Nowadays, as-is BIM generation has been popularly adopted in the architecture, engineering, construction and facility management (AEC/FM) industries. In order to generate a 3D as-is BIM of a structural component, current methods require a registration process that merges different sets of point cloud data obtained from multiple locations, which is time-consuming and registration error-prone. To tackle this limitation, this study proposes a registration-free 3D point cloud data acquisition technique for as-is BIM generation. In this study, small-size mirrors that rotate in both horizontal and vertical direction are used to enable the registration-free data acquisition technique. First, a geometric model that defines the relationship among the mirrors, the laser scanner and the target component is developed. Second, determinations of optimal laser scanner location and mirror location are performed based on the developed geometrical model. To validate the proposed registration-free as-is BIM generation technique, simulation tests are conducted on key construction components including a PC slab and a structural wall. The result demonstrates that the registration-free point cloud data acquisition technique can be applicable in various construction elements including PC elements and structural components for as-is BIM generation.

Reinforcement steel fixing is a skilled and manually intensive construction trade. Current practice for the quality assessment of reinforcement steel fixing is normally performed by fabricators and has high potential in having errors due to the tedious nature of the work. In order to overcome the current inspection limitation, this study presents an approach that provides visual assistance and inspection enhancement for inspectors to assess the dimensional layout of reinforcement steel fixing. To this end, this study aims to establish an end-to-end framework for rebar layout quality inspection using laser scanning and Augmented Reality (AR). The proposed framework is composed of three parts: (1) the laser-scanned rebar data processing; (2) the rebar inspection procedure integrating with AR; and (3) the checking and fixing the rebar layout through AR visualization. In order to investigate the feasibility of the proposed framework, a case study assessing the rebar layout of a lab-scaled formwork containing two rebar layers is conducted. The results of the case studies demonstrate that the proposed approach using laser scanning and AR has the potential to produce an intuitive and accurate quality assessment for the rebar layout.

Multilayer sub-contracting is a significant practice among the world, including Hong Kong. When a principal contractor secured a project from a developer, the specific jobs will usually be breaking down and sub-contractors with the lowest bid [1]. The adoption of multilayer sub-contracting has been a controversy issue which is considered as a two-side blade. While certain studies have been carried out to examine both the contributions, damages and improvements for multi-layer subcontracting, the construction industry and researchers are still waiting for a solid measure to enhance the system. Hence, this research attempts to study the advantages, disadvantages, conducts a comparison between single and multilayer sub-contracting and measures of current Hong Kong construction industry based on literature review, questionnaire and in-depth interviews. To achieve the objectives, Analytic Hierarchy Process (AHP) and total weighted score methods are adopted to examine and rank the criterion. The findings of this study provide a good basis for understanding the major reasons and problems caused by the adoption of multilayer sub-contracting. Besides, the identified safety perspective explores a new perspective regarding to issues of multi-layer subcontracting, which will serve as a solid foundation for further research to enhance safety performance. Finally, the findings of measurements towards improvement of multilayer sub-contracting will also provide a solidsolution for construction industry.

In the building construction, the steel-frame work occupies an important position in terms of structure, cost and quality. Especially in Japan, steel frames have traditionally been the main structure of many buildings. For steel-frame works in such positions, this paper investigates an existing steel fabricator to clarify the actual conditions of design decision making process and management method in steel production process. This study focuses on a steel fabricator (Company M in the following sentences), whose main market is Japan and which has facilities in Thailand, China, and Japan. Company M uses QR codes to control the production status of products, and exchanges all information between inside and outside the company via specialized departments in the form of documents. The authors have already analyzed the relationship between production lead time and defect rate based on actual project data at Architectural Institute of Japan in 2016. In 2019, we expressed the process from the confirmation of the design information of the current steel frame to the production by WBS, and clarified the relationship between the production lead time and steel frame product quality structurally. In this paper, the authors reoport the progress of the survey conducted so far, the positioning of the collected data, and the future survey policy.

Concurrent construction offers considerable improvement for shorten the project duration of its production process. Therefore, standardized concurrent construction is widely applied in building construction projects. However, resources planning for standardized concurrent construction project is manually developed by construction manager. This practice is not effective since it is time-consuming and error-prone for managers to identify all project-specific information, distinguish different activity-resource types, interpret these types and analyze how they affect resource allocated on an ad hoc basis. Therefore, this research investigates the opportunity for leveraging activity modeling to enable automated resource planning for standardized concurrent construction during project development, with identifying the characteristics of construction activities under standardized concurrent planning and determining the activity-resources types that affects resource planning. Both will function as a basis for modeling these construction activities in a computer-interpretable manner and for automation in resource planning.

The 21st century is witnessing a rapid growth of tall buildings in urban centers globally to create more urban space for an anticipated urban population. Tall buildings, however suffer from incessant delays and sometimes total abandonment. Consequently, this study investigated and ranked the causes of delay in tall building projects, while focusing on the Gulf Cooperation Council (GCC) countries. Initially, 36 common delay causes investigated globally were categorized into 9 groups, and then further ranked utilizing the Relative Importance Index (RII) through a questionnaire survey. Tall building professionals in the GCC countries (Saudi Arabia, United Arab Emirates, Bahrain, Kuwait, Oman and Qatar) were contacted. The respondents' categories include Consultants, Contractors, and Clients' Representatives/Facility Managers. The results reveal that the top three causes include "client's cash flow problems/delays in contractor's payment", "contractor's financial difficulties", and "poor site organization and coordination between various parties". The findings from this study could help construction professionals develop guidelines and controls for delay mitigation, as well as support them in risk-based decision making in the planning of tall building projects.

The assembly of modular construction requires a series of thoroughly-considered decisions for crane lifting including the crane model selection, crane location planning, and lift path planning. Traditionally, this decision-making process is empirical and time-consuming, requiring significant human inputs. Recently, research efforts have been dedicated to improving lift planning practices by leveraging cutting-edge technologies such as automated data acquisition, Building Information Modelling (BIM) and computational algorithms. It has been demonstrated that these technologies have advanced lift planning to some degree. However, the advancements tend to be fragmented and isolated. There are two hurdles prevented a systematic improvement of lift planning practices. First, the lack of formalized lift planning workflow, outlining the procedure and necessary information. Secondly, there is also an absence of a shared information environment, enabling storages, updates and the distribution of information to stakeholders in a timely manner. Thus, this paper aims to overcome the hurdles. The study starts with a literature review in combination with document analysis, enabling the initial workflow and information flow. These were contextualised through a series of interviews with Australian practitioners in the crane-related industry, and systematically analysed and schematically validated through an expert panel. Findings included formalized workflow and corresponding information exchanges in a traditional lift planning practice via a Business Process Model and Notation (BPMN). The traditional practice is thus reviewed to identify opportunities for further enhancements. Finally, a BIM-based lift planning workflow is proposed, which integrates the scattered technologies (e.g. BIM and computational algorithms) with the aim of supporting lift planning automation. The resulting framework is setting out procedures that need to be developed and the potential obstacles towards automated lift planning are identified.

Modular construction is a construction method whereby prefabricated volumetric units are produced in a factory and are installed on site to form a building block. The construction productivity can be substantially improved by the manufacturing and assembly of standardized modular units. 3D printing is a computer-controlled fabrication method first adopted in the manufacturing industry and was utilized for the automated construction of small-scale houses in recent years. Implementing 3D printing in the fabrication of modular units brings huge benefits to modular construction, including increased customization, lower material waste, and reduced labor work. Such implementation also benefits the large-scale and wider adoption of 3D printing in engineering practice. However, a critical issue for 3D printed modules is the loading capacity, particularly in response to horizontal forces like wind load, which requires a deeper understanding of the building structure behavior and the design of load-bearing modules. Therefore, this paper presents the state-of-the-art literature concerning recent achievement in 3D printing for buildings, followed by discussion on the opportunities and challenges for examining 3D printing in modular construction. Promising 3D printing techniques are critically reviewed and discussed with regard to their advantages and limitations in construction. The appropriate structural form needs to be determined at the design stage, taking into consideration the overall building structural behavior, site environmental conditions (e.g., wind), and load-carrying capacity of the 3D printed modules. Detailed finite element modelling of the entire modular buildings needs to be conducted to verify the structural performance, considering the code-stipulated lateral drift, strength criteria, and other design requirements. Moreover, integration of building information modelling (BIM) method is beneficial for generating the material and geometric details of the 3D printed modules, which can then be utilized for the fabrication.

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

The construction industry remains serious accidents, injuries, and fatalities due to it's unique, dynamic, and temporary nature. On workplace sites, Safety pre-task planning is one of the efforts to minimize injuries and help construction personnel to identify potential hazards. However, the working conditions are complicated. Many activities, including tasks or job steps, are executing at the same time and place. It may lead to an increase in the risks from simultaneous tasks. This paper contributes to addressing this issue by introducing a safety risk interaction analyzing framework. To accomplish this objective, accident reports of the Occupational Safety and Health Administration (OSHA) are investigated. The pairs of task incompatibility, which have time-space conflicts and lead to incidents, are found. Ontology technology is applied to build the risk database, in which the information is acquired, structuralized. The proposed system is expected to improve pre-task planning efficiency and relieve the burdens encountered by safety managers. A user scenario is also discussed to demonstrate how the ontology supports pre-task planning in practice.

The construction industry is undergoing a digital transformation in which Building Information Modelling (BIM) is a key technology. The potential of BIM in several areas such as design optimization, time management, cost management, and asset management/facility management (AM/FM) is widely acknowledged by the AECO (Architecture, Engineering, Construction, and Operation) industry around the world. However, BIM implementation in construction projects is faced with problems such as project delay and cost overruns. The lack of identification of risks in BIM projects and standard guidelines on mitigation techniques furthers poor performance, dissatisfaction, and disputes between employers and project participants, which results in low BIM adoption rates. Therefore, the objective of this paper is to identify the potential risks in BIM implementation under the primary categories - (1) technical, (2) contractual, (3) management-related, and (4) personnel-related risks in BIM projects and present solutions to reduce, manage, and mitigate risks. To meet the objective of this paper, a survey was designed and conducted in the Hong Kong construction industry in which over 140 respondents from different disciplines, with experience in BIM projects, have participated. Based on the analysis of the survey data, the most severe and frequently occurring BIM risks and their potential mitigation strategies were identified and discussed in this paper.

Many of hotel customers obtain information from hotel websites to find the best alternative. One of the crucial information for the choice is spatial/visual information of hotel rooms. However, hotel website provides photographs only showing representative room features that may not be sufficient to give a full understanding of hotel room to customers. HMD-based 3D virtual environments (HVE) created by 3D camera could improve customers' experiences of hotel rooms by providing full virtual tours of hotel rooms. However, to the best of our knowledge, whether HVE can adequately provide similar customers' perception on spatial/visual information remains unproven as physical hotel rooms. The present study thus aims to verify how similar and reliable information on physical hotel room HVE provides to hotel customers in comparison with hotel website with 2D photograph and display-based 3D virtual environment. For this purpose, this study conducted a comparative experiment to investigate perception of three environments. As a result, the study found that HVE is more effective to provide spatial/visual information as similar as an actual hotel room. In addition, HVE increases customers' perceptions towards the reliability of information, the quality of hotel room and intention to book.

This research investigates how a Virtual Reality (VR)-based simulation could be used to train safe operation skills for forklift operators. Forklift operation is categorized as high-risk work by many occupational health and safety regulators and authorities due to high injury and fatality rates involved with forklifts. Therefore, many safety guidelines have been developed for forklift operators. Typically, forklift operation safety training is delivered based on instructional texts or videos, which have limitations in influencing people's safety behavior. Against this background, we propose a VR-based forklift simulator that can enable safe operation skills training through a feedback system. The training program consists of several modules to teach how to perform the basic tasks of forklift operation, such as driving, loading and unloading, following the safety guidelines. The system provides instantaneous instructions and feedback regarding safe operation. This training system is based on the model of "learning-by-doing". The user can repeat the training modules as many times as necessary before being able to perform the given task without violating any safety guidelines. The last training module tests the user's acquisition of all safety skills required. The user feedback from several demonstration sessions showed the potential usefulness of the proposed training system.

Augmented reality (AR) technology assists construction workers by superimposing additional virtual information onto their real worksite environments. Ideally, this provides them with a better understanding of their tasks and hence boosts task performance. However, the additional information that AR places in users' field of view could limit their ability to understand what is going on in their surroundings and to predict how conditions may change in the near future. AR-assisted systems on construction sites could therefore expose their users to safety risks due to disturbance from the system. Hence, it is important to understand how AR-assisted systems can block users' understanding of their immediate environments, and in turn, how worksite safety in the construction industry could be improved through better design of such systems. This preliminary research conducted a laboratory experiment that simulated rebar inspection tasks and compared the situational awareness of AR users against that of subjects using traditional paper-based inspection methods, as measured by the Situation Awareness Rating Technique. Based on the results, we discuss the safety impact of head-mounted AR-assisted displays on situational awareness during construction tasks.

Providing safety training to construction workers is essential to reduce safety accidents at the construction site. With the prosperity of visualization technologies, Immersive Virtual Reality (IVR) has been adopted for construction safety training by providing interactive learning experiences in a virtual environment. Previous research efforts on IVR-based training have found that the level of fidelity of interaction between real and virtual worlds is one of the important factors contributing to the sense of presence that would affect training performance. Various interactive devices that link activities between real and virtual worlds have been applied in IVR-based training, ranging from existing computer input devices (e.g., keyboard, mouse, joystick, etc.) to specially designed devices such as high-end VR simulators. However, the need for high-fidelity interactive devices may hinder the applicability of IVR-based training as they would be more expensive than IVR headsets. In this regard, this study aims to understand the impact of the level of fidelity of interactive devices in the sense of presence in a virtual environment and the training performance during IVR-based forklift safety training. We conducted a comparative study by recruiting sixty participants, splitting them into two groups, and then providing different interactive devices such as a keyboard for a low fidelity group and a steering wheel and pedals for a high-fidelity group. The results showed that there was no significant difference between the two groups in terms of the sense of presence and task performance. These results indicate that the use of low-fidelity interactive devices would be acceptable for IVR-based safety training as safety training focuses on delivering safety knowledge, and thus would be different from skill transferring training that may need more realistic interaction between real and virtual worlds.

Blast furnace revamping in steel industry is one of the most important work to complete the complicated equipment within a short period of time based on the interfaces of various types of work. P company has planned to build a Smart Tracking System based on the wireless tag system with the aim of complying with the construction period and reducing costs, ahead of the revamping of blast furnace scheduled for construction in February next year. It combines the detailed design data with the wireless recognition technology to grasp the stage status of design, storage and installation. Then, it graphically displays the location information of each member in relation to the plan and the actual status in connection with Building Information Modeling (BIM) 4D Simulation. QR Code is used as a wireless tag in order to check the receiving status of core equipment considering the characteristics of each item. Then, DB in server system is built, status information is input. By implementing BIM 4D Simulation data using DELMIA, the information on location and status is provided. As a feature of the S/W function, a function for confirming the items will be added to the cellular phone screen in order to improve the accuracy of tagging of the items. Accuracy also increases by simultaneous processing of storage and location tagging. The most significant effect of building this system is to minimize errors in construction by preventing erroneous operation of members. This system will be very useful for overall project management because the information about the position and progress of each critical item can be visualized in real time. It could be eventually lead to cost reduction of project management.

Automated rebar fabrication, which requires effective information exchange between model designers and fabricators, has brought the integration and interoperability of data from different sources to the notice of both academics and industry practitioners. Industry Foundation Classes (IFC) was one of the most commonly used data formats to represent the semantic information of prefabricated components in buildings, whereas the data format utilized by rebar fabrication machine is BundesVereinigung der Bausoftware (BVBS), which is a numerical data structure exchanging reinforcement information through ASCII encoded files. Seamless transformation between IFC and BVBS empowers the automated rebar fabrication and improve the construction productivity. In order to improve data interoperability between IFC and BVBS, this study presents an IFC extension based on the attributes required by automated rebar fabrication machines with the help of Information Delivery Manual (IDM) and Model View Definition (MVD). IDM is applied to describe and display the information needed for the design, construction and operation of projects, whereas MVD is a subset of IFC schema used to describe the automated rebar fabrication workflow. Firstly, with a rich pool of vocabularies practitioners, OmniClass is used in information exchange between IFC and BVBS, providing a hierarchy classification structure for reinforcing elements. Then, using International Framework for Dictionaries (IFD), the usage of each attribute is defined in a more consistent manner to assist the data mapping process. Besides, in order to address missing information within automated fabrication process, a schematic data mapping diagram has been made to deliver IFC information from BIM models to BVBS format for better data interoperability among different software agents. A case study based on the data mapping will be presented to demonstrate the proposed IFC extension and how it could assist/facilitate the information management.

This paper presents a study investigating the relationship between stress level and reworks for construction professionals. As employees' work-life balance (WLB) becomes more important nowadays, controlling the level of stress in the workplace becomes more important as occupational stress has negative impacts on WLB. Reworks can be one severe occupational stressor as people suffer from stress when they need to redo their works. This study hypothesizes that there is a positive correlation between reworks and level of stress, meaning that people tend to show high level of stress when they need to redo their works. The hypothesis will be tested by checking the stress level when engineers redo their works because of changes or errors. An electroencephalography (EEG) sensor will be used to measure the stress level of engineers when they redo their works. For more accurate measure of stress, the stress level will also be measured by the galvanic skin response (GSR). This experiment is expected to prove that rework process is a severe stressor for construction professionals, which will contribute to lower productivity and poorer WLB. The finding will emphasize the importance of managing reworks in the construction industry, which will eventually help construction managers to control the level of employees' stress successfully.

This paper investigates the construction equipment accidents by time. Construction sites are unique with many different hazardous conditions which cause accidents. According to the Occupational Safety and Health Administration (OSHA), accidents related to construction equipment are one of the most leading causes of fatal injuries in the construction industry. While there have been many studies investigating the equipment-related accidents, few research studies provided in-depth analyses about the time that accidents frequently occurred. By using the OSHA accidents data collected between 1997 and 2012, this paper analyzed the accidents data by time, equipment type including excavator, backhoe, dozer, and crane, accident cause, and injury class. The analyses revealed that the time window with most accidents was between 13:00 and 13:59. In terms of the injury class, the time windows with the highest numbers of equipment accidents were between 13:00 and 13:59 and between 11:00 and 11:59 for fatality and hospitalization, respectively. For the accident causes, equipment operator's error was the highest number of accident causes. It is expected that findings from the analyses can be used to more strategically develop management plans and guidelines to prevent accidents related to construction equipment to practitioners.

Due to hazardous working environments at complex, unstructured, and dynamic construction sites, workers frequently face potential safety and health risks throughout the construction process. In this regard, addressing safety challenges remains one of the top priorities. Construction workers' ability to identify and assess risks is acquired through training, which is one of the primary key factors to determine their safety and wellbeing in hazardous working environments. As such, safety managers constantly focus on the effectiveness of the training materials provided to the workers. However, the construction workers are considerably at greater risk of injuries and fatalities compared to the workers in other industries. In this regard, further studies are required to build up a body of knowledge on the conventional safety training approaches as well as their evaluation techniques in order to boost up the adoption by the practitioners in a widespread manner. This paper provides a systematic review of the current safety training approaches and the various techniques for measuring their effectiveness. The attributes of the current safety training methods for construction workers and their evaluation techniques are identified and analyzed. Results indicated that: 1) immersive environment-based training methods are effective than the traditional safety training methods; 2) this effectiveness can be empirically supported by evaluation strategies, but the current techniques are subjective, intrusive, and error-prone. This research offers fresh opportunities to investigate the training strategies by objectively monitoring the physiological responses of construction crews. The results of this study can be used by researchers and practitioners to identify and determine optimal safety training programs that could potentially become ubiquitous in the construction industry.

Diversification of project delivery methods (PDM) under ever-changing construction business environment has significantly changed the role of project participants. Active efforts to effectively sharing the roles and responsibilities have been observed in the project management offices (PMOs) among owner/operator organizations as well as engineering, procurement, construction and maintenance (EPCM) firms. In order for being effective in a holistic way throughout the project life-cycle, a PMO needs to have 'adequate management skills' as well as 'essential technical capabilities' in cooperating with many different participants. One of the well-known examples of the PMO's tool to support these skills and capabilities is the effective 'work packaging (WP)' that serves as a common basis integrating all relevant information in a structured manner. In an attempt to enhance the construction productivity, the concept of 'advanced work packing (AWP)' has been introduced by Construction Industry Institute (CII). The AWP enables productivity to be improved by early planning of construction packages in the design phase "with the end in mind". The purpose of this study is to identify and evaluate the 'variables' of advanced work packing (AWP) for life-cycle information integration. Firstly, an extended concept of advanced WP based on the CII AWP was defined in order to comprehend many different issues of business functions (e.g. cost, schedule, quality, etc.). A structured list of major components and variables of AWP were then identified and examined for practical viability with real-world examples. Strategic fits and managerial effectiveness were stressed throughout the analyses. Findings, implications and lessons learned are briefly discussed as well.

The purpose of this study is to investigate the current status of claim management process of general contractors in South Korea. As the claim management becomes more important nowadays, maintaining the process for claim management systematically and consistently becomes more important as well. To improve the claim management process, it is necessary to diagnose the current status of claim management process so companies establish their targets for improvement. This study develops a survey to identify the current status of claim management process that major general contractors in South Korea have. Questions in the survey are classified into four categories including entitlement check, potential claim event check, time bar check, and tasks for substantiation. By conducting a series of statistical analyses with 94 survey data collected from employees working in the general contractor companies in South Korea, this study examines and analyzes their claim management process in terms of the several categories. It is expected that the results contribute to diagnosing how practitioners maintain their claim management, which will help them establish the direction of management enhancement.

This research evaluated the construction market and project environment of nine nations within the ASEAN members. Quantitative data from global consulting firms and international organizations were identified and normalized for evaluation. The result of the analysis was that Indonesia was ranked highest for construction market growth while Singapore was ranked highest for stability of project environment. The research results can be utilized by construction companies that are planning on entering the construction market within the ASEAN members.

Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

In addition to a range of H&S documentation, a range of actions, beliefs, interventions, practices, and states are important in terms of achieving optimum construction H&S. Conclusions include that H&S documentation facilitates and assists planning, organising, leading, controlling, and coordinating H&S. Furthermore, current H&S documentation: is inappropriate in that it can be complex, generic, lengthy, onerous, repetitive (duplicative), and vague; engenders dubious practices; generally, 'does not add the potential value'; shifts the focus from the physical process, and could be improved. Recommendations include: industry associations should review their 'audit system' to interrogate the allocation of points; H&S documents must reflect the intention of the requirement; the synergy between H&S documentation, and actions, interventions, and practices should be investigated, digested, and focused upon, and 'audits', or rather inspections, should focus more on the physical process, actions, interventions, and practices, than documentation.

Historical construction health and safety (H&S) challenges, in terms of a range of resources and issues, continue to be experienced, namely design process-related hazards are encountered on site, workers are unaware of the hazards and risks related to the construction process and its activities, activities are commenced on site without adequate hazard identification and risk assessments (HIRAs), difficulty is experienced in terms of real time monitoring of construction-related activities, workers handle heavy materials, plant, and equipment, and ultimately the experience of injuries. Given the abovementioned, and the advent of Industry 4.0, a quantitative study, which entailed the completion of a self-administered questionnaire online, was conducted among registered professional (Pr) and candidate Construction H&S Agents, to determine the potential of Industry 4.0 to contribute to resolving the challenges cited. The findings indicate that Industry 4.0 technologies such as augmented reality (AR), drone technology, virtual reality (VR), VR based H&S training, and wearable technology /sensors have the potential to resolve the cited H&S challenges as experienced in construction. Conclusions include that Industry 4.0 technologies can finally address the persistent H&S challenges experienced in construction. Recommendations include: employer associations, professional associations, and statutory councils should raise the level of awareness relative to the potential implementation of Industry 4.0 relative to H&S in construction; case studies should be documented and shared; tertiary construction management education programmes should integrate Industry 4.0 into all possible modules, especially H&S-related modules, and continuing professional development (CPD) H&S should address Industry 4.0.

Objective: To investigate test-retest reliability and responsiveness of Equivital Lifemonitor and photoplethysmography based wristwatch tools in assessing physiological parameters during a simulated fatigue task. Methods: Ten university students (Mean age, 30.6 ± 1.7 years) participated in this pilot study. Participants were asked to perform a 30-minute of a simulated fatigue task in an experimental setup in a lab. The physiological parameters (e.g., heart rate, heart rate variability, respiratory rate, electrodermal activity, and skin temperature) were measured at baseline and immediately after the fatigue task. An intraclass correlation coefficient (ICC2,1) was used to evaluate the test-retest reliability of each tool in assessing physiological measures. In addition, the responsiveness of each tool to measure changes from baseline to posttest was calculated using a standardized response mean. Results: The Equivital Lifemonitor has shown good to excellent test-retest reliability for the assessment of heart rate (ICC, 0.97), heart rate variability (ICC, 0.86), respiratory rate (ICC, 0.77), and local skin temperature (ICC, 0.76). However, photoplethysmography based wristwatch showed moderate to good test-retest reliability for the assessment of heart rate (ICC, 0.71), heart rate variability (ICC, 0.73), electrodermal activity (ICC, 0.80), and skin temperature (ICC, 0.72). A large standardized response mean (>0.8) indicates that both tools can capture the changes in heart rate, heart rate variability, respiratory rate, skin temperature, and electrodermal activity after a 30-minute of fatigue task. Conclusions: The Equivital Lifemonitor and photoplethysmography based wristwatch devices are reliable in measuring physiological parameters after the fatigue task. Additionally, both devices can capture the fatigue response after a simulated construction task. Future field studies with a larger sample should investigate the sensitivity and validity of these tools in measuring physiological parameters for fatigue assessment at construction sites.

The present research evaluates the public engagement approach in various project stages. Hong Kong had long been criticized as top-down and executive-led jurisdiction by overlooking the importance of cultural heritage and public concerns on public projects. It was suggested to the government to engage public and provide sufficient public consultation. Thereby, the government announced a series of revitalization and conversion measures in Policy Address in 2008. To carry out the measures, there were voices, because of diverse and sometimes conflicting interests, over the effect of revitalization project. On the other hand, studies reveal that there are benefits of revitalization and enhancement of public engagement approach. In pursuit of the subject, the present research aims at studying the Central Market as a case study pilot project. In October 2009, the Policy Address announced that the Central Market be revitalized. Tasked to implement the project, Urban Renewal Authority (URA) continued to adopt the people-oriented approach as the guiding principle in its core business and in heritage preservation and revitalization so as to create a sustainable development. Between government and the public, URA acts as a bridge for communication. As between URA and the public, URA conducted public consultation, set up an ad-hoc committee in January 2016 to be in charge of the project and will continuously inform the government and public with the updates and project progress. The main objectives of the present research are to assess the advantages and challenges in different stages of public engagement approach, to evaluate the engagement system, to give a comprehensive view for participation of stakeholders, and to find out effective strategies to enhance civil engagement. Research was achieved through interviews to key persons in the project, questionnaire that was distributed to community and experts in the field. Case Study of the Central Market was studied and investigated using different sources, such as newspapers, journals, etc, to evaluate the degree of public engagement in the project. Both detailed qualitative methodological approaches of interview, questionnaire, and case study, act as a synergy to demonstrate the research objectives and provide the comprehensive perceptions on the revitalization project. The results show that public participants in revitalized project have generated considerable value enhancements to social-cultural, environmental, political and economic aspect. This study provides valuable insights that the public participation can make positive contribution to sustainability in the city. The findings indicate that no any single system is flawless therefore seeking both public opinions and professional advices are also important as a comprehensive approach to achieve higher sustainability of the building.

Modular Integrated Construction (MiC) has gained quite momentum as it provides solutions for several problems in the construction sector, particularly in Hong Kong. MiC is converting the building into modules and erecting them easily on site providing various benefits, such as cost and time savings, better quality, lower risk, higher sustainability, less injuries and accidents, etc. The MiC is considered the best alternative to traditional construction approaches in solving the huge housing demand problem in many cities all over the world due to the time-saving privilege. The utilization of MiC is facing a lot of challenges, which are considered obstacles to the wide acceptance of this technique in the construction industry in Hong Kong. This, in turn, has led the interests of the research community to investigate its benefits and challenges aiming at addressing several solutions to harness the benefits of this technique and to tackle these challenges. The present research aims to review the main concept of MiC and to determine the benefits and the challenges of implementing MiC in construction industry. The findings of this research highlight the advantages and limitations of incorporating MiC technique in Hong Kong, which will help the stakeholders to effectively utilize this technique in the construction industry.

Newly graduated students are shown to constitute an important source of innovation within the architectural, engineering and construction (AEC) industry. In relation to digital technologies like BIM (Building Information Modelling) that is claimed to have a potential to transform the industry, newly graduated students may play a vital role in innovating with BIM. The paper aims to explore determinants for students perceived potential of BIM use (PPBU) and the role of the educational background. The aim will be achieved by analysing the results from a survey conducted among third- and fourth-year students in construction and civil engineering in Hong Kong, Sweden, and Thailand (n = 194). When the different groups are compared Swedish and Thai students perceive a significant higher PPBU than Hong Kong students. In a step-wise multiple regression analysis five predictors for PPBU were identified for Thai respectively Swedish students, and one predictor was identified for Hong Kong students. It is concluded that in the contemporary BIM-discourse it is claimed that BIM can/should transform the industry, and BIM is even seen as a disruptive technology, and newly graduated students will contribute to (digitally driven) innovation. However, from the predictors of PPBU, the question can raised if the awareness of the need for structural changes is lacking in the education, if students later in their working life should contribute to a BIM-induced transformation of the industry?

Safety culture is a collection of the beliefs, perceptions and values that employees share in relation to risks within an organisation. On the other hand, a resilient safety culture (RSC) means a culture with readiness of the organisation to respond effectively under stress, bounce back from shocks and continuously learn from them. RSC helps organisations to protect their interest which can be attributed to behavioural, psychological and managerial capabilities of the organization. Quantification of the degree of resilience in an organisation's safety culture can provide insights about the strong and weak links of the organisation's overall health and safety situation by identifying potential causes of system or sub-system failure. One of the major challenges of quantification of RSC is that the attributes that determine RSC need to be measured through constructs and indicators which are complex and often interrelated. In this paper, we address this challenge by applying a fault tree analysis (FTA) technique which can help analyse complex and interrelated constructs and indicators. The fault tree model of RSC is used to evaluate resilience levels of two organisations with remote and urban locations in order to demonstrate the failure path of the weak links in the RSC model.

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

Project delays, cost overruns, and disputes are becoming a norm for the construction industry in Hong Kong. Researchers argue that the inability of traditional contracts to manage risk and associated changes are perhaps the main points of contention. The Institution of Civil Engineers published a new engineering contract (NEC), NEC4 Suite of Contracts in this to facilitate better risk management through collaborative culture in construction projects. NEC aims to increase the chances of project success thought its flexible nature of contracts, 'simple' and 'clearly written' documents and provision for the incentive by adopting a better management approach. This paper focuses on traditional and NEC contracts to compare risk management and change management aspects. Through literature review and preliminary interviews with three industry professionals, the paper is exploring how a change in traditional contracts can recuperate from disaster. Our interviewees in this work have extensive experience in traditional as well as in NEC contracts. The results suggest a proactive risk management provisions in NEC contracts does make a difference to avoid later escalation of issues. Whereas, management of change helps streamline all identified issues through a structured process without going in mediation or litigation. NEC, with its new approach to collaborative working, allows partners to be vigilant, yet gratifying in the project process.

Due to the enhancing environmental concerns worldwide with the need of increasing demand for sustainability of building design, maintenance and operation, key stakeholders including the government and developers in many countries strike for the benefits in implementing the green design and building concepts in constructing, infrastructure as well as the buildings. Different countries have their standards or certifications for green buildings while the adoption rate of BEAM-Plus in HK is relatively less compared with other developed countries such as Europe, USA and Japan. Therefore, in the present research, BEAM-Plus, the beginning assessment method of green standard implemented in HK, will be mainly discussed. Current situation of BEAM-Plus implementation in HK will be reviewed and then adopt a systematic approach via literature review and research paper, questionnaire with Analytic Hierarchy Process (AHP) method to depict the opportunities and challenges from the perspective of government and developers regarding implementing BEAM-Plus in HK and thus investigate the implementation gaps. It is found that for both the macro level of opportunity and challenge, the most important criterion is political, in which the weighting value are 0.3114 and 0.2321 respectively. It is obvious that government plays a critical and significant role in affecting the development of BEAM plus. Technological difficulty is also an important factor that challenging and hindering the implementation of BEAM plus, the weighting value is 0.2194 under challenge hierarchy. More experts and professionals should be imported to Hong Kong to enhance the technique is building green buildings. At the end of this paper, solutions and actions will also be suggested and concluded in alleviating the challenges. Finally, solutions and actions are suggested and concluded in alleviating the challenges. Findings from this research can guide developers to consider adopting green elements, government and Green Building Council in HK to review green buildings' policy.

Modular integrated construction (MiC) is an innovative construction method where components of a building are manufactured in an offsite factory, trucked to the job site in sections, set in place with cranes, and assembled together to form a whole building. Where circumstances merit, favorable conditions exist and implemented effectively; MiC improves project performance. However, several key factors need to converge during implementation to realize the full benefits of MiC. Thus, a thorough understanding of the factors which are critical to the success of MiC projects is imperative. Drawing on a systematic review of 47 empirical studies, this research identified 25 key success factors (KSFs) for MiC projects. Of these, the five topmost cited KSFs for MiC projects include effective working collaboration and communication among project participants; standardization, optimization, automation and benchmarking of best practices; effective supply chain management; early design freeze and completion; and efficient procurement method and contracting. The study further proposed a conceptual model of the KSFs, highlighting the interdependences of people, processes, and technology-related KSFs for the effective accomplishment of MiC projects. The set of KSFs is practically relevant as they constitute a checklist of items for management to address and deal with during the planning and execution of MiC projects. They also provide a useful basis for future empirical studies tailored towards measuring the performance and success of MiC projects. MiC project participants and stakeholders will find this research useful in reducing failure risks and achieving more desired performance outcomes. One potential impact of the study is that it may inform, guide, and improve the successful implementation of MiC projects in the construction industry. However, the rigor of the analysis and relative importance ranking of the KSFs were limited due to the absence of data.

Recent efforts to develop a common standard for nuclear power plants (NPPs) with the aim of creating (1) a digital environment for a better understanding of NPPs life-cycle management aspect and (2) engineering data interoperability by using existing standards among different unspecified project participants (e.g., owners/operators, engineers, contractors, equipment suppliers) during plants' life cycle process (EPC, O&M, and decommissioning). In order to meet this goal, there is a need for formulating a standardized high-level physical breakdown structure (PBS) for NPPs project management office (PMO). However, high-level PBS must be comprehensive enough and able to represent the different types of plants and the new trends of technologies in the industry. This has triggered the need for addressing the issues of the recent operational NPPs and future technologies' ramification for evaluating the changes in the NPPs physical components in terms of structure, system, and component (SSC) configuration. In this context, this ongoing study examines the recent conventional NPPs and technological trends in the development of future NPPs facilities. New reactor models regarding the overlap of variant issues of nuclear technology were explored. Finally, issues on PBS for project management are explored by the examination of the configuration of NPPs primary system. The primary systems' configuration of different reactor models is assessed in order to clarify the need for analyzing the new trends in nuclear technology and to formulate a common high-level PBS. Findings and implications are discussed for further studies.

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

With the 4th industrial revolution, many advanced information technologies are being applied to the area of construction engineering and project management. These applications are usually focusing on design, construction and operation stage and are producing many meaningful fruits. Even though these studies are very important for the development of the construction industry, this study insists that the other stage perspective such as construction business also should be emphasized. Because business phase has significant impacts on the success of a construction project as well as design, construction and operation phase. So, this study reviewed the intelligent-approach papers in planning and marketing, estimation and bid, contract and claim, and project financing fields. This study provides some insights such as values, difficulties, limitations and future directions of business intelligence application.

Global construction and engineering consulting (E&C) firms are actively seeking entry into overseas markets based on loan projects from multilateral development banks to provide a basis for entry into overseas markets and sustainable growth. Bids on these projects are competitive between global top firms in terms of the technical level and price due to the limited number of projects; thus, developing a successful partnership to complement competence has become an essential element to win bids. In this regard, many studies have analyzed enterprises through characteristic analyses or the derivation of influential factors from the past social networks based on social network analysis (SNA). However, few studies have been conducted to reflect the process of changes to analyze collaborative relationships. Thus, this study aims to identify dynamic changes in past social networks and develop a model that can predict changes in the relationships between E&C firms based on similarities or differences between firms, presenting a methodology to target firms for appropriate collaboration. The analysis results demonstrate that the sensitivity of the developed prediction model was 70.26%, which could accurately predict 163 out of 232 actual cooperative cases.

Wearable biosensors have the potential to non-invasively and continuously monitor seniors' stress in their daily interaction with the urban environment, thereby enabling to address the stress and ultimately advance their outdoor mobility. However, current wearable biosensor-based stress detection methods have several drawbacks in field application due to their dependence on batch-learning algorithms. First, these methods train a single classifier, which might not account for multiple subjects' different physiological reactivity to stress. Second, they require a great deal of computational power to store and reuse all previous data for updating the signle classifier. To address this issue, we tested the feasibility of online multi-task learning (OMTL) algorithms to identify multiple seniors' stress from electrodermal activity (EDA) collected by a wristband-type biosensor in a daily trip setting. As a result, OMTL algorithms showed the higher test accuracy (75.7%, 76.2%, and 71.2%) than a batch-learning algorithm (64.8%). This finding demonstrates that the OMTL algorithms can strengthen the field applicability of the wearable biosensor-based stress detection, thereby contributing to better understanding the seniors' stress in the urban environment and ultimately advancing their mobility.

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

This paper proposes a study measuring productivity improvement by using a type of technology called "Machine Guidance" through work sampling in earthwork. Earthwork is the activity typically on the critical path, indicating that productivity for the activity is critical for managing schedule on time. Thanks to the development of sensing and information system technologies, productivity for earthwork has been improved. While there have been many studies investigating the application of a certain type of technology to earthwork, few studies have measured the productivity improvement and presented how the technology leads to productivity improvement. Based on the thorough literature review, it is hypothesized that Machine Guidance contributes to improving productivity of earthwork by reducing indirect workhours spent for information waiting and inspection. In addition to the literature review, this paper presents a research model to test the hypothesis by using the work sampling technique. By proving and quantifying the productivity improvement from the technology use, this study can help practitioners justify the investment for technology use, which will contribute to the deployment of technology and more effective execution of earthwork.

Vast quantities of environmental pollutants from construction projects are causing significant damage to nearby local communities and thus generate environmental complaints. The construction company, responsible for compensating and resolving environmental complaints, suffers economic damages due to additional expenditures and schedule delays in construction projects. Meanwhile, the construction industry can stagnate from a broader perspective. Therefore, this study aimed to propose a framework for developing an automated management system which consists of two models for environmental complaints in construction projects: (i) the prediction model: a model for predicting environmental complaints based on factors related to environmental complaints; and (ii) the prevention model: a model for providing construction companies with the optimal prevention measure to effectively prevent environmental complaints according to the results of the prediction model. In addition, the algorithm for integrating the developed models into the management system in construction projects was proposed. Eventually, the application of the management system to construction projects can ensure the profitability of construction companies and mitigate damage from environmental pollutants to the nearby local community.

In order to improve the sustainability and smart construction, it is discussed arguably that developing and applying consistent "standard model" to plan business, design, construct and operate a building is considered to be one of the effective and efficient approach. The scope of this article is to examine, from the international developer's perspective, the "standard model" approach of a hotel brand to building projects in the UK, and also to explore potential role of project team to mitigate any local difference at the project level. These projects are developed by the same developer adopting the same business plan, design and operation to each project. In order to clarify the actual and likely difference in construction results, reference is also made to those building projects located in other geographical markets including Japan, Germany and USA, and focus is given on the analysis of its programme and cost. Principle findings are that there exists geographical difference especially in environmental and planning system, and that major local difference is found at least in the programme at the design stage. In contrast, the difference in the building cost itself may not be necessarily considered major if currency exchange rate being taken into account appropriately. It is also observed that there were cases where any difference in the programme was mitigated by taking different approach to procuring and defining roles of management and professional team at the project level. In conclusion, from the international developer's perspective, the geographical difference of the "construction system" surrounding building projects can typically lead to major prolongation of programme, however, these different construction results could be mitigated at least to a certain extent by introducing appropriate changes to the role of project team.

As eco-friendly and sustainable architecture is becoming more popular, the interest in Korean traditional wooden buildings (Hanok) has also been increasing. The building technologies of the wooden construction have been actively developed in all over the world through the diversification of new materials and construction methods. On the other hand, the growth rate of wooden construction market is still slow in Korea. In an attempt to promote the Korean traditional wooden buildings, a comprehensive research project has been conducted. This R&D project is developing standard designs, new materials, and methods for modernized Hanok including houses, public buildings, long-span structures, and even high-rise buildings. To this end, the purpose of this study is to formulate a technological strategy for popularization of modernized Hanok. Influencing variables and issues are analyzed and defined first. At the same time, the five keywords have examined in the perspective of dissemination of modernized Hanok technology. Finally, a technology road map for strategic development of modernized Hanok is proposed through casual diagrams.

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

Information on the energy consumption of buildings that can be obtained through conventional methods is limited. Therefore, this study aims to develop a model that can support decision making about building facility management through digital transformation technologies. Through the IoT sensor, the building's energy data and indoor air quality data are collected, and the monitored data is visualized through the ELK Stack and produced as a dashboard. In addition, the target building is photographed with a 360-degree camera and maps using a tool to create a 360-degree tour. Using such digital transformation technologies, users of buildings can obtain various information in real time without visiting buildings directly. This can lead to changes in actions or actions for building management, supporting facility management decisions, and consequently reducing building energy consumption.

Traditionally, road projects are initiated based on an assessment of their economic benefit, after which the environmental, social and governance effects are addressed discretely for the project according to a set of predetermined alternatives. Sustainable road infrastructure planning is vital as issues like diminishing access to road construction supplies, water scarcity, Greenhouse Gas emissions, road-related fatalities and congestion pricing etc., have imposed severe economic, social, and environmental damages to the society. In the process of addressing these sustainability factors in the operational phase of the project, the dynamics of these factors are generally ignored. This paper argues that effective delivery of sustainable roads should consider such dynamics and highlights how different aspects of sustainability have the potential to affect project sustainability. The paper initially presents the different sustainability-assessment tools that have been developed to determine the sustainability performance of road projects and discuss the inability of these tools to model the interrelationships among sustainability-related factors. The paper then argues the need for a new assessment framework that facilitates modelling these dynamics at the macro-level (system level) and helping policymakers for sustainable infrastructure planning through evaluating regulatory policies.

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.