Journal of Construction Engineering and Management

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.

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.