Journal of Construction Engineering and Management

Reducing cost is a critical objective for project teams. However, unlike the research on topics such as cost overruns, cost reduction literature is limited. Previous studies have primarily examined cost estimation and reduction models or high-level cost reduction strategies. Therefore, it is necessary to identify specific actions that project teams can take to reduce cost. By focusing on the “cost drivers” of infrastructure projects, researchers have a practically relevant lens through which cost reduction can be promoted and studied. Thus, this integrative literature review establishes a foundation for infrastructure project cost drivers knowledge, addressing three research questions revolving around deriving a definition, classification scheme, and conceptual framework of cost drivers. This paper first produces a definition of cost drivers by critically analyzing cost driver concepts and perspectives and existing definitions. Second, cost drivers were drawn from relevant articles and classified to produce a taxonomy of 14 key cost drivers. Third, a conceptual framework (two-by-two matrix) of cost driver types was developed to acknowledge the influence project teams can have. Finally, a research agenda proposes further investigation in cost drivers and reduction research regarding each cost driver, apt research methods, and necessary project contexts. This paper’s theoretical contribution is a deepening of infrastructure project cost drivers knowledge using a definition, taxonomy, and conceptual framework. The practical contribution is a deepened awareness of the key opportunities for—and threats to—cost reduction for project teams, as well as knowledge of those cost drivers that they actually have an influence over, that is, to reduce their project’s cost.

The contractor–subcontractor relationship is evolving from a traditional structure to a collaborative and partnering arrangement, leading to subcontracting project success and relationship stability as dual goals. Subcontracting project success denotes a good performance assessed by cost, time, quality, and collaboration satisfaction, whereas relationship stability implies a tendency of long-term orientation toward the dyadic relationship. However, dependence asymmetry, which refers to the different dependence magnitude levels between general contractors and subcontractors, hinders such dual goals. This article collected 223 questionnaires from subcontractors to investigate how to reduce the negative effect of dependence asymmetry on subcontracting project success and relationship stability. Meanwhile, this article employed the fairness perception as the psychological mechanism to aggregate such negative effect and took an explicit contract as an efficient moderator to reduce it. Results confirm the context-sensitive nature of fairness perception, showing that distributive fairness perception and procedural fairness perception mediated the relationship between dependence asymmetry and two dependent variables (subcontracting project success and relationship stability), whereas the effect of interactional fairness perception was not significant. Moreover, an explicit contract weakens the negative impact of dependence asymmetry on distributive fairness perception and procedural fairness perception, while it does not regulate the impact of dependence asymmetry on interactional fairness perception. Theoretically, this article clarifies the necessity of emphasizing long-term relationship development between general contractors and subcontractors in construction. Furthermore, this article extends dependence asymmetry research in contractor–subcontractor collaboration by adding both the psychological lens of fairness perception and a restraining moderator of explicit contract. Practically, the findings help project managers to identify dual means of explicit contract and psychological perception to balance the degree of dependence asymmetry and prompt collaborative performance.

Vision-based sensors have been widely used in reality capture and the corresponding scene understanding tasks such as object detection. Given the increasing complexity of built environments, geometric features from the raw scanning data can become too vague for effective object detection. One example challenge is stacked object recognition, i.e., the segmentation, detection, and recognition of objects being stacked together with similar geometric features or occlusions. Previous methods propose to use high-resolution sensors to capture more detailed geometry information to highlight the boundaries between adjacent objects, which increase the deployment cost and computing needs. This paper proposes a novel data augmentation and voting method for stacked object detection with only low-cost sparse sensors. Several locomotion strategies were used to focus on filling the gaps of the sparse light detection and ranging (LiDAR) sensor. A modified LiDAR odometry and mapping (LOAM) method was used to register and augment raw point cloud data from multiple scans in real time. Then a voxel-based density voting method was applied to centralize the points in enhanced scan for a more accurate clustering. Finally, the clustered points were grouped and applied to generate three-dimensional (3D) bounding boxes for object boundary identification. A pilot test was performed to show the improved results of the proposed methods. A series of benchmarking studies were also performed to identify the minimum acceptable density level for the proposed method.

Previous research has recognized that the successful realization of megaproject social responsibility (MSR) relies heavily on the cooperation of a variety of stakeholders. However, the process of stakeholders conducting MSR exchanges reciprocally to achieve MSR goals has not been fully revealed, hindering the consistent implementation of interorganizational and cooperative MSR. To bridge these gaps, based on social exchange theory (SET), stakeholder theory, and network theory, this study used the stakeholder value network (SVN) to unravel MSR exchanges with multiple types and their contribution to social value. Given that, we can identify the critical stakeholders, MSR exchanges, and types to facilitate achieving social responsibility goals in megaprojects. A qualitative MSR network was first developed based on seven stakeholders with 77 MSR flows involving four MSR types, which was further quantified through a survey of 216 managers involved in 133 megaprojects. The results demonstrate that when owners act as the focal organization, the government and the general contractors act as critical external and internal stakeholders to promote reciprocal exchanges in the MSR network. Eleven critical MSR exchanges (i.e., five restricted exchanges and six generalized exchanges) are identified to facilitate the reciprocal and joint implementation of MSR to obtain high megaproject social values. It is also suggested that stakeholders should pay particular attention to political MSR to guarantee a stable environment for reciprocal MSR exchanges among primary stakeholders. This research intends to guide the efforts of stakeholders to engage in effective MSR to satisfy their partners mutually, contributing to realizing high megaproject social value.

Abstract

Cost overruns (COs) in public sector social housing programs (PSSHPs) will continue unabated without immediate intervention to resolve the ad hoc amalgamation of root cause ontologies. This study applied a fuzzy synthetic evaluation (FSE) multicriteria decision support model to evaluate and prioritize the overall cost overrun risk in PSSHPs. In a Caribbean Small Island Developing State (SIDS) context, a questionnaire survey measuring 41 perceived critical risk factors (CRFs) was used to derive the risk impact and normalized impact values from 150 professional participants. Twenty-two normalized CRFs and four principal risk groups’ (PRGs) membership functions were determined using fuzzy synthetic knowledge-based inference procedures. Politically linked contractors and intentionally inferior building contracts caused most PSSHP cost overruns. The FSE risk model reveals that political PRG is the major root cause of COs, followed by socioeconomic, technical, and psychological PRGs. The study found that the overall risk level (ORL) of PSSHPs is high, meaning that government investment in public housing is a high-risk endeavor and ill-informed choices could lead to “wicked” social problems. Defuzzifying cost risks provided deeper insights into the nature and scope of the risk and related management implications that lead to unsustainable decision-making practices and provide opportunities for adequate control strategies to maintain economic viability. This research evidenced the fusion of different CO ontologies and raises awareness of the hidden political risk factors that should be prioritized to achieve sustainable PSSHP decision-making practices.

Practical Applications

This study adds to the PSSHP’s cost overrun knowledge by merging known root causes and criticalities into a practical risk model that gives empirical risk values at several tiers, from individual critical factors to root causes and the overall risk assessment. Many theories and ontologies explain CO causes (e.g., political root causes grounded on agency theory and relativism ontology, psychological root causes based on prospect theory and relativism ontology, and economic root causes based on transaction cost theory and realism ontology). Subjectivity and a growing divide between top researchers and practitioners result from the ad hoc amalgamation of these ontologies. The study expands the theoretical foundations of CO research by tackling the ontological issues through a structured, methodical grouping of root causes based on their ontologies. The defuzzification method applied in this research eliminated subjectivity and bias in SIDS risk assessment. Therefore, construction experts can use this research to identify strategically hidden risks, analyze their effects on the project and society, and design control strategies to limit their economic impacts. This pragmatic tool can be used in the tendering and contract award processes and across the supply and logistic chain to optimize project cost performance and benefit society with value-driven, circular economy–friendly social projects.

Abstract

A building lifecycle consists of multiple domains, and building information modeling (BIM) applications involve cross-domain knowledge exchange. In the knowledge spiral theory proposed by Nonaka, externalization facilitates knowledge exchange in designing cooperation, and a model library (ML) contains the necessary information for the architecture, engineering, and construction (AEC) industry. However, current MLs are centralized systems that are vulnerable to data tampering and copyright disputes, and the provided models may not match the requirements of current building projects. Therefore, this article develops a blockchain-based parametric ML (BBPML) for BIM collaboration to realize knowledge sharing. The parametric model in our ML can be flexibly applied to a project by adjusting the parameters. A blockchain network provides reliable storage and ensures the upload sequence. The research creates a website based on the proposed system through Ethereum and applies it to a curtain wall project.

Practical Applications

This article applies the nonaka knowledge spiral theory to improve building information modeling (BIM) cooperation. A blockchain-based model library (ML) is proposed to facilitate knowledge sharing in BIM projects. The system records the building knowledge through a blockchain network to ensure the building knowledge is immutable and quickly traceable in the upload sequence. Our approach has been practiced in a physical curtain project and allows for better BIM collaboration.

Due to the capacity deficits of the healthcare sector, governments faced many difficulties when responding to the burden caused by the COVID-19 pandemic. Private sector involvement in healthcare facilities delivery enabled through public–private partnerships (PPPs) can play a crucial role in overcoming these capacity deficits. Although PPP projects can help to overcome infrastructure deficiencies, some are criticized due to the low value created for stakeholders. These include not only society in general, which uses the healthcare facilities, but also the project participants. Value generation for the latter is also an essential objective of PPP healthcare projects in a generic view, which is often neglected. This study aims to understand value creation in healthcare PPPs by developing a framework based on concepts related to the project value and eventually proposing a business model to maximize the generated value at the organizational level for all stakeholders. In order to achieve the study objective, insights are obtained from a literature survey, semistructured interviews, and focus group discussions. A value-creation model consisting of components and subcomponents of value generation and the relationship between these is proposed. The model demonstrates that the process component plays a central role in value creation, requiring sufficient assets of the parties engaged in and the appropriate governance of PPP projects. The value-creation model proposed here further highlights the driving role of a well-balanced legal framework and organizational learning. All the identified subcomponents are found to be interrelated, highlighting the importance of the proposed business model design in value creation. Overall, given the lack of healthcare facility capacity in many countries and the need to create value in healthcare PPP attempts, the proposed value-creation model is expected to help project participants develop a more value-centric way of managing, controlling, and operating these projects.

Environmental collaboration between organizations involved in construction projects enables the efficiency of environmental management to gain environmental sustainability. Yet, in many projects, this collaboration is gamed promoting contractor greenwashing behavior, thereby diminishing the effectiveness of environmental management. What is unclear are the underpinning mechanisms to concurrently increase environmental collaboration and decrease contractor greenwashing behavior in construction projects. We used an integrated theoretical framework based on social exchange theory and transaction cost economics to evaluate the potential linear, curvilinear, and combined influence of interorganizational trust and formal contracts on environmental collaboration and contractor greenwashing behavior. Drawing evidence from questionnaire surveys, we find that two categories of interorganizational trust yield positive impacts on environmental collaboration and different curvilinear impacts on greenwashing behavior. Two categories of formal contracts exert an inverted U-shaped effect on environmental collaboration and heterogeneous effects on greenwashing behavior. We also find that formal contracts negatively moderate the effects of interorganizational trust on environmental collaboration, and interorganizational trust negatively moderates the impact of formal contracts on greenwashing. We provide novel insights into the interorganizational governance mechanisms regarding greenwashing in construction projects relevant for construction managers concerned with the environmental efficiency-effectiveness.

Perceived leader–member exchange differentiation (PLMXD) refers to the degree to which workers perceive that their exchange relationships with their leaders within a group differ. Although PLMXD has been demonstrated to be critical to project teams’ safety management, there is limited evidence on the relationships between PLMXD and construction workers’ safety citizenship behavior (SCB), including proactive and prosocial safety behavior. Based on leader–member exchange theory and social information processing theory, this study investigates organizational identity and felt safety responsibility as mediating mechanisms that link PLMXD to proactive and prosocial safety behaviors. In total, 320 valid data points were collected from construction workers from six construction projects. The results reveal that PLMXD negatively and indirectly affects proactive safety behavior by reducing organizational identity and felt safety responsibility. Moreover, felt safety responsibility mediates the negative association between PLMXD and prosocial safety behavior. By addressing the research gap in the relationship between PLMXD and SCB, this study enriches the body of knowledge on safety management in the construction industry. The findings extend SCB research by elucidating two distinct processes by which PLMXD affects SCB. By fostering a supportive organizational environment, organizations can empower workers to take proactive safety actions and engage in prosocial safety behavior, ultimately improving overall safety performance on construction sites.

Abstract

Inspecting the number of rebars in each column of a reinforced concrete (RC) structure is a significant task that must be undertaken during the rebar inspection process. Conventionally, counting the rebars has relied on a manual inspection carried out by visiting inspectors. However, this approach is very time-consuming, labor-intensive, and poses a potential safety risk. Previous studies have focused on the applications of counting the rebars for a production line and/or warehouse, using vision-based methods. Therefore, this study aims to propose an innovative approach incorporating the use of an unmanned aerial vehicle (UAV) on real construction sites to count the rebars automatically. For analyzing the images, robust object detection methods based on deep learning (Faster R-CNN, R-FCN, SSD 300, SSD500, YOLOv5, and YOLOv6) were developed. A total of 384 models generated from six different methods were trained and implemented using data sets based on the original and augmented images with adjustments made for the hyperparameters. In a test, the best optimized model based on Faster R-CNN produced an accuracy of 94.61% at AP50. In addition, video testing demonstrated a coverage of up to 32 frames per second in the experimental environment, suggesting that this method has potential for real-time application.

Practical Applications

Drones provide an efficient way to monitor the number of rebars in reinforced columns by capturing still images or video footage. However, manually counting the rebars from this data in the form of images is both time-consuming and laborious. This research therefore develops an AI-driven technique, based on deep learning, designed to automate the process. In the experiment, the approach that was developed achieved an accuracy rate of 94.61% under diverse conditions on real construction sites, including nonuniform illumination and complex backgrounds (e.g., scaffolding and molding). Nevertheless, there is potential for further improvement in certain scenarios (e.g., where there are shadows in high-illumination images, or similar objects close to the rebars). In addition, video testing demonstrated that the system could process up to 32 frames per s. Despite its limitations, the method developed in this research could be put to practical use on construction sites, except in those scenarios where it showed a lower rate of accuracy. Moreover, as 30 frames per s is often regarded as equivalent to real-time, it would also be feasible to use it for video analytics’ applications such as real-time monitoring and progress tracking.

Abstract

Accurate house price prediction allows construction investors to make informed decisions about the housing market and understand the growth opportunities for development and the risks and rewards of different construction projects. Machine learning (ML) models have been utilized as house price predictors, reducing decision-making costs, and increasing reliability. To further improve the reliability of the existing predictors, this study develops a hybrid multiedge graph convolutional network (GCN) that considers the various relationships between house price records. The developed hybrid multiedge GCN receives richer input from the multidependency information and thus provides a more reliable prediction that accounts for price changes based on the neighborhood, building age, and number of bedrooms. Compared to other ML approaches, the developed multiedge GCN house price predictor displayed good prediction accuracy while providing valuable insights into the factors that affect the house price, such as the desirability of different neighborhoods and building age.

Practical Applications

In the context of construction management and property valuation, the multiedge GCN model introduces an enhanced level of reliability for house price prediction. It stands out with its improved interpretability, rooted in its ability to maintain the inherent structure of the house price data set. This added transparency provides professionals with a more profound understanding and trust in prediction outcomes. By encompassing the richer content of the house price data set that includes the multidependency information, the model presents a comprehensive view of house price data sets, facilitating a more accurate and thorough understanding of housing market patterns. As a result, the GCN model matches the accuracy of other ML models while providing greater interpretability and transparency. This model’s capabilities are expected to arm investors, contractors, and policymakers with valuable insights, aiding informed decision-making. It is also envisaged as a beneficial tool for construction project owners and contractors in refining budgets and informed investment decisions. The synthesis of transparency, representativeness, and accuracy makes this model a dependable tool for construction managers to make informed decisions, ultimately enhancing their operational efficacy.

Abstract

Transaction costs of subcontracting are considered hidden costs due to unobservability and immeasurability, which potentially jeopardize project performance. This study develops a framework of transaction costs in construction projects [i.e., prevention-oriented costs (monitoring costs and coordination costs) and rectification-oriented costs (renegotiation costs)]. Furthermore, this study explores the impacts of the subcontractor dispersion on transaction costs and also provided a holistic view of the relationships by investigating the contingent role of different attributes, including project, relational, and institutional attributes. Using empirical evidence obtained from 283 professionals employed by Chinese general contractors, this study finds that a higher level of the subcontractor dispersion leads to more coordination and monitoring costs but fewer renegotiation costs. In addition, the contingent effects of different attributes proffer insights into the underlying mechanisms of how different transaction costs rise in a subcontracting relationship. Coordination costs occur due to information processing requirements, monitoring costs rise from the worry of shrinking, and renegotiation costs come from the relative power between parties. This study highlights the subcontractor dispersion as an important component of the subcontracting arrangement that has been long ignored and reveals the potential trade-off between different types of costs when determining the distribution of subcontracted work among subcontractors. Besides, the moderation analyses demonstrate different features and sources of each type of transaction costs, confirming the significance of the framework both theoretically and empirically. This study reminds project managers of the hidden costs that are frequently underestimated and the contingency analyses help them recognize effective domains for different subcontracting arrangements.

Practical Applications

This study elucidates the significance of hidden costs in construction projects and the impact of subcontracting arrangements on these costs. By comprehending the expenses associated with subcontracting engagements, managers can adequately account for these expenditures when evaluating subcontracting strategies from a financial perspective. Such additional expenses may include maintaining a larger retained management team, designing a more complete formal contract, establishing a more elaborate performance monitoring and evaluation program, and facilitating more extensive information systems integration among participants. The findings remind project managers of the trade-offs between different types of hidden costs in different contexts and help them recognize effective domains for distinct subcontracting arrangement. For example, in projects in a country with weak legal enforceability, the general contractor must consider the escalating monitoring costs when deciding to subcontract to additional parties. In such case, a low level of subcontractor dispersion may prove more advantageous. Conversely, when the subcontractors’ market is less competitive, using a limited number of subcontractors may become more challenging since the hold-up hazards will be further exacerbated. Factors such as project complexity, uncertainty, and relational ties all contribute to transaction costs, which should be considered in choosing subcontracting arrangements.

Mental health in the construction industry has gained increasing attention in academic research. However, previous studies have largely focused on selective mental health issues, thus limiting a comprehensive understanding of the theoretical underpinnings in this field of study. This study presents a systematic review of previous studies on mental health in the construction industry, focusing on explanatory theories and identifying the cognitive and behavioral processes that affect mental health in the construction industry. A three-stage approach was employed to identify 163 relevant articles that met the inclusion criteria for the review. Following the grounded theory, this review synthesized theories and findings to develop a model explaining the cognitive and behavioral processes affecting mental health among construction workers. The review revealed that work and life demands and resources derived from strain theories and work–life balance theories had been the focus of previous research. Meanwhile, knowledge of coping mechanisms had generally been understudied. By integrating motivation and masculinity theories, the proposed model extends the understanding of coping mechanisms consisting of coping belief, coping motivation, and coping behavior, illustrating how masculine norms in the construction industry influence coping responses for mental health. The model lays a solid theoretical foundation for a comprehensive understanding of how cognitive and behavioral processes affect mental health in the construction industry, thus providing a valuable reference for future research. Theoretical and practical implications and future research directions are also discussed.

Vision-based methods for action recognition are valuable for supervising construction workers’ unsafe behaviors. However, current monitoring methods have limitations in extracting dynamic information about workers. Identifying hazardous actions based on the spatiotemporal relationships between workers’ skeletal points remains a significant challenge in construction sites. This paper proposed an automated method for recognizing dynamic hazardous actions. The method used the OpenPose network to extract workers’ skeleton information from the video and applied a spatiotemporal graph convolutional network (ST-GCN) to analyze the dynamic spatiotemporal relationships between workers’ body skeletons, enabling automatic recognition of hazardous actions. A novel human partitioning strategy and nonlocal attention mechanism were designed to assign appropriate weight parameters to different joints involved in actions, thereby improving the recognition accuracy of complex construction actions. The enhanced model is called the attention module spatiotemporal graph convolutional network (AM-STGCN). The method achieved a test accuracy of 90.50% and 87.08% in typical work scenarios, namely high-altitude scaffolding scenes with close-up and far views, surpassing the performance of the original ST-GCN model. The high-accuracy test results demonstrate that the model can accurately identify workers’ hazardous actions. The newly proposed model is inferred to have promising application prospects and the potential to be applied in broader construction scenarios for on-site monitoring of hazardous actions.

Workers’ motion trajectories or spatial tracks on construction sites contain useful safety-related information. Existing safety management research on worker trajectory typically analyzes the interactions between worker motion trajectory and risk sources. The historical accident-free zone of a group of workers is a reflection of the potential safety zones on a construction site. Few studies have investigated construction workers’ trajectory safety risks from the integrated perspectives of group and hazard sources. Therefore this study developed a novel and integrated safety risk diagnosis method combining hazard source and group movement distributions to fully utilize the phone Global Positioning System (GPS) trajectory information of construction workers. The proposed method diagnoses workers’ risk exposures by considering workers’ trajectories in unsafe and safe areas. In addition, the method uses expert confidence and comprehensive decision indexes to determine the feature weights. Furthermore, the proposed safety risk diagnosis method adopts the grey optimization Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) diagnosis model to diagnose workers’ risk management priorities and behavior adjustment direction. An actual project site was used to assess the performance of the proposed diagnostic method. The results show that the developed method provides a quantitative means for project managers to measure workers’ spatial-temporal risk exposure, diagnose safety risks, and plan for safety controls. The proposed integrated method provides a new perspective to make full use of workers’ trajectory information and helps to provide practical and specific data-driven safety guidance for construction managers and workers.

Abstract

Nowadays, competition in the international contracting market is becoming increasingly fierce, and knowledge has become one of the most important resources for international construction projects (ICPs). Academia and industry have gradually realized that members’ individual knowledge should be integrated into the project work process by interacting with different individuals, which is defined as the process of project knowledge transfer. However, there is still a lack of concrete and complete understanding of how factors from each dimension (i.e., individual, team, and communication) integrate to affect knowledge transfer within ICPs. The purpose of this research is to examine the key drivers that affect knowledge transfer within ICPs from both individual and team perspectives. Data were collected from 353 samples of 87 international construction projects from people who were participating or had participated in the projects. The results of structure equation modeling (SEM) analysis suggested that cultural distance among project members hinders the willingness to transmit and receive knowledge, whereas members’ prior stock of knowledge positively influences their transmittal willingness. Then, intercultural competence and effective learning mechanism affect knowledge transfer effectiveness (KTE) by positively influencing internal communication intensity. Additionally, the degree of trust affects KTE by positively influencing members’ transmittal and reception willingness. Last, the willingness to transfer knowledge has a direct positive impact on KTE and also affects KTE by positively influencing internal communication intensity. This study provides suggestions for international project managers and members to promote KTE, which is conducive to the knowledge flow and accumulation of project teams and construction companies.

Practical Applications

This study provides several practical implications for improving knowledge transfer effectiveness within international construction projects. Project managers and members can draw upon the results to select proper strategies to promote their knowledge-transfer activities. The following recommendations can be made from three aspects according to the transfer method: From the perspective of knowledge transfer between members, members are supposed to respect others’ cultural customs while maintaining good patience in communication. Also, project teams can hire compound talents with extensive professional knowledge to decrease the effect caused by members’ different knowledge levels. Last, project teams can strengthen relationship governance and cultivate mutual trust between employees. To improve knowledge transfer from project members to the team, project managers are expected to organize an open and inclusive cross-cultural team to improve cross-cultural competence. Moreover, a complete learning system can be established through which members can report their knowledge in a timely manner to the upper organization. Last, communication channels can be expanded to make employees communicate smoothly through seminars, mentoring, report meetings, and so on. In terms of knowledge transfer from the project team to members, teams can conduct more cross-cultural training and establish a scientific training support system. Then, a complete learning system can be established that enables the superior organization to convey knowledge to members quickly and clearly. Finally, international construction projects (ICPs) are supported to create a knowledge-exchange platform in which members can upload, browse, and download knowledge.

Construction safety is a critical concern for industry and academia, and numerous models and algorithms have been developed to predict incidents or accidents to facilitate proactive decision-making. However, previous studies have been limited due to the inability to account for uncertainties because predictions are given as a single value (i.e., Yes or No) and the failure to integrate subjective judgment. To address these limitations, this research proposes a machine learning–based Bayesian framework for predicting construction incidents using interval estimates. This framework combines a state-of-the-art machine-learning algorithm with a binary Bayesian inference model to develop an incident predictor that considers a range of project characteristics and conditions. Notably, this framework also is capable of incorporating historical or subjective judgment through prior selection and outputs the unsafe event prediction as an interval of possibilities, thus accounting for various uncertainties. The efficacy of our framework was demonstrated in a real-life case study, showcasing its practical implications for proactive decision-making and risk management in the construction industry and representing a valuable contribution to the field of construction safety.

Drones have had a significant impact on the construction industry. However, their safety implications on construction jobsites are yet to be explored. This user-centered study empirically investigates whether drone presence is associated with any physiological, attentional, and emotional impact on humans, particularly those working at heights who are susceptible to higher risks of injuries and fatalities. Through a between-subject study design, recruited participants were asked to perform construction tasks in a virtual reality environment, with or without the presence of drones, to measure these impacts. Results obtained from the objective and subjective measures showed that drone presence was not associated with changes in participants’ physiological and emotional states. However, participants diverted some of their attention from the assigned task toward the drone. This study advances the knowledge of human–drone interaction in construction by informing the construction industry about the safety challenges of drone presence on individuals working at heights.