyzed and visualized in a fast and automated manner.

An architect creates his/her design to meet owner's requirements. Floor plans, perspective drawings, and scale models are used in order for the owner to choose the design. The tools are a little helpful for communication between the architect and the owner in case the owner does not know architecture. The scale models are good, but it is hard to make scale models while design is in progress. 3D CAD is a good tool for communication, but it is time-consuming and requires high-performance computer hardware. Augmented reality is able to show 3D virtual models that are updated by the architect with smart devices such as a smart phone and a tablet PC. The owner frequently reviews the updated design anytime anywhere. This study proposes a method to use augmented reality for architectural design and construction management. The method supports the communication between the owner, the architect and the contractor to review updated designs, and to complete the building project. 3D models expressed in augmented reality are created using SketchUp with 2D drawings for building construction. An Android application implementing augmented reality is developed by Qualcomm Vuforia and Unity on smart devices. Drawings as markers and 3D models are connected in Unity. And functions that temporarily hide unnecessary parts can be implemented in C# programming language. If an owner, an architect, or a contractor looks at a smart phone on a 2D drawing, he/she can identify building elements such as 3D buildings or columns on a screen. This can help communication between them.

The use of Hi-Tech in cultural heritage preservation and the promotion of cultural heritage values in general, particularly artifacts, opens new opportunities for attracting tourists while also posing a challenge due to the need to reward high-quality excursions to visitors historical and cultural values. Building Information Modeling (BIM) and Hi-Tech in new building management have been widely adopted in the construction industry; however, Historic Building Information Modeling (HBIM) is an exciting challenge in 3D modeling and building management. For those reasons, the Scan-to-HBIM approach involves generating an HBIM model for existing buildings from the point cloud data collected by Terrestrial 3D Laser Scanner integrated with Virtual Reality (VR), Augmented Reality (AR), contributes to spatial historic sites simulation for virtual experiences. Therefore, this study aims to (1) generate the application of Virtual Reality, Augmented Reality to Historic Building Information Modeling - based workflows in a case study which is a monument in the city; (2) evaluate the application of these technologies to improve awareness of visitors related to the promotion of historical values by surveying the experience before and after using this application. The findings shed light on the barriers that prevent users from utilizing technologies and problem-solving solutions. According to the survey results, after experiencing virtual tours through applications and video explanations, participant's perception of the case study improved. When combined with emerging Hi-Tech and immersive interactive games, the Historic Building Information Modeling helps increase information transmission to improve visitor awareness and promote heritage values.

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.

The number of buildings is increasing day by day. The next logical footstep is tackling challenges regarding scarcity of resources and sustainability, as well as shifting focus on existing building structures to renovate and retrofit. Many existing old and heritage buildings lack documentation, such as building models, despite their necessity. Technological advances allow us to use virtual reality, augmented reality, and mixed reality on mobile platforms in various aspects of the construction industry. For these purposes, having a BIM model or high detail 3D model is not always necessary, as a simpler model can serve the purpose within many mobile platforms. This paper streamlines a framework for generating a lightweight 3D model for mobile platforms. In doing so, we use an existing structure's site survey data for the foundation data, followed by mobile VR implementation. This research conducted a pilot study on an existing building. The study provides a process of swiftly generating a lightweight 3D model of a building with relative accuracy and cost savings.

Denoising, registering, and detecting changes of 3D digital map are generally conducted by skilled technicians, which leads to inefficiency and the intervention of individual judgment. The manual post-processing for analyzing 3D point cloud data of construction sites requires a long time and sufficient resources. This study develops automation technology for analyzing 3D point cloud data for construction sites. Scanned data are automatically denoised, and the denoised data are stored in a specific storage. The stored data set is automatically registrated when the data set to be registrated is prepared. In addition, regions with non-homogeneous densities will be converted into homogeneous data. The change detection function is developed to automatically analyze the degree of terrain change occurred between time series data.

Cost segregation helps reduce tax liabilities by reclassifying real property to personal property and accelerates tax depreciation of a property. A typical cost segregation study requires much time and high costs. This study proposed a BIM integrated cost segregation process that can be applied to any commercial building project. The proposed BIM-based cost segregation process was verified in a new commercial construction project. It approved that this approach can: (1) increase the cash flow for the owner and provide assistance to tax-paying enterprises; (2) enable the contractor to use it as an added value in the bidding process; (3) realize data sharing in a common platform to improve the cost segregation study efficiency and reduce costs and errors; (4) contribute to the asset management in the life cycle of buildings while filling in the blank of cost segregation process. Future studies will focus on the automation of cost segregation and asset management in building construction's life cycle.

Construction laborers and crews play a critical role in achieving a safe and productive construction site. Many past research studies used top-down approaches/perspectives for studying the impact of laborers' performance on overall construction site outputs with limited flexibility in accounting for laborers' various characteristics. However, the recent reap in computational advances allowed applications of bottom-up architectures, which can potentially incorporate heterogeneous characteristics of laborers' individual behavioral and decision-making features effectively. Accordingly, agent-based modeling (ABM), as a tool to leverage a bottom-up methodological approach, has been widely adopted by recent research. Existing literature investigated the influence of changes in laborers' behaviors and interactions on either construction sites' safety performance or productivity performance individually, leaving the tradeoff between safety and productivity in this context relatively unexplored. Accordingly, this study aims to develop an agent-based framework to study the tradeoff between project safety and productivity performances resulting from changes in laborers' behaviors after attending safety trainings. Our findings via simulations indicate that proper safety trainings can improve safety performance without negatively impacting productivity performance.

The concept of Digital Twin (DT) has been receiving an increasing amount of attention in the construction management and building engineering research domains. Although the benefits of DT are evident, confusion with regards to the concept of DTs and its relationship with others such as Cyber-Physical Systems (CPS), Building Information Modelling (BIM) and Internet of Things (IoT) remains. This paper aims to help allay this confusion through an in-depth analysis of the definition of DT and its unique characteristics. As such, a review of the past and current definitions of DT and CPS in various domains is performed. An analysis is then conducted to identify the overlaps between the definition of DT with CPS, as well as with BIM and IoT. Finally, given the relatively closer resemblances between DT and CPS, a set of four distinct dimensions enabling their comparative analysis and highlighting their shared and unique characteristics is discussed. This paper contributes to the existing literature by exploring the definition of DT and presenting two original conceptualizations that help further refine the concept of DT in the construction and management and building engineering domain.

The Industry Foundation Classes (IFC) provides standardized product models for the building construction domain. However, the current IFC schema has limited representation for infrastructure. Several studies have examined the data schema for road and highway modeling, but not in a sufficiently comprehensive and robust manner to facilitate the overall integrated project delivery of road projects. Several discussions have focused on slope engineering for road projects, but no solution has been provided regarding the formalized parametric modeling up to now. Iterative design, analysis, and modification are observed during the process of slope design for road projects. The practitioners need to carry out the stability analysis to consider different road design alternatives, including horizontal, vertical, and cross-section designs. The procedure is neither formalized nor automated. Thus, there is a need to develop the formal representation of the product and process of slope analysis for road design. The objective of this research is to develop a formal representation (i.e., an IFC extension data schema) for slope analysis. It consists of comprehensive information required for slope analysis in a structured manner. The deliverable of this study contributes to both the formal representation of infrastructure development and, further, the automated process of slope design for road projects.

While pursuing digitalization and paperless projects, the construction industry needs to settle on how to make the most of digitized data and information. On-site workers, who currently rely on paper documents to check and review design and construction plans, will need alternative ways to efficiently access the information without using any paper. Augmented Reality is a potential solution where the information customized to a user is aligned with the physical world. This paper proposes the Augmented Reality framework to deliver the information on on-site resources (e.g., workers and equipment) using head-mounted devices. The proposed framework was developed by interoperating Augmented Reality-supported devices and a digital twin platform in which all information related to ongoing tasks is accumulated in real-time. On-site resources appearing in the user's field of view are automatically detected by an object detection algorithm and then assigned to the corresponding information by matching the data in the digital twin platform. Preliminary experiments show the feasibility of the proposed framework. Worker detection results can be visualized on HoloLens 2 in near real-time, and the matching process obtained the accuracy greater than 88%.

As the population is aging, accidents involving elderly people are also increasing (2014:11,667 persons; 2018: 11,797 persons). In the case of the elderly population, falling accidents are the primary direct or indirect causes of death; in particular, they face an elevated risk of staircase falls. This study proposes a method of evaluating the safety of staircases using Building Information Modeling (BIM)-based virtual simulation. By making a virtual user with the behavioral characteristics of the elderly respond to a staircase in a BIM model, its safety performance can be evaluated. The evaluation criteria were derived from regulations, elements, and characteristics relevant to the safety of staircases. To validate the proposed method, safety evaluation tests were simulated on actual staircases. The evaluation result of the test simulation shows the safety scores of 1.97 points for the elderly user and 2.95 points for the average male adult user against a required safety score of a minimum of 2 points. That is, safety is relative to users as the safety of the same staircase can be different depending upon the different behavioral characteristics of users. The study suggests that the risk of staircase-related fall accidents to the elderly can be reduced by improving staircase designs through the proposed method.

Building Information Modelling (BIM) has evolved into a comprehensive, collaborative method in construction project delivery. Most industrialized and developed countries have made BIM mandatory in the government and public projects, whereas developing countries are embracing and catching up BIM technologies to improve their professional's abilities and reduce claims in the construction projects. However, BIM awareness and professional's competence have become critical in implementing BIM in infrastructure projects in Nepal, particularly hydropower projects. The objectives of this study are to find the BIM awareness among hydropower professionals in Nepal and assess their response. The study used a questionnaire survey to assess the awareness. The results showed that only few professionals (12 percentage in this study) are aware of BIM and its application in Hydropower infrastructures. Majority of the respondents (more than 50%) were interested in BIM trainings and believed BIM implementation in future projects. The study indicated that lack of BIM training and lack of BIM awareness were the top factors affecting BIM implementation in hydropower projects in Nepal. The findings showed that about 66 percent of the respondents who used BIM in their projects mainly used during construction phase. More than 80 percent believed that BIM should be mandated for the hydropower projects in Nepal. The findings presented in this study could promote awareness among different professionals, organization, and construction team and encourage BIM implementation in Hydropower projects. The findings could raise awareness of BIM in Nepal's infrastructure sectors and its invaluable benefits in construction.

As buildings become more and more complicated, the importance of developing and managing facility management information is increasing. In a building fire situation, various information is generated and needed to be quickly shared among participants. However, the current fire response system fails to monitor the relevant information in a real time basis. This study aims to develop a system prototype for fire protection management which can quickly and accurately manage and effectively deliver the pertinent information to the target participants. The research contributes to the efficiency of fire protection endeavors by interpreting both dynamic and static fire information in an appropriate manner.

Indoor POI data, an essential component of indoor spatial data, has attribute information of a specific place in the room and is the most critical information necessary for the user. Currently, indoor POI data is manually updated by direct investigation, which is expensive and time-consuming. Recently, research on updating POI using the attribute information of indoor photographs has been advanced to overcome these problems. However, the range of use, such as using only photographs with text information, is limited. Therefore, in this study, and to improvement this, I proposed a new method to update indoor POI data using a smartphone camera. In the proposed method, the POI name is obtained by classifying the indoor scene's photograph into artificial intelligence technology CNN and matching the location criteria to indoor spatial data through AR technology. As a result of creating and experimenting with a prototype application to evaluate the proposed method, it was possible to update POI data that reflects the real world with high accuracy. Therefore, the results of this study can be used as a complement or substitute for the existing methodologies that have been advanced mainly by direct research.

Building Information Modeling (BIM) is widely used to efficiently share, utilize and manage information generated in every phase of a construction project. Recently, mixed reality (MR) technologies have been introduced to more effectively utilize BIM elements. This study deals with the haptic interactions between humans and BIM elements in MR to improve BIM usability. As the first step in interacting with virtual objects in mixed reality, we challenged moving a virtual object to the desired location using finger-pointing. This paper presents the procedure of developing a haptic interface system where users can interact with a BIM object to move it to the desired location in MR. The interface system consists of an MR-based head-mounted display (HMD) and a mobile application developed using Unity 3D. This study defined two segments to compute the scale factor and rotation angle of the virtual object to be moved. As a result of testing a cuboid, the user can successfully move it to the desired location. The developed MR-based haptic interface can be used for aligning BIM elements overlaid in the real world at the construction site.

A construction site is a highly complex and constantly changing environment, where hazardous areas are difficult to detect if workers lack sufficient knowledge and awareness. Thus, frequent worker safety training is required. Numerous studies on using virtual reality (VR) for safety training were published. While they demonstrate the potential for improving the skills necessary to avoid accidents in the construction industry, they remain difficult to apply at actual construction sites. VR requires specialized hardware and software, limiting workers' access and restricting workers' participation in training sessions. As a result, this paper proposes multiple platforms for immersive virtual reality safety training (VRMP) based on Industry Foundation Classes (IFC) and web technologies such as immersive web (WebXR). The VRMP is compatible with mobile and desktop devices currently by workers and demonstrates scenario models familiar to workers. Also, it reduces development time by utilizing Building Information Models (BIM). Additionally, The VRMP collects data from workers in a virtual environment to assess each worker's safety level, assisting workers in effectively and comfortably gaining a better understanding and raising their awareness. This paper develops a case study based on the VRPM in order to assess its effectiveness.

The existing literature has witnessed the importance of productivity assessment and deducing factors affecting it. However, yet many models have shown limitations in practical applications in actual construction sites for process planning due to uncertainty and lack of data. This research presents a productivity assessment and database generation framework using simulation and compares the results with RSMeans to derive appropriate equipment combinations alternatives for earthwork operations. Data of 15 different conditions was collected from 5 different construction sites. Prediction accuracy above 90% were achieved for the simulation models with average error rate of 7.4%. The construction productivity assessment framework presented in this study is expected to be highly applicable to operation planning for earthwork operations.

Techniques based on computer vision are becoming increasingly important in construction safety monitoring. Using AI algorithms can automatically identify conceivable hazards and give feedback to stakeholders. However, the construction site remains various potential hazard situations during the project. Due to the site complexity, many visual devices simultaneously participate in the monitoring process. Therefore, it challenges developing and operating corresponding AI detection algorithms. Safety information resulting from computer vision needs to organize before delivering it to safety managers. This study proposes a framework for computer vision-aided construction safety monitoring using collaborative 4D BIM information to address this issue, called CSM4D. The suggested framework consists of two-module: (1) collaborative BIM information extraction module (CBIE) extracts the spatial-temporal information and potential hazard scenario of a specific activity; through that, Computer Vision-aid Safety Monitoring Module (CVSM) can apply accurate algorithms at the right workplace during the project. The proposed framework is expected to aid safety monitoring using computer vision and 4D BIM.

The majority of construction site incidents occur due to a lack of hazard awareness among workers on job sites. This lack of awareness is despite mandatory construction safety training, typically in the form of written content (safety manuals) or of images depicting hazards. To reduce job-site injuries and fatalities, general contractors have started adopting Virtual Reality (VR) to impart safety training to job site personnel. VR safety training can take the form of an immersive simulation comprising potential safety hazards intentionally embedded into a virtual job site; users are required to identify these hazards within a specified time frame with the expectation that they will be more adept at recognizing hazards on an actual job-site, resulting in fewer accidents. This research study seeks to identify the actual impacts of VR on construction safety awareness among participants. The research addresses the following question: Does VR improve hazard recognition awareness? The primary objective is to evaluate participants' performance of past construction safety awareness against present construction safety awareness after receiving VR training. Participants were asked to complete a multiple-choice Qualtrics™ questionnaire. The results of the study showed a statistically significant knowledge gain advantage with respect to hazard recognition and construction safety awareness with the use of interactive, immersive VR over a more conventional and passive safety training method.

Constructionarium Ltd is a not-for-profit organisation which delivers a residential, experiential, immersive learning opportunity to university students from across the built environment education sector. Since 2002, the Constructionarium education model has been available to students in engineering, construction management and architecture at a purpose built, 19-acre multi-disciplinary training facility in Bircham Newton, England simulating real site life and reflecting site processes, practices and health and safety requirements. The unique approach of Constructionarium puts experiential learning and sustainability at the heart of everything. In a week, students develop a practical understanding of the construction process, develop transferable skills, build a team and are exposed to the latest in sustainable technologies. Experiential learning is what differentiates a Constructionarium project from regular field trips or site visits. At Constructionarium the focus is on learning by participation rather than learning through theory or watching a demonstration. The projects cannot be replicated in a classroom or on campus. Using the hands-on construction of scaled down versions of iconic structures from around the world, students learn that it requires the involvement of the whole construction team to successfully complete their project. Skills such as communication, planning, budgeting, time management and decision making are woven into a week-long interrelationship with industry professionals, academic mentors and trades workers. Working together to enhance transferable skills brings the educational environment into the reality of completing an actual construction project handled by the students. Constructionarium has used this transformational learning model to educate thousands of students from all over the United Kingdom, Europe and Asia. Texas A&M University in the United States has sent multiple teams of students from its Department of Construction Science every operational year since 2016.

Studying abroad in and of itself should be a unique and transformational learning experience for university students. Too often, "study abroad" is a code word for "faculty vacation" or "easy credit hours". For an international learning experience to be truly transformational it must offer an intense and directed program that maximizes the time the student spends in the accumulation of information that is new or different from what the student has "learned" previously. "Study abroad" may be a misnomer because it is not only about studying in another country or culture, that is, taking courses that usually have an attendance time of a few hours a week, but it is also about living in another country which becomes a 24/7 learning experience. Providing these programs during the Covid-19 pandemic has been a keen opportunity for institutional learning. When this immersion in foreign culture is combined with academic rigor applied to a student's chosen field of study the growth can be exponential. So, what is the relationship between academic and personal growth? The National Association for Study Abroad has found that "students who have studied abroad are better able to work with people from other countries, understand the complexity of global issues, and have greater intercultural learning. One study found that students returned from their study abroad experiences more tolerant and less fearful of other countries, but with a greater sense of nationalism-a phenomenon they called 'enlightened nationalism'." It is often said that "you only really learn to appreciate things that are important to you when they are gone, when you miss them." The international learning environment can provide this opportunity. The restrictions on various societies in the past two years due to the international Covid pandemic have provided existing study abroad programs with a true testing ground for the validity of their programs. At the end of the day, American colleges and universities are not helpless in the face of these developments. A lot depends on how a university positions itself for a future based on the uncertainties of the past. As Winston Churchill was working to form the United Nations after WWII, he famously said, "Never let a good crisis go to waste". In another context, Churchill's insight on human nature can also be applied to the coming semesters and years as studying abroad rebounds. What new strategies will be developed and maintained? Institutional commitment without fear will be necessary to assure that "studying abroad" will continue to develop as a truly unique and transformational learning experience.

Because students majoring in construction-related fields must develop a broad repository of knowledge and skills, effective transferal of these is the primary focus of most academic programs. While inculcation of this body of knowledge is certainly critical, actual construction projects are complicated ventures that involve levels of risk and uncertainty, such as resistant neighboring communities, unforeseen weather conditions, escalating material costs, labor shortages and strikes, accidents on jobsites, challenges with emerging forms of technology, etc. Learning how to develop a level of discernment about potential ways to handle such uncertainty often takes years of costly trial-and-error in the proverbial "school of hard knocks." There is therefore a need to proactively expedite the development of a sharpened intuition when making decisions. The AGC Education and Research Foundation case study committee was formed to address this need. Since its inception in 2011, 14 freely downloadable case studies have thus far been jointly developed by an academics and industry practitioners to help educators elicit varied responses from students about potential ways to respond when facing an actual project dilemma. AGC case studies are typically designed to focus on a particular concern and topics have thus far included: ethics, site logistics planning, financial management, prefabrication and modularization, safety, lean practices, preconstruction planning, subcontractor management, collaborative teamwork, sustainable construction, mobile technology, and building information modeling (BIM). This session will include an overview of the history and intent of the AGC case study program, as well as lively interactive demonstrations and discussions on how case studies can be used both by educators within a typical academic setting, as well as by industry practitioners seeking a novel tool for their in-house training programs.

Construction projects are fraught with risks from cost or other overruns to accidents along with other issues. This is true whether the relevant organization is an owner, general contractor, CM, specialty-trade contractor, or other entity. When cost issues or other issues confront arise, how should an organization proceed whether attempting to gain additional compensation in terms of cost/other damages or protecting the same against such claims if they do not appear to be warranted? Enter construction cost forensics. This presentation will focus on strategies/techniques with construction cost forensics in these areas in order to be successful. Covered techniques include those to develop and analyze claims including fundamental construction cost analysis techniques. When an unexpected event disrupts a construction project, using sound analytical methods to identify the cause and quantify the extent of the issue will be important for negotiating a fair result or for obtaining a successful outcome in arbitration or litigation. Key examples of uncovering issues via construction cost forensics will be covered in this presentation.

In the current construction industry, difficulty arises in creating an adequate baseline schedule to establish a fundamental plan for construction. This presentation will present the research findings which investigated industry-recognized schedule metrics that aid in the successful implementation of said schedule. Industry organizations (Association for the Advancement of Cost Engineering, the Government Accountability Office, the Project Management Institute, and local city, state, and county government offices) provide standardized guidelines with specific metrics requirements to ensure successful implementation. However, most of those metrics are substantiated or validated in their effectiveness. The study examined the impact between these industry-recognized critical metrics and three distinct scheduling characteristics: Project Type, Project Duration, and Project Density (number of activities within a schedule). The research results showed that, among the 12 various schedules evaluated in parallel with 20 industry-recognized critical metrics, seven metrics substantially demonstrate a significant impact on a project schedule's success. Furthermore, six of the seven metrics directly correlate to at least one of the three scheduling characteristics outlined. As a result, this research has established more predictable outcomes based on impacts between three distinct project characteristics and 20 of the most discussed/researched critical scheduling metrics in the field. This allows management teams to have more confidence in establishing critical milestones and accurate turnover dates from the start of the project through its final completion.

Annually, several construction workers fall ill, are injured, or die due to heat-related exposure. The prevalence of work-related heat illness may rise and become an issue for workers operating in temperate climates, given the increase in frequency and intensity of heatwaves in the US. An increase in temperature negatively impacts physical exertion levels and mental state, thereby increasing the potential of accidents on the job site. To reduce the impact of heat stress on workers, it is critical to develop and implement measures for monitoring physical exertion levels and mental state in hot conditions. For this, limited studies have evaluated the utility of wearable biosensors in measuring physical exertion and mental workload in hot conditions. In addition, most studies focus solely on male participants, with little to no reference to female workers who may be exposed to greater heat stress risk. Therefore, this study aims to develop a process for objective and continuous assessment of worker physical exertion and mental workload using wearable biosensors. Physiological data were collected from eight (four male and four female) participants performing a simulated drilling task at 92oF and about 50% humidity level. After removing signal artifacts from the data using multiple filtering processes, the data was compared to a perceived muscle exertion scale and mental workload scale. Results indicate that biosensors' features can effectively detect the change in worker physical and mental state in hot conditions. Therefore, wearable biosensors provide a feasible and effective opportunity to continuously assess worker physical exertion and mental workload.

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

Walkability is an indicator of how much pedestrians are willing to walk and how well a walking environment is created. As walking can promote pedestrians' mental and physical health, there has been increasing focus on improving walkability in different ways. Thus, plenty of research has been undertaken to measure walkability. When measuring walkability, there are many objective and subjective variables. Subjective variables include a feeling of safety, pleasure, or comfort, which can significantly affect perceived walkability. However, these subjective factors are difficult to measure by making the walkability index more reliant on objective and physical factors. Because many subjective variables are associated with human emotional states, understanding pedestrians' emotional states provides an opportunity to measure the subjective walkability variables more quantitatively. Pedestrians' emotions can be examined through surveys, but there are social and economic difficulties involved when conducting surveys. Recently, an increasing number of studies have employed physiological data to measure pedestrians' stress responses when navigating unpleasant environmental barriers on their walking paths. However, studies investigating the emotional states of pedestrians in the walking environment, including assessing their positive emotions felt, such as pleasure, have rarely been conducted. Using wearable devices, this study examined the various emotional states of pedestrians affected by the walking environment. Specifically, this study aimed to demonstrate the feasibility of monitoring biometric data, such as electrodermal activity (EDA) and heart rate variability (HRV), using wearable devices as an indicator of pedestrians' emotional states-both pleasant-unpleasant and aroused-relaxed states. To this end, various walking environments with different characteristics were set up to collect and analyze the pedestrians' biometric data. Subsequently, the subjects wearing the wearable devices were allowed to walk on the experimental paths as usual. After the experiment, the valence (i.e., pleasant or unpleasant) and arousal (i.e., activated or relaxed) scale of the pedestrians was identified through a bipolar dimension survey. The survey results were compared with many potentially relevant EDA and HRV signal features. The research results revealed the potential for physiological responses to indicate the pedestrians' emotional states, but further investigation is warranted. The research results were expected to provide a method to measure the subjective factors of walkability by measuring emotions and monitoring pedestrians' positive or negative feelings when walking to improve the walking environment. However, due to the lack of samples and other internal and external factors influencing emotions (which need to be studied further), it cannot be comprehensively concluded that the pedestrians' emotional states were affected by the walking environment.

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.

The facade, an exterior material of a building, is one of the crucial factors that determine its morphological identity and its functional levels, such as energy performance, earthquake and fire resistance. However, regardless of the type of exterior materials, huge property and human casualties are continuing due to frequent exterior materials dropout accidents. The quality of the building envelope depends on the detailed design and is closely related to the back frames that support the exterior material. Detailed design means the creation of a shop drawing, which is the stage of developing the basic design to a level where construction is possible by specifying the exact necessary details. However, due to chronic problems in the construction industry, such as reducing working hours and the lack of design personnel, detailed design is not being appropriately implemented. Considering these characteristics, it is necessary to develop the detailed design process of exterior materials and works based on the domain-expert knowledge of the construction industry using artificial intelligence (AI). Therefore, this study aims to establish a detailed design automation algorithm for AI-based condition-responsive exterior wall panels and their back frames. The scope of the study is limited to "detailed design" performed based on the working drawings during the exterior work process and "stone panels" among exterior materials. First, working-level data on stone works is collected to analyze the existing detailed design process. After that, design parameters are derived by analyzing factors that affect the design of the building's exterior wall and back frames, such as structure, floor height, wind load, lift limit, and transportation elements. The relational expression between the derived parameters is derived, and it is algorithmized to implement a rule-based AI design. These algorithms can be applied to detailed designs based on 3D BIM to automatically calculate quantity and unit price. The next goal is to derive the iterative elements that occur in the process and implement a robotic process automation (RPA)-based system to link the entire "Detailed design-Quality calculation-Order process." This study is significant because it expands the design automation research, which has been rather limited to basic and implemented design, to the detailed design area at the beginning of the construction execution and increases the productivity by using AI. In addition, it can help fundamentally improve the working environment of the construction industry through the development of direct and applicable technologies to practice.

Measuring management is an important part of preventing the collapse of retaining walls in advance by evaluating their stability with a variety of measuring instruments. The current work of measuring management requires considerable human and material resources since measurement companies need to install measuring instruments at various places on the retaining wall and visit the construction site to collect measurement data and evaluate the stability of the retaining wall. It was investigated that the applicability of the current work of measuring management is poor at small and medium-sized urban construction sites(excavation depth<10m) where measuring management is not essential. Therefore, the purpose of this study is to develop a laser sensor-based hardware to support the wall displacement measurements and their control software applicable to small and medium-sized urban construction sites. The 2D lidar sensor, which is more economical than a 3D laser scanner, is applied as element technology. Additionally, the hardware is mounted on the corner strut of the retaining wall, and it collects point cloud data of the retaining wall by rotating the 2D lidar sensor 360° through a servo motor. Point cloud data collected from the hardware can be transmitted through Wi-Fi to a displacement analysis device (notebook). The hardware control software is designed to control the 2D lidar sensor and servo motor in the displacement analysis device by remote access. The process of analyzing the displacement of a retaining wall using the developed hardware and software is as follows: the construction site manager uses the displacement analysis device to 1)collect the initial point cloud data, and after a certain period 2)comparative point cloud data is collected, and 3)the distance between the initial point and comparison point cloud data is calculated in order. As a result of performing an indoor experiment, the analyses show that a displacement of approximately 15 mm can be identified. In the future, the integrated system of the hardware designed here, and the displacement analysis software to be developed can be applied to small and medium-sized urban construction sites through several field experiments. Therefore, effective management of the displacement of the retaining wall is possible in comparison with the current measuring management work in terms of ease of installation, dismantlement, displacement measurement, and economic feasibility.

Temporary work that utilizes temporary equipment (e.g., system scaffold and system pipe support) in construction work is one of the most vulnerable work from a safety perspective in South Korea. Typically, temporary equipment is reused at construction sites. The Korea Occupational Safety and Health Agency announced guidelines regarding the performance standards for reusable temporary equipment to prevent the accidental collapse of temporary facilities. Nevertheless, temporary facilities' collapse still occurs, which could be attributed to a degradation in the performance due to the reuse of temporary equipment. Therefore, this study investigated the performance of simple temporary structures assembled with new and reused equipment. To this end, an experimental module was designed based on previous research cases, and two experimental models were constructed, in which one was assembled using new equipment (Model A), and the other was built using reused equipment (Model B). To determine the performance of each model, a load test was conducted to measure the maximum load that each model could withstand. The experimental results revealed that the maximum load of Model B was 15% lower than that of Model A. This indicates that there is a meaningful performance difference between those two models. Based on this result, the authors decided to perform additional tests with more realistic models than previous ones. The new experimental module was designed to ensure compliance with the Korean design guidelines. In this presentation, the authors show details of the first tests and their results and plan for the additional test.

Although the construction industry is changing from a 2D-based to a 3D BIM-based management process, 2D drawings are still used as standards for permits and construction. For this reason, 2D deliverables extracted from 3D BIM are one of the essential achievements of BIM projects. However, due to technical and institutional problems that exist in practice, the process of extracting 2D deliverables from BIM requires additional work beyond generating 3D BIM models. In addition, the consistency of data between 3D BIM models and 2D deliverables is low, which is a major factor hindering work productivity in practice. To solve this problem, it is necessary to build BIM data that meets information requirements (IRs) for extracting 2D deliverables to minimize the amount of work of users and maximize the utilization of BIM data. However, despite this, the additional work that occurs in the BIM process for drawing creation is still a burden on BIM users. To solve this problem, the purpose of this study is to increase the productivity of the BIM process by automating the process of extracting 2D deliverables from BIM and securing data consistency between the BIM model and 2D deliverables. For this, an expert interview was conducted, and the requirements for automation of the process of extracting 2D deliverables from BIM were analyzed. Based on the requirements, the types of drawings and drawing expression elements that require automation of drawing generation in the design development stage were derived. Finally, the method for developing automation technology targeting elements that require automation was classified and analyzed, and the process for automatically extracting BIM-based 2D deliverables through templates and rule-based automation modules were derived. At this time, the automation module was developed as an add-on to Revit software, a representative BIM authoring tool, and 120 rule-based automation rulesets, and the combinations of these rulesets were used to automatically generate 2D deliverables from BIM. Through this, it was possible to automatically create about 80% of drawing expression elements, and it was possible to simplify the user's work process compared to the existing work. Through the automation process proposed in this study, it is expected that the productivity of extracting 2D deliverables from BIM will increase, thereby increasing the practical value of BIM utilization.

The Multi-family Residential is one of the most famous building types for a rental property in the US. Often times it includes multiple residential buildings and some amenity facilities, including a clubhouse or leasing office, swimming pool, dog park, and garages. Since the building type is built for rental purposes, the construction planning is phased and it makes the project complicated. Detailed planning and execution are important for successful construction management. This paper provides some management practices that are applied to one of the multi-family residential construction projects in Phoenix, AZ. The Front End Planning (FEP) process performed by both owner and contractor is the first key to a successful construction project. Specifically, the early review of phased turnover strategy, grading, fire/Americans with Disabilities Act (ADA) compliance, and Mechanical/ Electricity/Plumbing/Technology (MEPT) will provide absolute benefit to the project. Second, using a scheduling method to control short-term schedules and long-term can provide the ability to manage the issues with agility. Third, material delivery and procurement dominate the both project schedule and cost. With this COVID-19 circumstance, it is hard to expect the material, equipment, and labor forces to be delivered on time with the contracted price. Managing floats are more than important to managing construction productivity. Risk management should work to share the risks fairly. Lastly, turnover is directly linked with the profit of the project for both owner and contractor. The communication between the owner and contractor to re-schedule the proper turnover schedule is important for the phased construction project.

Recently, research on digital twins to generate digital information and manage construction in real-time using advanced technology is being conducted actively. However, in the construction industry, it is difficult to optimize and apply digital technology in real-time due to the nature of the construction industry in which information is constantly fluctuating. In addition, inaccurate information on the topography of construction projects is a major challenge for earthmoving processes. In order to ultimately improve the cost-effectiveness of construction projects, both construction quality and productivity should be addressed through efficient construction information management in large-scale earthworks projects. Therefore, in this study, a 3D digital map-based AR site management work support system for higher efficiency and accuracy of site management was proposed by using unmanned aerial vehicles (UAV) in wide earthworks construction sites to generate point cloud data, building a 3D digital map through acquisition and analysis of on-site sensor-based information, and performing the visualization with AR at the site By utilizing the 3D digital map-based AR site management work support system proposed in this study, information is able to be provided quickly to field managers to enable an intuitive understanding of field conditions and immediate work processing, thereby reducing field management sluggishness and limitations of traditional information exchange systems. It is expected to contribute to the improvement of productivity by overcoming factors that decrease productivity in the construction industry and the improvement of work efficiency at construction sites.

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.