Journal of Construction Engineering and Management

Off-site construction (OSC) is an environmentally friendly building method with substantial benefits, yet it encounters challenges, particularly in supply chain efficiency. Current research on off-site construction supply chain (OSCSC) challenges faces issues such as dispersion, cross-disciplinarity, and timeliness. This is despite a number of studies in recent years focusing on the optimization of OSCSC to improve their performance and efficiency. However, the current literature still lacks a comprehensive summary of OSCSC problems, which hinders the popularization of OSC. To address this, improve OSCSC efficiency, and enhance OSC practicability, this paper conducted a thorough review of 244 published articles. Utilizing bibliometrics and scientometrics methods, it identified research trends, prominent researchers, and topics. Current OSCSC issues were further extracted and classified from the internal supply chain perspective. The findings reveal key issues hindering OSCSC efficiency, offering practitioners, policymakers, and researchers a nuanced understanding of current research issues. In addition, this paper provides future research directions and performance optimization recommendations, contributing to solving OSCSC challenges and exploring innovative solutions for continuous improvement in the OSCSC field.

The construction industry suffers from frequent and expensive safety accidents, significantly affecting construction project performance. Numerous data-driven classification models have been developed to categorize construction accident outcomes. While critical influencing factors provide insights for safety prevention, existing models have given less attention to the cost of accidents—an important indicator influencing management decisions. This study aims to develop accident cost prediction models that examine crucial precursors of safety accidents, offering guidance for construction safety prevention from a financial perspective. This study collected 1,606 accident reports from the Chinese construction industry between 2005 and 2022 to address this gap. Three ensemble data-driven methods, namely random forest, extreme gradient boosting regressor (XGBoost), and natural gradient boosting regressor (NGBoost) were employed to develop accident cost prediction models. Based on the performance comparison, the random forest regression model for accident cost was determined to be the best prediction model. To extract the critical attributes affecting safety accident costs, this study utilized shapely additive explanations (SHAP) value to analyze the sensitivity and influence of input variables of data-driven models. The findings showed that collapse has the greatest impact on accident costs, as indicated by the highest mean SHAP value, followed by falling from height. Furthermore, factors such as year, safety supervision, drawing, and construction plan are noteworthy in affecting accident cost prediction. Safety department, protection, and work conditions hold a slightly higher degree of influence compared to contracting arrangement, safety culture, safety supervision, training and examination, and mechanical equipment on the model output. This study provides a dimension that might be overlooked in the investigation of safety accidents in the construction industry and the insights provided by findings will contribute to the development of targeted safety accident prevention strategies.

Automation technology has experienced explosive growth in recent decades. However, the construction industry, let alone the field of construction safety, is still among the least digitized globally. Although many types of automation have proven effective in enhancing productivity and accuracy, adoption and acceptance by construction professionals are still limited. Human factors are essential for the success of automation. Therefore, this study investigated the effects of different levels of system automation and transparency, and their interactions, on human states (trust, mental workload, situational awareness) and perceived performance (explainability, satisfaction, usability). An experiment was conducted using a tool to assess scaffolding design safety with varying automation and transparency levels. A between-group design was adopted in which participants were assigned to four groups (a $2\times 2$ matrix of automation and transparency). In a multi-methods approach, human states were measured through questionnaires and electroencephalography, and system performance was measured through a questionnaire. The results indicated that (1) automation level does not have significant impact on human states or perceived system performance, (2) a highly transparent automation system is associated with significantly higher trust and better perceived system performance, and (3) the positive impacts of transparency tend to be more obvious in low-automation systems. Due to perceived complexity, one might think that transparency would be emphasized more in highly automated systems. However, the results of this study shed light on the importance of providing a highly transparent interface for explaining system logic in all automated systems for design safety assessment in construction, particularly those with low automation levels.

Project bundling is a method used by state Departments of Transportation (DOTs) and other public agencies to combine multiple construction projects into a single contract. Project bundling is typically applied when the projects share similarities in terms of type, size, and geographical location. Prior research has demonstrated cost savings and accelerated project delivery times due to this approach. However, little-to-no previous research efforts have particularly focused on developing a comprehensive guidance strategy for project bundling of various transportation projects. This paper aims to address this knowledge gap by identifying the best practices of project bundling that enable state DOTs to implement project bundling more efficiently. To achieve this goal, empirical evidence was gathered through semistructured interviews and follow-up correspondence with 16 representatives from different state DOTs and a local public agency. These interviews gathered insights into the methods and strategies used by different state DOTs to successfully bundle projects. For instance, the study discovered that agencies utilize different bundling strategies (for instance, optional-tie or mandatory-tie), as well as bundling approaches (scope-based or location-based). By qualitatively analyzing the success stories and lessons learned from these interviews, the study provides a comprehensive strategy that serves as a guide for state DOTs in navigating the project bundling for different construction projects. The main contribution of the study is the synthesis of the project bunding practices, which allows state DOTs and other transportation agencies to have a holistic approach to implementing project bundling to reap its maximum benefits.

Abstract

Various studies have shown that the strategic alignment of information and communication technology (ICT) implementation in construction organizations has a positive impact on performance that can be achieved from the organization’s project to organizational levels. Although many researchers have studied the effect of this strategic alignment on the different industries’ performance, including the construction field, the detailed effects of ICT infrastructure alignment with organizational alignment on construction organizations’ outcomes has not been investigated. The main objective of this research was to investigate whether construction organizations’ performance may be affected by the strength of the alignment between the ICT and organizational infrastructure domains. To achieve that, a self-administered quantitative expert survey questionnaire was developed and distributed to a targeted group of construction industry professionals. The survey results were then organized and analyzed using the principal component factor analysis method for variable selection, and then multiple regression analysis was used for the final results and conclusion. The research verified the analyzed data from the survey questionnaire using the qualitative research method through semistructured interviews from the New Zealand construction industry. Based on the research results, the functional integration of strategic alignment in construction organizations could optimize organizations’ performance and outcomes. The effects of this functional integration on organization performance are discussed, and recommendations for optimal integration and alignment are provided. This research enables organizations to understand the functions of ICT applications and infrastructure in enhancing organization outcomes and performance. Moreover, because the issues of strategic ICT alignment have been investigated in this study for the construction industry, this adds to the body of knowledge on new technology applications and uses in construction.

Practical Applications

In this study, the authors investigated the benefit and positive effect of information and communication technology (ICT) implementation, considering the organization’s infrastructure and project performance and outcomes. This investigation was achieved using a self-administered quantitative expert survey questionnaire that was developed and distributed to a targeted group of construction industry professionals. The survey results were then organized and analyzed using the principal component factor analysis method for variable selection, and then multiple regression analysis was used for the final results and conclusion. Thus, the survey questionnaire’s analyzed data were verified using the qualitative research method through semistructured interviews from the New Zealand construction industry. The authors found that strong congruence between the organization’s ICT and organizational infrastructure is essential in facilitating the organization’s project delivery and enhancing its outcomes. Finally, the results from this research enable the identification of less effective ICT infrastructure and can cater to more objective and project-based outcomes. Moreover, a decision support system or tool can be developed based on this research model that provides recommendations and guidance to construction organizations in terms of effective ICT implementations.

This study focuses on an important issue of the mental health of white-collar construction workers, a concern that has grown more serious following the COVID-19 pandemic. Therefore, the purpose of this study is to investigate the factors that influenced the mental health of the white-collar construction workers during the COVID-19 scenario. Based on the review of extant literature, a survey questionnaire was developed and distributed online to Indian professional construction workers, 206 responses were received, and a data set was prepared for further analysis. The data set was analyzed using exploratory factor analysis, and second-order confirmatory factor analysis and distinct stressors were identified. The most critical were lack of upskilling challenges ($\mathrm{SFL}=0.85$) and workplace-related issues ($\mathrm{SFL}=0.85$), followed by financial constraints ($\mathrm{SFL}=0.76$) and family-related problems ($\mathrm{SFL}=0.44$), both having a moderate impact. The results of the study were used to design a stress management matrix that recommends practical strategies to minimize work-related stress. By aligning with the UN Sustainable Development Goals, this research contributes to enhancing worker wellbeing and enhanced productivity of construction workers.

Abstract

In construction projects, when resources assigned to two or more simultaneous activities share the same workspace, they generate time-space conflicts associated with congestion, safety, and quality issues. The use of building information modeling (BIM) methodologies, particularly four-dimensional (4D) models, offers the possibility to facilitate the identification of these types of conflicts. Existing literature has proposed formal methods for conflict detection; however, these largely focus on automating computational analyses without providing a practical and useful procedure for planners. Moreover, previous research has not evaluated the impact of using formal conflict detection methodologies. To address these gaps, this study proposes and evaluates the use of a formal conflict detection methodology in workspaces based on 4D-BIM. The research methodology includes a first stage that develops and validates the formalized conflict detection methodology through a literature review and interviews with field professionals. The second stage quantifies the impact of using the proposed methodology through an experiment that compares the effectiveness and efficiency of a group of planners in detecting conflicts using 4D-BIM models with and without the proposed methodology. The results demonstrate that the formalized methodology enhances the planning process by increasing the detection of conflicts from 18% to 82%. Additionally, the formalized methodology reduces review time by 23% and enhances user experience. These findings contribute to improving the planning process by managing and reducing time-space conflicts in construction schedules. Consequently, the construction process will become more efficient by averting issues related to productivity, safety, and quality based on the identification of workspace conflicts. Last, the proposed methodology contributes to a better adoption of BIM by providing a structured procedure for using the 4D-BIM model to support planning processes.

Practical Applications

This study lays the groundwork for companies aiming to enhance their conflict detection processes and refine planning and conflict management procedures. The practical contributions include achieving greater effectiveness and efficiency in identifying conflicts in workspaces. The proposed formalized methodology enhances productivity, reduces safety-related risks, and prevents quality issues in building projects by managing and mitigating time-space conflicts in construction programs. Additionally, the research contributes to the enrichment of BIM application in construction activity planning. It formalizes the use of BIM through a conflict detection methodology, particularly in the analysis of construction programs and the identification of workspace conflicts. The results further strengthen the application of the BIM methodology by facilitating the design of BIM processes associated with planning, incorporating the methodology proposed in this research. Furthermore, the study encourages the adoption of BIM in construction activity planning by evaluating the positive impact of the proposed formalized methodology, providing evidence for its implementation in organizations and highlighting its potential benefits in terms of effectiveness and efficiency.

Worker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace.

The construction industry, traditionally male-dominated, faces notable turnover among women. The rise in female graduates compared with their low industry retention highlights a need for systemic changes to create a more inclusive and equitable environment. This study focuses on the attributes and experiences of women in construction and recognizes the critical need for systemic changes within the industry to foster a more inclusive and equitable environment. This study examined the personal attributes of women in construction and the organizational factors of their workplaces that influenced women’s persistence in the industry. Participants were classified as persisters or nonpersisters and responded to 101 statements categorized into 14 scales that measured personal and organizational factors identified in the available literature. Responses from persisters ($n=108$) and nonpersisters ($n=20$) were compared using multivariate analysis of variance (MANOVA), ANOVA, and logistic regression models. The study acknowledges a limitation in its statistical power due to a smaller sample size of nonpersisters ($n=20$), with a power analysis indicating 68% power for hypothesis testing. Despite this limitation, significant findings were observed, such as indicating that women with a higher degree of career resilience were significantly more likely to persist in the industry [adjusted odds ratio $(a\mathrm{OR})=6.82$; $p=0.01$], whereas women with high organizational self-efficacy were significantly less likely to persist ($a\mathrm{OR}=0.05$; $p=0.01$). These findings suggest that resilience may be an effective intervention target to increase women’s retention in the construction industry. The findings offer fresh insights and have significant implications for industry practices and policies, paving the way for more inclusive and equitable workplace environments in construction. Future research should aim to deepen our understanding of these dynamics and help develop targeted interventions to support women’s sustained participation in the construction industry.

Despite efforts to promote gender diversity, the construction industry in Australia remains one of the most male-dominated industries. Women, compared to their male counterparts, progress more slowly and encounter major obstacles to career development in the construction industry. Existing research tends to focus on broad factors that impact women’s career development in all career stages despite the fact that women’s career priorities often change as they progress through different career stages. This study applied Super’s career development model to investigate the influential factors on women’s career development across four career stages: explore (precareer), establishment (early career), midcareer, and late career. By conducting a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this research identified influential factors related to women’s career development in the construction industry, namely, attitude, capability, interest, past experiences, family responsibilities, parental influences, role models, support, culture, organizational practice, and policies and regulations. Based on the women’s empowerment framework, the identified factors were coded and synthesized into three dimensions: individual, relational, and environmental. The research finds there is a significant focus on providing relational and environmental support to women in construction, and less attention is given to individual factors. The review revealed that personal interest and parental perceptions in construction influence women’s career development in precareer and early career stages, while masculine culture significantly impacts women’s career development in all career stages. This study also summarizes the theoretical contributions and practical implications drawn from existing research on different stages of women’s career development and provides guidance for further research.

Habituation to auditory warnings is a phenomenon where an individual exposed to frequent auditory warnings responds slowly to them. Repetitive auditory warnings are triggered to prevent struck-by accidents in blind spots on construction equipment; however, these can cause habituation that may increase the likelihood of accidents. The current body of knowledge lacks any evidence quantitatively showing such phenomenon. This study aims at quantifying habituation to auditory warnings using behavioral and physiological data. In the construction equipment operation simulation developed for this study, participants pressed the brake pedal when they heard auditory warnings. Behavioral data included the reaction times (auditory warning trigger to the pedal pressing instant). In addition to the behavioral data, physiological features related to alertness from electroencephalography and electrodermal activity sensors were used to measure habituation to auditory warnings experienced by the participants. It was found that reaction time slowed down as the warnings repeated. Among the physiological features, the skin conductance level best measured habituation to auditory warnings. This study sheds light on the issue of habituation to auditory warnings in construction equipment and contributes to the reduction of the number of struck-by accidents in the construction industry.

The engagement of construction firms in corporate social responsibility (CSR) has gained strategic importance in facilitating the long-lasting transformation of the construction industry, particularly in light of the proliferation of sustainable development goals (SDGs). While previous studies have acknowledged the influence of regulative and normative pressures on CSR engagement, the impact of mimetic pressures, namely peer effects, on a construction firm’s CSR engagement remains unexplored. In the diffusion of peer effects, limited information on CSR-related activities of peer firms hampers the observation and learning from each other, where the interconnections between firms through directors serving on multiple boards come into play as a valuable resource in presenting opportunities for mutual learning and knowledge sharing. Therefore, drawing on the neoinstitutional theory, this study seeks to address this research gap by conducting a multiple regression analysis using secondary data from 35 Chinese-listed construction firms between 2010 and 2020. Specifically, this study investigates how and to what extent peer effects shape a construction firm’s CSR engagement through board interlocking ties and examines the moderating role of corporate governance mechanisms, such as ownership concentration and board independence. The results reveal the positive and significant influence of peer effects on the CSR engagement of construction firms through board interlocking ties. Moreover, the findings demonstrate that both ownership concentration and board independence serve as positive moderators in the relationship between peer effects and CSR engagement. This study contributes to the expanding body of research on the environmental, social, and governance practices of construction firms and provides novel insights into the role of board interlocking ties in promoting CSR imitation in the context of construction industry transformation. The findings not only broaden the understanding of CSR imitation among construction firms but also provide practical guidance for aligning the governance mechanisms of construction firms.

The COVID-19 pandemic has led to widespread implementation of virtual teams (VTs) in the built environment industry, transitioning the industry to virtualization. However, to our best knowledge, studies are limited on trust-building and team effectiveness of VTs compared with traditional face-to-face teams (TFTs). As such, this research aims to: 1) investigate the differences in factors affecting trust and levels of team effectiveness between VTs and TFTs; and 2) propose strategies that can effectively develop trust in VTs. To achieve these objectives, trust-influencing factors were identified under cognitive and affective dimensions through comprehensive literature reviews. Additionally, team effectiveness was assessed based on team performance and team satisfaction, followed by a structured questionnaire survey validated by experts to identify the significant factors affecting trust-building, to evaluate levels of team effectiveness in VTs and TFTs, and to assess feasible strategies to develop trust in VTs. Moreover, interviews with industry experts were conducted to validate the survey results. The results showed that the most significant factors affecting trust were competence, integrity, and relation in VTs and TFTs, although these factors showed different relative importance. Further, the factors affecting VTs were more affective-based and less cognitive-based than TFTs. In terms of effectiveness, the result showed that VTs had a higher team performance level, while their team satisfaction levels were lower than TFTs. The top-three strategies that could assist trust-building in VTs were proved to be sharing of personal interest and information, ice-breaker exercises, and face-to-face meetings when possible. The findings of this study can serve as a guide to improve the trust and effectiveness of VTs in the built environment industry, providing a valuable reference for organizations moving toward more digitalization and virtualization of their team meetings.

Understanding the antecedents of construction workers’ unsafe behavior is significant for developing effective interventions. Many studies have suggested that hazard perception failure influences workers’ unsafe behavior. However, the relationship between hazard perception failure and workers’ unsafe behavior remains anecdotal and has yet to be empirically examined. Furthermore, the mechanism of hazard perception failure affecting workers’ unsafe behavior is also unclear. To fill these gaps, this study breaks down hazard perception failure into attention failure, hazard recognition failure, and risk perception failure, and examines how these three types of hazard perception failure interact to influence workers’ unsafe behavior. Laboratory experiments combining eye-tracking and thinking-aloud were conducted to collect data. And then, regression analysis was employed to test the research hypotheses. The results demonstrated that: (1) attention failure ($\beta =0.292$), hazard recognition failure ($\beta =0.818$), and risk perception failure ($\beta =0.388$) have a significant positive effect on workers’ unsafe behavior; (2) attention failure has a significant positive effect on hazard recognition failure ($\beta =0.321$) and risk perception failure ($\beta =0.109$), and hazard recognition failure has a significant positive effect on risk perception failure ($\beta =0.392$); and (3) attention failure has an indirect effect on workers’ unsafe behavior through hazard recognition failure (0.260) and risk perception failure (0.040). The primary contributions of this study include: (1) providing empirical evidence to support the relationship between hazard perception failure and workers’ unsafe behavior by validating the significant positive effects of the three types of hazard perception failure on workers’ unsafe behavior; and (2) providing insights into the mechanism of hazard perception failure affecting workers’ unsafe behavior by revealing how the three types of hazard perception failure interact to influence workers’ unsafe behavior.

Drones are becoming indispensable in emergency search and rescue (SAR), particularly in intricate urban areas where rapid and accurate response is crucial. This study addresses the pressing need for enhancing drone navigation in such complex, dynamic urban environments, where obstacles like building layouts and varying wind conditions create unique challenges. Particularly, the need for adapting drone autonomous navigation in correspondence with dynamic wind conditions in urban settings is emphasized because it is important for drones to avoid loss of control or crashes during SAR. This paper introduces a pioneering method integrating multiobjective reinforcement learning (MORL) with a convolutional autoencoder to train autonomous drones in comprehending and reacting to aerodynamic features in urban SAR. MORL enables the drone to optimize multiple goals, whereas the convolutional autoencoder generates synthetic wind simulations with a substantially lower computation cost compared to traditional computational fluid dynamics (CFD) simulations. A unique data transfer structure is also proposed, which fosters a seamless integration of perception and decision-making between machine learning (ML) and reinforcement learning (RL) components. This approach uses imagery data, specific to building layouts, allowing the drone to autonomously formulate policies, prioritize navigation decisions, optimize paths, and mitigate the impact of wind, all while negating the necessity for conventional aerodynamic force sensors. The method was validated with a model of New York City, offering substantial implications for enhancing automation algorithms in urban SAR. This innovation enables the possibility of more efficient, precise, and timely drone SAR operations within intricate urban landscapes.

This paper proposes a hybrid conceptual and performance assessment integrated dispute risk system that enables construction professionals to evaluate, track, and manage construction disputes. It establishes a correlation between dispute-contributing factors and their respective preconstruction stages, facilitating early dispute detection and mitigation before the construction phase. This method can be applied to gauge the effectiveness of the preconstruction phase in relation to its influence on the emergence of disputes. Through an extensive literature review, expert interviews, and the Delphi technique, 48 key risk factors were identified and categorized into four groups. Eighteen experts were selected using purposive sampling, and their survey responses were checked for normality and reliability using the Shapiro–Wilk test and Cronbach’s alpha. The Delphi method was employed to reach a consensus among the panelists after two rounds of evaluation, using the nonparametric statistical parameters, such as Kendall’s coefficient of concordance and chi-squared. Subsequently, the interrater agreement (IRA) was measured, and the dispute factors were ranked. Based on the ranking results, a novel integrated hybrid system was formulated that synergistically combines a hybrid conceptual and performance assessment framework with a fuzzy logic–structural equation model (SEM). The proposed hybrid system includes two key components: a unique conceptual and performance assessment framework and a hybrid model that combines fuzzy logic and SEM. This combined approach captures uncertainty and ambiguity in assessing dispute risks related to probability and severity, providing a more precise evaluation of disputes in construction projects. Additionally, the introduction of new dispute categories specifically targeting the preconstruction phase proactively deals with potential disputes before the construction begins. This advanced integrated hybrid system can be an effective preconstruction assessment tool for evaluating and managing various dispute factors, enabling project managers to proactively assess, manage, and mitigate construction disputes between project parties.

Due to the progress in light detection and ranging (LiDAR) technology, the collection of road point cloud data containing depth information and spatial coordinates has become more accessible. Consequently, utilizing point cloud data for pavement distress detection and quantification emerges as a crucial approach to improving the precision and reliability of road maintenance procedures. This paper aims to automatically detect and visualize pavement distress using LiDAR, deep learning-based 3D object detection method, and building information modeling (BIM). A pavement distress data set is first established using the point cloud data obtained from LiDAR. Then, the 3D object detection network, namely PointPillar, is employed for pavement distress detection, and the detection results will be quantified at a region-level. Finally, pavement BIM model integrating parametrically modeled distress families is built to visually manage the detected distress. After training and validating the model with the pavement distress data set, a detection performance index of recall is 78.5%, mean average precision (mAP) is 62.7%, which is better than other compared point cloud-based methods though the detection performance can be further improved. In addition, a newly untrained section of road is applied for the experiment. The detected distress is integrated in BIM environment for a visual management, providing a better maintenance guidance.

Over the last 30 years, the last planner system (LPS), as a Lean-driven production planning and control tool, has consistently demonstrated its capability to improve planning reliability and overall performance in construction projects. Recent research provides evidence suggesting that the LPS ability to do good to projects has not reached its full potential yet. In fact, it has been observed that by identifying the missing social mechanisms in its implementation process and managing more effectively the complex socio-technical nature of the LPS, its implementation effectiveness is improved. However, commonly used research approaches, such as the case study method, have been criticized for lacking scientific control, replicability, and external validity. Alternatively, immersive virtual reality (IVR)-based simulation games have the methodological capability to enable the study of the social mechanisms that the LPS engenders when implemented in projects, by providing a highly controlled, reproducible, and ecologically valid experimental environment. However, how to leverage effectively IVR-based gaming technology to study the social mechanisms that the LPS engenders in construction organizations is not well understood. In order to bridge this gap, this paper established the conceptual and technical foundations to set IVR-based experimental environments and developed an IVR prototype encompassing nonlean and LPS-based rounds. The evaluation of the IVR prototype involved both quantitative and qualitative research approaches, focusing on its functionality, user-perceived presence and usability, task performance, and lean expert feedback. The results demonstrated the prototype’s capability to create a controlled experimental environment while offering participants a user-friendly, immersive, and realistic LPS simulation experience. The contributions of this study are twofold: (1) the addition of a new method to the research methodology toolbox in construction engineering and management; and (2) the generation of design insights to develop IVR prototypes for research in construction engineering and management, and preliminary evidence for its ecological validity.

Abstract

Bridge network maintenance and rehabilitation activities are crucial to prevent bridge failure from human-made and natural hazards such as deterioration, overload, collision, fire, flooding, and earthquake. Under a limited budget, understanding the bridge closure cost to social-economic-environmental aspects can help prioritizing bridge network maintenance and rehabilitation in a cost-benefit justifiable manner. In this study, the costing method is applied to calculate social-economic-environmental costs incurred due to detour routes with additional travel time and distance. In addition to the commonly used cost factors of time value cost and vehicle running cost, this study also uses probability theory to calculate the expected consequence costs of delayed rescue services of ambulance, police, and fire-fighting caused by bridge closure. A case study with a bridge network is used to demonstrate the methodology adopted and outcome benefits from study. The results of case study show that the time value cost of vehicle passengers, vehicle running cost, and ambulance delay cost are the major cost contributors. The bridge importance ranking and consequence cost of bridge closure can be used to provide prioritized inspection and repair program for bridge components that have a high ranking under limited budget and provide benefit estimations.

Practical Applications

In the real asset management of bridge networks, information on consequences of bridge closure is essential to bridge maintenance and rehabilitation (M&R) planning. For example, if the daily cost of bridge closure can be estimated particularly as a monetary value, the funding application for bridge M&R can be easily approved because it can show the M&R cost is lower than the benefit, which is to avoid the consequence cost of bridge closure. Furthermore, under a limited budget, bridges with a higher consequence cost of bridge closure in the bridge network can be prioritized for M&R planning. Understanding the consequence cost of bridge closure can also help in developing consequence-mitigated planning during actual bridge closures for planned repair. This study developed a costing methodology to estimate the daily consequence cost of bridge closure as a monetary value in terms of social-economic-environmental impacts. The results show that the time value cost of vehicle passengers and vehicle running cost are the most important contributors to the total consequence cost for bridges with available detour routes for crossing. For bridges with no alternative crossing routes, ambulance delay cost is the major contributor, followed by delay cost of police and fire-fighting services.

Intrusion behavior in hazardous areas is one of the major causes of construction safety accidents including falls from height and strikes by objects. Implementing automatic and preassessment of intrusions to enhance safety performance is of great importance in construction areas. Traditional behavioral safety management mainly relies on manual observation, which makes it difficult to accurately identify detailed changes in behavioral posture, while the results of risk analysis are susceptible to bias due to subjective factors. The emergence of artificial intelligence techniques and computer vision has provided new solutions for human behavior detection in recent years. Accurate vision-based skeleton extraction helps capture detailed behavioral information. Current studies generally focus on intrusion after the occurrence and rarely select metrics considering complex human motion features. It is difficult to accurately assess the potential intrusion risk, resulting in inefficient ex-ante safety management outcomes. This paper presents a novel intrusion assessment approach by integrating human kinematics to extract risk indicators and apply objective assessment methods for risk quantification. An indoor experiment with control groups was conducted by employing skeleton detection technology with safety knowledge to demonstrate its feasibility and effectiveness. The risk levels of the different activities were compared through a control group experimental analysis. The results show that a satisfying accuracy of intrusion assessment can be achieved for different workers. Appropriate warning and intervention methods can be implemented to mitigate the occurrence or reduce the severity of intrusions, thus reducing safety incidents on construction sites.

Abstract

Low back pain (LBP) is prevalent among construction workers, which causes many LBP sufferers, and consequently leads to corresponding economic and skilled workforce losses. Back exoskeletons are verified to be effective in low back load and damage reduction, but existing devices are restricted by bulky size, heavy weight, and high price. This study proposes a lightweight active soft back exosuit to assist construction workers with overexertion prevention in heavy lifting tasks, and the actuator is innovatively based on McKibben air muscle. This exosuit prioritizes its lightweight, compacted size, flexibility, good breathability, low cost, and providing support as needed. The maximal injected gas pressure of pneumatic artificial muscle (PAM) is 0.20 MPa, and each PAM provides maximum contraction force of 116 N. A biomechanics evaluation suggests the exosuit can provide assistive moment of up to $54.08\mathrm{N}·\mathrm{m}$. Human subject testing shows significant electromyographic (EMG) signal reduction at longissimus (LG) and multifidus (MF) with the proposed exosuit, and the subject evaluation of the exosuit shows the exosuit was helpful in reducing mental and physical demand and effort. The results suggest the great potential of this exosuit to serve construction management in terms of worker LBP prevention and reducing the corresponding economic and skilled workforce losses.

Practical Applications

Work-related diseases, particularly low back pain, pose a significant burden in the construction industry, leading to substantial healthcare costs, decreased productivity, and skilled workforce attrition. The etiology of low back pain can be attributed to work-related physical postures, prolonged durations, as well as high physical effort and heavy load. Back exoskeletons have emerged as promising wearable assistive devices, mitigating the impact of these factors by providing external force to share the load borne by the lower back. However, existing exoskeletons suffer from limitations such as bulky size, heavy weight, poor adaptability, and high cost, rendering them unsuitable for construction workers. In this study, the authors draw inspiration from the contraction of human muscles and leverage compressed gas-driven artificial muscles to address the aforementioned shortcomings. The proposed exosuit for construction workers involved in lifting tasks harnesses the power of artificial muscles, providing targeted contraction force to the lower back of users. This force is activated by sensing the intent of users lifting their trunk through integrated sensors within the suit, effectively sharing a proportion of the load on the lower back. The design of the proposed exosuit prioritizes ease of use for construction workers engaged in frequent lifting tasks. By ensuring lightweight construction, compact size, flexibility, and overall device compliance, the solution proposed by the authors overcomes the limitations of existing exoskeletons. Moreover, the proposed exosuit holds significant potential in construction management, offering substantial benefits in terms of preventing lower back pain among workers and curbing associated economic losses and workforce decline.

Unsafe behavior is considered the primary cause of construction safety accidents. However, the main measures for unsafe behavior management are real-time monitoring and postevent correction, which cannot prevent unsafe behavior. Therefore, this study attempted to construct an artificial cognition approach to predict the potential unsafe behavior of workers and explain why workers engage in unsafe behaviors. First, based on the cognitive model of unsafe behavior, data on workers were collected with a questionnaire, and the cognitive model was validated. Second, the cognitive process of unsafe behaviors was analyzed using latent class analysis, and the cognitive characteristics of four types of unsafe behaviors were obtained. Subsequently, with the cognitive model of unsafe behavior as the input attribute, seven types of algorithms (gradient Boosting, random forest, naïve bayes, back propagation, K-nearest neighbor, logistic regression, and support vector machine) were used to construct artificial cognition to predict the potential unsafe behaviors of workers. The results showed that all seven algorithms performed well for prediction. Thus, artificial cognition that simulates the cognitive process of unsafe behavior is not limited to particular algorithms. Finally, artificial cognition was empirically validated in a construction project. The findings demonstrated that artificial cognition could effectively predict the potential unsafe behavior of workers and provide an explanation for why workers engage in unsafe behaviors.

An energy-saving control strategy for wheel loaders is proposed in this paper to address the issue of high energy consumption during their operation. The strategy is based on the intelligent identification of working stages, allowing for staged power matching and resulting in reduced energy consumption. Each work stage of the loader is identified by matching it to the main pump pressure waveform and actuator pilot pressure waveform. Using a sliding time window method, pressure waveforms from each working stage are subjected to feature extraction. A bidirectional long short-term memory neural network (BILSTM) algorithm is then used to establish an intelligent recognition model. Based on work stage identification, an energy-saving control strategy based on power matching is proposed for the shoveling stage of the loader, and the Grey Wolf optimization (GWO)-PID algorithm is utilized for control parameter tuning. Finally, the effectiveness of the energy-saving control strategy based on work stage identification is verified through experiments. The research results indicate that the BILSTM recognition model outperforms other models with a recognition accuracy of 96.1%. The optimal time window width is 0.6 s, and the proposed energy-saving control strategy achieves a fuel-saving rate of 6.81%. This method provides feasibility for reducing energy consumption in construction machinery and achieving energy-saving and carbon-reduction goals.

Road and highway construction projects are often a concern among surrounding communities as these projects possess significant risks to the environment, and their impacts can reach far beyond the project areas. Monitoring the environmental management plan (EMP) implementation of road and highway construction projects is challenging as it requires a high level of human resources. Thus, allowing the public to assess and report any potential misconduct in road and highway construction projects can help monitor EMP performances. This study aims to compare key performance indicators (PIs) for assessing EMP implementation in road and highway construction projects. To achieve the aim, the study objectives are to (1) determine the key PIs for road and highway construction projects, (2) develop underlying components that categorize interrelated PIs, and (3) compare the underlying components between road and highway construction projects. To achieve those objectives, this study first develops a questionnaire survey by conducting a systematic literature review and semistructured interviews with industry professionals. Throughout the process, 39 PIs for assessing EMP performances have been identified. Then, a questionnaire survey was developed using the PIs and distributed to environmental professionals, collecting 111 valid responses. Finally, the data for the study were analyzed using mean score ranking, factor analysis, and agreement analysis among the two project types. This study has identified 18 and 21 key PIs for assessing EMP implementation in road and highway construction projects. The key PIs can be categorized under four underlying components for both types of construction projects: geological, pollution, environmental change, and ecological for roads, and pollution, ecological, public safety, and geological for highways. This study adds to the environmental management literature by comparing potential PIs for road and highway construction projects, potentially helping policymakers and industry practitioners develop public assessments for EMP implementation of road and highway construction projects.

The modular construction industry struggles to implement the modularization strategy appropriately due to a poor understanding of the differences between stick-built and modular construction methods and unique modular challenges. Among many modular challenges, transporting prefinished volumetric modules is one of the underinvestigated key challenges—the conventional methods for module transportation [e.g., truck, self-propelled modular transporter (SPMT), and so on), although fitting for projects comprising multiple modules, are not ideal for projects with fewer modules and limited jobsite space. This study investigates hydraulic jacks and trailer bogie as the potential alternative transporting and module lifting/offloading methods by examining how the prefinished volumetric housing module is affected during the lifting, transporting (on-road and jobsite), and offloading phases of transportation. A sensor was installed on the volumetric housing module to record transportation-induced shocks in front-to-back tilt and left-to-right roll, deformation, and impact. The peaks and lows recorded for tilt and roll during each transportation phase were 42, 57, 1, and 0, and 346, 70, 6, and 14, respectively. The results indicated that impacts to the module were more directly associated with left-to-right rolls than front-to-back tilt, because high rolls primarily reflected the module making wide turns, whereas no deformation or impact was recorded. This study contributes to the body of knowledge by highlighting the hydraulic jack and trailer bogie as potential alternatives to the conventional crane and truck under certain conditions through the assessment of the transportation-induced impact on the prefinished volumetric housing module.