Journal of Construction Engineering and Management

Abstract

Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been slow due to the influence of several barriers. In recent years, virtual reality (VR) applications have been used to address many of these barriers and promote the implementation of OSC projects. However, a comprehensive and coherent literature review that establishes the current state and categorizes VR applications in OSC projects is still lacking. To address this research gap, this study provides a state-of-the-art review of VR applications in OSC (VR–OSC) using the scientometric and systematic review methods. This study characterizes the synthesis between VR and OSC and identifies research trends and gaps that can be studied in future VR–OSC research. The scientometric review focuses on identifying the main topics of both research domains separately and combined based on the collected articles. The systematic review, meanwhile, qualitatively evaluates these articles, highlighting the existing research gaps and anticipating future research frontiers. The scientometric results indicate that VR applications in OSC can be organized into a number of clusters, such as Crane Operations and Onsite Planning, Educational Applications, Safety and Ergonomics, and Evaluation of Design Alternatives. The qualitative analysis identifies several future research directions to advance the field of VR–OSC, including (1) multiuser VR models in crane operation planning, (2) consideration of the role of human emotions in VR safety training by adopting biometric sensors, (3) decentralized web-VR platforms for remote OSC planning, and (4) VR-solutions for modeling robotic movements in OSC factories. This study can serve as a useful point of reference for VR–OSC researchers and provides a sound foundation for future research on VR–OSC.

Practical Applications

As the market share of OSC techniques continues to grow, it is vital for construction professionals to stay up to date with the latest advances and technologies so that they are well-positioned to identify and address adoption barriers. This study provides a comprehensive analysis of the two topics (i.e., OSC and VR) both independently and integrally. The review of VR applications and their categorization into several OSC subdomains, such as crane operations management, safety management, processes planning, and educational practices, provides OSC practitioners with a holistic understanding on the basis of which to employ VR solutions more efficiently and effectively in their projects. Furthermore, the integration of VR with other technologies, such as eye-tracking technologies, motion capture sensors, and robotic tools, illustrates how VR tools can be used to address many OSC-related problems, including complex production lines, inefficient safety training, and overlapping crane operations. Finally, the improved understanding of current gaps in the domain of VR–OSC can help to galvanize industry support for more in-depth research on the use of VR in OSC.

The relationship between digital transformation (DT) and dynamic capabilities (DC) has been extensively studied in various industries; however, their implications in the construction sector are lagging. The study uses the PRISMA framework and NVivo to systematically review 63 peer-reviewed journal articles, examining how DT and related technologies like BIM, AI, and the IoT are integrated into the global construction industry. Findings reveal that while these technologies enhance operational efficiency and innovation, they face barriers such as cultural resistance, high costs, and skill shortages. The analysis highlights the essential role of DC in understanding and facilitating the effective integration of DT into construction. The paper indicates the need for future studies to develop strategic frameworks and regulatory guidelines for successful DT implementation. Further, it identifies the long-term environmental and sustainability impacts of DT as crucial areas for future research, emphasizing the importance of addressing both technological and socio-economic aspects. Additionally, it encourages stakeholders involved in the DT process to acquire the necessary skills required for technology implementation and DT.

Abstract

There is emerging evidence that negative emotions can be leveraged to help construction workers recognize hazards, with fear being closely associated with avoidance behavior among negative emotions. Previous research has indicated that decision-making during a simulated crisis differs significantly from real crisis scenarios. In order to induce fear arousal based on actual harm (referred to as real fear arousal), this study utilized the high degree of consistency between humans and mice in conducting a three-phase experiment employing a modified two-way active avoidance paradigm. The study’s key finding indicates that integrating construction elements into the modified two-way active avoidance paradigm can activate the renewal of active avoidance behavior toward shocks under conditions of fear arousal throughout the experiment. The level of fear demonstrated a significant impact on active avoidance of hazards (AAH) in the three-phase experiment.

Practical Applications

Managerial decision-making under crisis theory emphasizes the notable disparity between decision-making in simulated crisis scenarios and actual crisis situations. Consequently, effectively intervening in construction workers’ risk-taking behaviors often proves challenging. Numerous studies have established a close relationship between fear and avoidance behaviors. In order to ethically and effectively explore the influence of fearful emotions on real-life risk-taking behaviors, a three-phase avoidance experiment (comprising acquisition, extinction, and renewal phases) was conducted based on the classical paradigm used in experimental methods of animal behavior. Therefore, appropriately inducing fear arousal may enhance construction workers’ ability to avoid risk, particularly among those who have previously been injured, with potentially more pronounced effects. We advocate for a focus on enhancing the fear experience within reasonable limits during future safety training initiatives.

Abstract

Prefabrication is highly recognized in construction due to its inherent cost efficiency, quality control, and sustainability benefits. However, the presence of uncertainties in prefabrication processes hinders the realization of these advantages, necessitating addressing production time uncertainty. Mixed reality (MR) has demonstrated promising outcomes in mitigating time uncertainties as an immersive visualization and simulation technology in different domains. Therefore, this study investigates the impact of MR-based approaches in prefabrication to reduce time uncertainty. The methodology comprises Discrete event simulation (DES) modeling, correlation analysis, and the development of an MR-based immersive visualization and simulation tool. Following an initial work study to measure standard times, visualizations of 13 processes in the prefabricated panel production were developed in the Unity environment and deployed on the HoloLens 2. Through several work studies before and after MR implementations, the study demonstrated a 10.8% reduction in the total completion times (P99) in correlated production activities. Findings emphasize that MR reduces time uncertainty by providing an immersive, interactive, and dynamic approach, ultimately improving efficiency in prefabrication. The study contributes to existing knowledge by quantifying the impact of MR on completion times and underscores the importance of addressing time uncertainties for sustainable lean prefabrication. The sensitivity analysis results prioritized the reinforcement trade in managerial decision-making, emphasizing the need for MR-based approaches for targeted improvement opportunities. Minimizing scheduling uncertainty is of practical importance in realizing prefabrication benefits.

Practical Applications

This study emphasizes the practical applications of MR technology in the construction industry, specifically in the prefabrication process. By utilizing the developed MR-based approach as an immersive visualization and simulation tool, construction stakeholders can streamline their operations, reduce uncertainties, and achieve significant benefits. The MR-based approach enables real-time monitoring, analysis, and collaboration among project teams, empowering them to make informed decisions and adapt to dynamic production processes. By quantifying the impact of uncertainties and delays, stakeholders can prioritize MR-based approaches, particularly for skilled trade teams, to optimize resource allocation and improve production efficiency. Further, MR contributes to achieving lean production targets of prefabrication by optimizing resource usage, enhancing values, and reducing waste. The findings emphasize the importance of MR in managerial decision-making, providing insights that enable informed choices to maximize efficiency and productivity. The study demonstrates how MR can revolutionize prefabrication practices, making construction projects more sustainable, cost-effective, and efficient.

Abstract

Biological data-based methods for monitoring workers’ mental fatigue have become widely adopted in recent years. However, few have concentrated on the online monitoring and assessment of mental fatigue considering the complexity and high dimension of the biological data, especially for scenarios where data arrives continuously in the form of flows. This study aimed to propose an online learning model to learn model parameters according to the order of data acquisition. Specifically, the fuzziness-based online sequential extreme learning machine (Fuzziness-OS-ELM) model was proposed, consisting of two parts: (1) a data value estimator; and (2) an online mental fatigue classification model. As new data arrives, the Fuzziness-OS-ELM model can effectively identify and select samples with high data quality based on fuzziness, which are then used to continuously update the online mental fatigue classification model. A cognitive experiment was carried out to evaluate the Fuzziness-OS-ELM model. The results indicated that samples with low fuzziness corresponded to high data quality. The proposed online sequential learning model exhibited enhanced classification performance on mental fatigue. This study’s dynamic diagnostic method for identifying the onset and progression of mental fatigue can provide targeted support for precise interventions aimed at construction workers.

Practical Applications

Different from subjective and passive management of mental fatigue, this study proposed an enabling technology for minimally obtrusive monitoring and online feedback on construction workers, allowing for monitoring over extended periods of time on construction sites. With the online visualization of mental statuses, workers in special construction operations at inappropriate mental fatigue levels can be found. The ability to monitor fluctuations allows for immediate and proactive interventions, thereby addressing mental fatigue more effectively than interval-based monitoring methods. Based on the portability of the research outcomes, long-term mental status data of workers on sites can be collected. Through revealing the generation and development rules of construction workers’ psychological load, targeted industry-wide guidance will be developed and promoted.

Construction workers are exposed to long physically demanding work hours and require sufficient work rests to avoid strenuous fatigue, productivity loss, and adverse health effects. In the literature, limited efforts incorporated standardized work–rest periods in the construction schedule without accounting for the variation among the workers’ live physiological conditions. Rather than focusing on the worker’s fatigue data collection, this paper utilizes sample cardiovascular stress and energy exertion data from health-related studies, collected using wearable Fitbits. The paper then proposes a framework for the analysis and utilization of this physiological data to design optimum work–rest schedules and worker reassignment plans as two strategies to mitigate workers’ fatigue. The framework uses a constraint programming schedule optimization model that minimizes workers’ physiological strain through optimized work rests and worker reassignments. Using a hypothetical schedule with the workers’ fatigue data, the model proposed two 20-min breaks for one worker and two 5-min breaks for other workers and reassigned eight workers to different tasks. The framework helps project managers to efficiently improve the health and safety of workers and improve productivity, leading to a more inclusive work environment.

Site monitoring is indispensable for modern construction management. Contact approaches, represented by wearable devices, have problems such as privacy leaks and hindering working. Vision-based noncontact methods depend highly on light and environmental conditions, and have poor three-dimensional perception ability. To propose an all-weather noncontact activity identification approach on construction sites, four-dimensional (4D) millimeter-wave (MMW) radar is adopted in this study for the first time because of its excellent abilities of motion sensing, spatial sensing, and penetration. First, a feature processing method is proposed to convert the MMW signal to a seven-dimensional point cloud, which consists of the shape information ($x$, $y$, and $z$) and four attributes (${\mathrm{Doppler}}^{\prime }$, ${\mathrm{SNR}}^{\prime }$, $H$, and $V$), representing the information of velocity, signal-to-noise ratio, height, and volume, respectively. Second, a novel deep learning framework is developed, which contains (1) one shape subnetwork, driven by the PointNet++ model, to capture the shape information of objects; (2) four attribute subnetworks to fully utilize the additional attribute features; and (3) a two-layer fusion module to combine all the outputs of the subnetworks. With precision of 0.963, recall of 0.961, and an F1 score of 0.962, the results show that the proposed method can accurately identify construction activities under different environmental conditions. It also can facilitate further development of MMW radar–based solutions for construction site analysis.

This research presents a novel microlevel (i.e., project-level) construction productivity framework aimed at measuring and benchmarking productivity consistently within and between projects, work packages, and activities. Using a design science research approach, the framework was rigorously developed with data collected from UK-based construction projects and refined through several cycles of implementation and industry engagement. The framework is useful in providing new insights into project performance, helping understand process variability, setting benchmarks, and conducting data analytics for improved decision making. This framework can be used for industrywide measurement both within and across subsectors of construction in the UK and internationally.

Abstract

Practitioners in construction management primarily focus on two key indicators of project success: total cost and completion time. Heavy equipment and machinery play pivotal role in determining these measures, representing significant cost elements in various heavy construction projects such as road construction. Consequently, there is a pressing need for an efficient approach to determining the optimal scheduling of these heavy resources to minimize costs and shorten completion times. This paper proposes an innovative approach to address this challenge by introducing a mixed integer linear programming (MILP) model. The aim is to identify the optimal configuration for heavy equipment in earthmoving operations. The dynamic nature of the configuration process is adopted, enabling daily updates to the schedule based on the contractor’s available resources. Moreover, environmental considerations are integrated into the decision-making process, ensuring a comprehensive approach to project optimization. To demonstrate the superiority of the developed model, three case projects from the literature have been solved. The proposed model led to a significant improvement in project cost, with an average enhancement of 25%, and in completion time, with an average improvement of 50% compared with the literature case studies.

Practical Applications

This paper presents a novel MILP model designed to optimize earthmoving operations, focusing on dynamic fleet configurations and emission costs. Unlike existing models, this approach provides daily fleet setups for multiple cut and fill sites, considering the contractor’s available resources. It calculates optimal soil quantities to be moved, monitors soil levels at sites, and estimates daily trips between them. In the realm of project bidding and management, this model offers valuable insights and practical applications. It empowers project managers with a robust tool for optimizing fleet configurations during bid preparation, enabling contractors to determine the most cost-effective and time-efficient setups, enhancing their competitiveness. Moreover, the model facilitates the assessment of different parameters’ impacts, such as resource additions or equipment upgrades, on project cost and completion time. This dynamic approach enables contractors to make informed decisions during project execution, ensuring projects remain on track and within budget. Additionally, by considering emission costs, the model aligns with the growing emphasis on sustainability in construction projects. Optimizing fleet configurations to minimize emissions not only reduces environmental impact but also helps contractors comply with stringent regulations and meet client demands for eco-friendly practices.

Abstract

The architecture, engineering, and construction (AEC) industry in the US faces increasing labor shortages while accumulating downward productivity trends. Previous research efforts have shown that lack of timely access to information is one of the main factors hindering construction workers’ productivity, and prompt information extraction (IE) from BIM sources can help address this issue. This study develops a BIM-web interoperable system, featuring a voice-based intelligent virtual agent (VIVA), to provide workers with information on-demand when conducting their activities on the jobsite. VIVA integrates construction digital sources with web platforms to enable real-time data communication between the users, workers in this case, and BIM platforms. The voice interaction feature of VIVA provides an intuitive and safe tool that can easily be used by workers with varying ranges of experience, with minimal need for training. The responsiveness and accuracy of VIVA is evaluated through two proof-of-concept case studies. Results indicate that the BIM-VIVA link and underlying natural language understanding (NLU) algorithms in the Google Actions platform accurately understand user queries, with a performance of 97.50% correct responses. As a result, VIVA achieves superior performance compared with previous research works based on NLU algorithms with text-based queries. The developments made in this research not only contribute to improving the safety and productivity of construction workers but also can open the door for easier onboarding of less-experienced workers, thus addressing the industry’s severe skilled labor shortage issue.

Practical Applications

This study introduces an innovative web-based system featuring VIVA to address the pressing labor shortage and diminishing productivity in the AEC industry. VIVA seamlessly integrates construction digital sources with web platforms, enabling real-time communication with construction workers and facilitating their on-demand information retrieval. The intuitive voice interaction feature of VIVA offers a safe and easily navigable tool that demands minimal training for workers with diverse backgrounds and experience levels. This feature also makes VIVA a practical tool for hands-free communication and information retrieval on jobsites, which minimizes the imposed distraction inherent in technological solutions. Construction workers can use voice commands to access project plans, schedules, and safety protocols, allowing them to retrieve critical information without having to stop work or use their hands. This intuitive interface reduces the need for complex user interfaces or manual input, making it easier for workers to interact with technology while focusing on their tasks. Hence, VIVA promises to improve productivity and safety of diverse group of workers without causing distraction or safety issues itself, which is a common concern when implementing technology to assist workers in hazardous and ever-changing construction jobsites. Beyond enhancing safety and productivity, widespread use of VIVA can help address the AEC sector’s severe skilled labor shortage by providing an accessible means to onboard less-experienced workers. As such, VIVA provides opportunities to augment efficiency for seasonal/nonnative workers, thus facilitating the integration of a broader workforce and addressing the industry’s pressing labor challenges.

Productivity assessment plays a key role in successful earthwork projects and is primarily achieved by monitoring key construction equipment such as excavators and dump trucks. Vision-based methods are widely adopted to analyze the productivity of construction equipment in earthwork projects. These methods are inexpensive and easy to implement and maintain. However, previous studies on vision-based productivity analysis of earthmoving equipment have predominantly demonstrated its effectiveness under favorable conditions characterized by stable lighting, areas devoid of shadows, and high-visibility environments free from significant occlusion by other objects or terrain. There is a significant illumination difference and limited visibility under harsh low-visibility conditions at earthwork construction sites, which makes it difficult to achieve reliable identification accuracy using these already developed methods. To address this problem, this study proposes an automatic vision-based dump truck productivity measurement method based on a deep learning illumination enhancement algorithm combined with transfer learning for low-visibility, harsh conditions at earthwork construction sites. This method uses an internet of things (IoT) system equipped with a camera to capture the image and a deep learning illumination enhancement algorithm, trainable deep hybrid network (TDHN), to enhance the image quality under low-light conditions at earthwork sites. Then, a deep convolutional neural network image recognition algorithm, ResNet50, was combined with a transfer learning technique to extract information from the image. Through the IoT, this processed information is sent to the earthwork platform to perform productivity analysis and make timely decisions regarding equipment allocation schemes. To validate the effectiveness of the proposed methodology, it was implemented in a real-time earthwork project. This study results show that image recognition accuracy of 99.6%, 95.67%, and 94.77% under normal lighting, low lighting, and extremely low lighting conditions, respectively. The dump truck recognition accuracy increased by 1.10%, 3.62%, and 21.19%, leading to a significant improvement in productivity measurement of 1.08%, 3.54%, and 20.71% for the above-mentioned lighting conditions, respectively.

Abstract

The positive outcomes associated with the Last Planner System (LPS) of production control are usually interpreted as stemming from the application of LPS-as-theorized in the seminal publications about this method. However, the use of LPS in real settings is subjected to contextual influences and involvement of actors with varying perspectives of what counts as an effective implementation. This suggests that LPS-as-theorized may not be the only valid and sufficient representation to explain its success. This paper investigates how the different representations of LPS contribute to its effectiveness and adaptation to local conditions. It is based on a case study on the implementation of LPS in a refurbishment project. Four representations are compared: LPS-as-theorized (literature based), LPS-as-prescribed (handbook based), LPS-as-disclosed (interviews based), and LPS-as-done, i.e., what happened in reality, according to different sources of evidence. Findings set the basis for proposing a model that frames LPS-as-done as an emergent phenomenon unique to each construction project. This new perspective contributes to the recognition of the role of informal practices, the development of more accurate assessments of LPS implementation, the analysis of trade-offs when using LPS, and the alignment of expectations on the adoption of LPS.

Practical Applications

The outcomes of this investigation can help practitioners to identify and appreciate the complementary roles of different representations of the LPS, which are not limited to LPS-as-theorized described in seminal academic papers. The use of LPS in real settings depends on contextual factors and is therefore always unique to some extent. Giving visibility to diverse representations (e.g., LPS-as-prescribed in companies’ handbooks, LPS-as-disclosed by managers) supports a more accurate and shared understanding of how LPS is implemented, making it possible to align expectations among project stakeholders. This analysis also sheds light on the need to fill the gaps in the formal planning system, setting the basis for revising procedures and training programs. Furthermore, the findings suggest that the assessment methods of LPS implementation should be based on different sources of data in order to account for the multiple representations. Overall, the results are likely to resonate with practitioners because these will recognize that they are not merely strictly following predefined procedures but rather playing an active role that demands expertise for adjusting theory into practice on an everyday basis.

Abstract

Currently, the incidence of accidents involving falls from height at construction sites caused by workers climbing guardrails is still high. Traditional unsafe behavior management mainly relies on a safety patrol of construction-site supervisors, which consumes considerable laborpower and time. There is still a critical need for an automated safety management method to identify unsafe guardrail climbing behavior. This study proposes a worker behavior identification method based on visual data fusion of a worker’s surrounding environment and posture data. Videos of seven participants’ guardrail climbing behavior through multiangle and multidistance cameras were analyzed to verify this method. By analyzing the environment and posture of the participants, three methods based on environment, posture, and fusion data were used to detect the stage of guardrail climbing action of the workers and compare them with the ground truth labeled by safety experts. The precision and recall of worker guardrail climbing behavior based on the fusion method were 82% and 83% respectively, which is better performance than that obtained using a single method. The data fusion–based method avoids the misjudgment generated by a single detection method and can identify the guardrail climbing behavior more accurately.

Practical Applications

Guardrail climbing is a typical unsafe behavior that exposes workers to a high risk of falling from height. However, there is a lack of research on the interaction between workers and guardrail systems in the construction industry. This study provides a nonintrusive method for automating detection and management of guardrail climbing behavior on construction site. Using existing surveillance cameras, this method can be deployed at low cost with slight interference with workers. Based on the detection, appropriate interventions are expected to effectively reduce workers’ unsafe behaviors during construction and improve safety on site. The detection of guardrail climbing, which is one of the variety of unsafe behaviors associated with falls from height, can enrich the intelligent construction safety management system effectively. Moreover, this study also provides reference and quantitative indicators (e.g., a guardrail climbing unsafe behavior database) for risk assessment and early warning of workers who are exposed to risk of fall from height.

Abstract

The process of selecting contractors for megaprojects involves inherent uncertainty in judgment and preferences, especially under multiple criteria group decision-making (MCGDM) scenarios. This uncertainty often stems from the ambiguity and hesitation when decision makers (DM) evaluate and prioritize different criteria, causing challenges for the evaluation of contractors’ technical capabilities. This study aims to enhance a generalized comparison table (GCT) by applying hesitant fuzzy soft sets to capture this uncertainty. Through the operation of a criteria adjustment algorithm for group consensus, experts-trust network, and the rule of maximum deviation, the GCT is improved by the ability to incorporate objective weights of subjective criteria. The improved GCT provides a transparent evaluation tool and has been tested on contractors’ assessments for a mega tunneling project in China. Its practicality is demonstrated by matching the method-assisted decision with the final choice of the project manager. The key research findings highlight the importance of incorporating project-specific criteria, the criteria adjustment algorithm, and the objective weights of criteria to refine MCGDM. The main contributions of this study include (1) a novel framework of the improved GCT for MCGDM, (2) a new criteria system that includes common categories and a project-specific criteria category, (3) a criteria adjustment algorithm to attain group consensus decisions, and (4) objective weights of criteria and DM based on the experts-trust network and maximum deviation model.

Practical Applications

Selecting the right contractor for megaprojects can be a daunting task filled with uncertainty. This uncertainty primarily arises from the ambiguity and hesitation when decision-makers evaluate various technical capabilities of different contractors. This study, therefore, aims to improve a method to make the decision-making process more efficient and dependable, particularly in scenarios involving multiple decision-makers and criteria. The improved method incorporates various factors, from technical ability to project-specific criteria, ensuring a comprehensive evaluation of potential contractors. For practicality, it utilizes a fuzzy evaluation language to capture the decision-makers’ uncertainty during the evaluation process. Additionally, the study uses a specific algorithm method to generate a group consensus result. The improved method also applies objective weights of both criteria and decision-makers to help reduce subjective evaluation. Through a case study, it is further found that the improved method is beneficial for inviting disciplinary decision-makers to join the megaproject evaluation process. The improved method is recommended for evaluating tenderer megaprojects, especially when decision-makers’ backgrounds are diverse and the accurate assessment of technical capabilities is critical. For practitioners interested in applying this method to their own projects, the programming code of this study is available for reference and use.

The so-called digital transformation of the construction industry is essential to overcoming long-standing global productivity stagnation. This transformation aims to adopt the latest technological developments and methodologies to improve construction productivity while supporting data-informed decision-making. However, the construction sector has fallen short of meeting the fast-growing population’s demands for sustainable quality infrastructure at the required pace as it has not yet taken full advantage of these advancements. Despite broad experience in managing projects, when it comes to modeling, monitoring, and re-engineering processes, the construction industry has fallen behind several other industries. To overcome these challenges, efficient construction processes and operational strategies are essential to keeping organizations competitive and meeting market demands. In this regard, even though several studies on process modeling and management in construction exist, research on construction process improvement and automation through data-driven process mining remains understudied. Moreover, the literature lacks a comprehensive review of process-oriented studies with practical industry insights. To fill these gaps, this paper aims to provide an exhaustive analysis of process mining, modeling, and management as reported by the most current state of the literature in the architecture, engineering, construction/facility management (AEC/FM) domain coupled with a current industry perspective. As a result, the authors: (1) propose a conceptual process classification framework that considers the broad spectrum of process-oriented studies in the existing literature; (2) identify construction processes commonly present across a project’s life cycle; (3) design and conduct structured interviews with subject matter experts to validate identified processes and get industry insights about them; (4) spot major literature gaps describing future research opportunities; and (5) develop a business process model canvas template that supports construction organizations in improving their corporate memory and pursuing construction productivity growth by better managing, monitoring, and automating construction processes.

Abstract

Installation quality check is essential for ensuring the construction quality of prefabrication construction. The existing techniques for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor intensive and slow. Laser scanning was previously adopted in construction quality check, however, few studies have focused on using laser scanners to assess the verticality of prefabricated wall panels, and no method has been developed for effective practical implementation. This study proposes a streamlined laser scanning approach for onsite verticality check of prefabricated wall panels. Based on systematic experiments of using the point cloud data collected by different types of laser scanners, and 25 prefabrication wall panels of four shapes, this study validates the proposed method and compares the use of different laser scanners. To facilitate an effective streamlined process for practical use, this study identifies the point cloud segmentation parameters under different laser scanning data sets and suggests suitable parameters for these case scenarios. These parameters can be adopted directly or used as references for practical application of the proposed laser scanning method in the installation verticality check. This study contributes to improving the efficiency of installation quality check of prefabrication construction, and facilitating the digital evolution of the construction industry.

Practical Applications

Checking the verticality of the installed prefabricated wall panels is crucial in construction quality control. However, traditional methods for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor-intensive, slow, and costly. For project involves a large number of same or similar type of prefabricated construction elements, this repetitive work also causes human fatigue and in-efficiency. This paper proposes a laser scanning method to streamline the quality check process for the installation of prefabricated wall panels. By systematically experimenting with the point cloud data collected by different types of laser scanners for various wall panels of different shapes, this study validates the effectiveness of the proposed method. Another major contribution of this research is preidentification of optimal segmentation parameters for laser scanning point cloud. This means construction professionals can use these parameters directly or as references for identifying suitable segmentation parameters for other projects. The streamlined laser scanning method contributes greatly to improving the efficiency of installation quality check of prefabrication construction practice, especially when large number of identical or similar elements are used.

Abstract

Head-mounted augmented reality (HMD AR) technology in construction has shown promise in enhancing operational efficiency. Concurrently, the critical role of hazard identification (HI) in ensuring worker safety is well-documented. Despite this, studies delving into HMD AR’s impact on HI, especially across varying task complexities, are limited. This study aims to bridge this gap by examining the influence of HMD AR on construction workers’ HI in diverse task environments. The research engaged 48 participants in an experiment to evaluate their hazard situation awareness (HSA), cognitive load (CL), and task performance (TP). These were assessed using the Situation Awareness Global Assessment Technique (SAGAT), the Situation Awareness Rating Technique (SART), the National Aeronautics and Space Administration Task Load Index (NASA-TLX), and measures of task accuracy. Findings indicated that environmental conditions significantly modulate HMD AR’s effect on HI. In simple and safe scenes, HMD AR has a negligible impact on HI and might enhance hazard detection capabilities. However, in complex and hazardous situations with intense workloads, HMD AR can impair HI, potentially increasing safety risks. In other words, whether HMD AR brings opportunities or risks for HI depends on the different environmental conditions. This research contributes to the nascent field of cognitive safety in HMD AR, specifically regarding HI in construction, augmenting the current understanding and laying a theoretical groundwork for HMD AR’s future safety applications in the architecture, engineering, and construction (AEC) industry. The insights gained inform the prudent implementation, training, and interface design of HMD AR on construction sites. In the context of HMD AR’s rapid evolution, this study underscores the importance of critically assessing safety implications to guide the responsible utilization of new technologies.

Practical Applications

The findings from this study reveal that the impact of HMD AR on HI is contingent upon the complexity and safety of the work environment. In scenes that are straightforward and safe, HMD AR has been shown to improve HI among participants. Conversely, in environments that are complex and fraught with danger, HMD AR may hinder HI, increasing the likelihood of hazards. The conclusions of this study suggest that the safety management and application of HMD AR on construction sites should consider the opportunities and risks of using HMD AR in different environmental conditions. It is advised that decision makers on construction sites evaluate the suitability of HMD AR deployment based on the specific complexities of their scenes. In instances where the potential hazards are intricate, the use of HMD AR might be inadvisable. For sites already utilizing HMD AR, it is crucial to provide additional safety training to users. Such training should aim to enhance awareness of how HMD AR might affect HI, equipping workers with the knowledge to mitigate potential risks effectively. Furthermore, developers of HMD AR technologies are encouraged to focus on improving the safety features of their products. A key area of improvement is the optimization of information density on the HMD AR display interface. By doing so, developers can help prevent cognitive overload among users, thereby minimizing any adverse effects on HI. This approach not only enhances the safety on construction sites but also ensures that the benefits of HMD AR can be fully realized without compromising worker safety.

In the construction process of construction engineering, there exist a complex construction site layout and multiequipment collaborative work. If a reasonable construction plan cannot be made quickly and accurately, the investment in construction costs will be increased. Aiming at the construction material distribution, this study proposed a construction plan planning method under the multinode and multivehicle scene. Firstly, a mathematical model of the material distribution planning problem in the multinode multivehicle scene was established. The basic assumptions, objective functions, and constraints were proposed. The triangular scalar variables and ‘unitization’ processing methods were incorporated into the mathematical model. In order to solve the problem of material distribution planning, a heuristic neural network algorithm was designed. A ‘gene-chromosome-individual’ representation method and a ‘two-step’ calculation concept were defined. The specific implementation steps of the algorithm were given. Fully considering the type and quantity of vehicles and the length of the path, a method of establishing the mapping relationship between the construction scheme and the construction cost was proposed. The feasibility of the proposed theoretical method was verified by a cable truss structure model experiment. A calculation method with high fitting degree and fast running speed was established. According to the experiment model, the best construction scheme was formed. The results showed that the optimal construction scheme had a high vehicle loading rate and a clear distribution path, which effectively saved the construction cost under the premise of ensuring smooth construction.

Tax incentives are a powerful driver for the development of infrastructure real estate investment trusts (REITs). Complementary antiavoidance regulations are essential to ensure the sustainable and healthy development of the REIT market. This paper breaks through the traditional policy approach that emphasizes incentives but neglects supervision. Based on factors such as information asymmetry and regulatory capture, a tripartite game model of REIT tax incentives and antiavoidance regulation is established. Utilizing global REIT policy and market data, the paper sets parameter values and conducts numerical simulations to explore the dynamic strategic interaction mechanisms among the government, REIT stakeholders, and financial regulators. The study finds that (1) government’s tax incentive policies and regulatory strategies show a phase transition characteristic with the changes of REIT market returns; and (2) based on the project’s profit status, financial regulators and investors are prone to develop a collusive avoidance evolutionary strategy. Therefore, antiavoidance regulations and mechanisms to prevent regulatory capture should be implemented in tandem to ensure the efficiency of tax incentives. The conclusion of the study provides a theoretical basis for the formulation of REIT tax incentive policy and the promotion of the sustainable development of the REIT market.

The group safety behavior of construction workers has become a topic of global concern calling for effective organizational management measures. However, existing research predominantly relies on empirical case studies, which solely focus on factors without delving into the underlying theoretical mechanisms that drive group safety behaviors. This limitation hinders a comprehensive understanding of how organizational management measures systematically influence safety behaviors. Against this background, this paper reviews and emphasizes how organizational management measures mitigate the interference of environmental factors, subsequently influencing group internal factors and group safety behavior. The qualitative simulation (QSIM) method was used to investigate the variations and distribution patterns of group safety behavior and group internal factors in 10 different simulation schemes with varying intensities and strategies. The findings reveal that: (1) organizational management measures must outweigh the disruptive effects of environmental factors for positive developments in internal group factors and subsequent improvements in group safety behavior; (2) organizational management measures influence group safety behavior through two pathways. First, they directly impact group safety behavior, resulting in significant improvements but failing to enhance group internal factors. Second, they initially affect internal group factors, which subsequently influence group safety behavior; and (3) in resource-abundant scenarios, safety utility is maximized when multiple organizational management measures are employed, whereas in resource-constrained situations, prioritizing a single measure yields the greatest increase in safety utility. The study links organizational management measures, group internal factors, and environmental factors together, filling the gap in the model of construction workers’ group safety behavior and expanding the application of group theory in the construction industry. Project managers can adopt targeted control measures tailored to the specific environmental and internal group factors to reduce safety behavior risks among construction worker groups.

Numerous studies indicate that unsafe behaviors of construction workers (UBCW) are the direct causes of most construction safety incidents. To unveil the interconnections among unsafe behaviors and the underlying mechanisms of different accident types, this study extracts the unsafe behavior chains from accidents. Based on the theory of complex networks, we constructs both the whole network of UBCW and four subnetworks corresponding to different accident types by using unsafe behavior chains. The findings reveal that the degree distributions of all networks conform to truncated power-law distributions, highlighting the critical role of hub nodes in accident occurrences. Moreover, the whole network exhibits small-world characteristics, underscoring the need to address the propagation and clustering effects of unsafe behaviors, reassess risk management approaches, and devise targeted control strategies and measures. By analyzing the topological structures of the networks, this paper ranks the importance of different behaviors across various metrics and proposes tailored accident prevention recommendations through comparative analysis of different accident types. This research holds significant theoretical and practical implications for refining accident causality models and enhancing safety management practices at construction sites. However, the study has limitations, notably future studies should focus on the absence of specific construction accident databases and the unaddressed link between accident severity and unsafe behaviors.

Abstract

Tunnel construction is crucial to the advancement of global transportation networks. Despite their importance, there have been few investigations on the safety and comfort of workers. Tunnel workers operate under various adverse conditions that lead to multiple forms of fatigue. Researchers have suggested that there is a specific type of fatigue stemming from insufficient natural or improper artificial lighting that has largely been overlooked in previous studies. To validate the existence of illumination-induced fatigue and explore control measures, this study conducted virtual reality experiments in a simulated tunnel construction setting and collected and analyzed data from scales, electroencephalogram, and eye-tracking. The results indicated significant variations in fatigue levels with changes in illumination, whereas the environment remained constant, confirming the presence of illumination fatigue. Moreover, the analysis of the scale results aligned with the EEG findings, ranking the comfort levels of the mucking illumination as $100>75>150>50>200>30>15>10>0\mathrm{lx}$. The modeling of illumination fatigue identified three comfort levels of illumination, I: [75, 100], II: [15, 75) ∪ (100, 200], III: [0, 15], thereby establishing that illumination fatigue can be controlled through improved lighting. Subsequently, to address the issue of uneven illumination distribution at construction sites, key areas for mucking task lighting were identified using eye-tracking technology as the upper and lower edges of the bucket, debris being moved, and expected stacking area. Finally, by comparing and summarizing the divergent conclusions of previous studies, this study proposed various targeted lighting guidelines. This comprehensive study not only enriches the current understanding and experimental design reference for tunnel construction fatigue but also provides practitioners with specific and effective lighting guidance to significantly reduce workers’ fatigue and improve their working conditions.

Practical Applications

The results of laboratory studies indicate that inadequate or excessive artificial lighting during tunnel construction significantly increases worker fatigue. This research identified an illuminance range of 75–100 lx as the most comfortable range for mucking activities. Occasionally, the construction environment is complex, making it challenging to control the illuminance distribution uniformly. Therefore, this study further examines the attention distribution during the mucking processes, identifying key lighting points as the upper and lower edges of the bucket, debris being moved, and expected stacking area. Furthermore, the results of this study address the discrepancies in previous guidelines on illumination levels and propose different illumination ranges tailored to specific construction areas. Worldwide construction teams can determine the appropriate level of illumination within these ranges by considering the composition of the workforce, environmental factors, and policy requirements. These findings can help enhance worker comfort and improve construction safety, ultimately ensuring productivity and efficiency.

Abstract

Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. However, during the construction phase, rebar cutting schemes are usually not rationalized enough and may lead to up to 8% waste. Previous studies and practices showed that obtaining data on rebar cutting can be labor intensive and imprecise, lacking the profiling of quantity take-off factors. Additionally, existing optimization techniques often do not adequately consider field characteristics, such as the number of cutting patterns and the scale of the problem, and the characteristics of the cutting data. This study proposes a practical, integrated approach for minimizing rebar cutting waste during the construction phase. The proposed approach consists of two main parts. The first part is obtaining rebar cutting data based on building information modeling (BIM). In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. The hierarchy also reflects repeatable commonalities between different rebar systems, which helps reduce repetitive modeling efforts and increase efficiency. In the second part, a cutting pattern-oriented multiobjective optimization method is proposed to optimize the cutting scheme. The process is divided into three stages: generating valid cutting patterns, optimizing their frequency, and combining a small number of remaining components. The method combines three algorithms—column generation, particle swarm optimization, and best-fit decreasing—to solve the problem according to the characteristics of each stage. The influence of multiobjectives on the final optimization result is further explored through a sensitivity analysis. The integrated approach was validated with test sets of different sizes, types, and complexities. Its effectiveness and generalizability in reducing rebar cutting waste contribute to its applicability and the achievement of construction sustainability goals.

Practical Applications

Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. Due to the extensiveness of the construction management mode, rebar has inevitably become one of the main sources of construction waste. However, due to the lack of practical optimization methods, this percentage can reach 5%–8% or even higher. This study proposes a practical approach for minimizing rebar cutting waste during the construction phase. In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. Then, the cutting patterns are optimized, including generating valid cutting patterns, optimizing the frequency of the cutting patterns, combining a small number of remaining components, and organizing them into cutting schemes. The integrated approach provides a flexible perspective for solving this problem. The approach was tested for its generalizability and adaptability in various construction scenarios using test sets of different sizes, types, and complexities. The effectiveness of the approach in reducing rebar cutting waste, combined with the consideration of field characteristics and engineering habits, contributes to its applicability and the achievement of construction sustainability goals.

Prefabricated construction has shifted a portion of environmental responsibilities (ERs) from on-site to off-site, prompting a focus on the entire supply chain. As an issue emerging alongside the prefabricated construction, ERs are yet to be clearly allocated along the supply chain. This study aims to explore the optimal allocation of ERs between the agents involved in a prefabricated construction supply chain (PCSC), considering two distinct types of government subsidies, namely, subsidies based on ER levels (EL subsidy) and subsidies based on ER incremental costs (EC subsidy). Additionally, it aims to compare the influence of two types of government subsidies on this allocation process. A three-stage supply-chain Stackelberg model involving the government, a supplier, and a contractor was established to analyze their optimal decisions and a numerical analysis based on a case was further exploited to validate the results. The study revealed a positive correlation between ER levels of the entire supply chain and the ER managerial efficiency of both agents under each type of subsidy. The ER allocation between the two agents varied depending on the subsidy type. With EL subsidies, the allocation of one’s ER level was contingent on its own ER managerial efficiency, while under EC subsidies, the optimal allocation of ERs between the two agents was equal. No subsidy could simultaneously render both agents more profitable than the alternative subsidy, but optimal profits were largely determined by the ER managerial efficiency of the involved agents. By comparing subsidy effectiveness indicators, it was found that EL subsidies are deemed more beneficial for the construction environment than EC subsidies. The findings can serve as a valuable reference for government authorities to incentivize ER behaviors and help enterprises in the supply chain achieve a win-win situation.

The lack of a proper risk management procedure for water transmission projects is a significant problem that could lead to claims and disputes. This study explores risk management practices in an engineering, procurement, and construction (EPC) water transmission project in Bahrain. It addresses key research questions on inadequate preconstruction risk management, its impact on project execution and stakeholder satisfaction, and the potential benefits of integrated risk management and project scope strategies. Using a case study approach, the research employs content analysis of 15 claims within the case study and thematic coding to identify risks across project phases. The study reveals challenges like scope gaps, technical deficiencies, and communication breakdowns resulting in legal disputes and a cost overrun of USD 3.6 million on the original EPC contract of USD 57.27 million. The dispute was ultimately resolved through intensive negotiations, with the contractor receiving USD 2.4 million; by analyzing contract agreements, meeting decisions, site reports, contractual letters. This case study emphasizes the need for proactive risk management during procurement and vigilant monitoring during execution, offering insights for effective project delivery. Lessons from the study emphasize the importance of stakeholder identification, change management procedures, and specialized works management for successful project execution. The research contributes practical recommendations, highlighting the critical role of comprehensive risk mitigation in EPC projects and empowering stakeholders to make informed decisions for successful outcomes. The findings can also assist vendors in understanding potential risks, being aware of the consequences of improper risk management implementation and making informed decisions before signing a contract. Through its methodology, case study insights, and practical recommendations, this study significantly contributes to the body of knowledge, providing valuable lessons and strategies applicable to a broader spectrum of similar projects.

Abstract

Effective quality management is essential for ensuring construction quality, adhering to schedules, and controlling costs. The application of Building Information Modeling (BIM) for quality control during the construction phase presents challenges, particularly with the real-time monitoring and tracing of onsite quality data. In response, we propose a framework that integrates Internet of Things (IoT)-based quality data with four-dimensional (4D) BIM for enhanced quality monitoring and acceptance. This framework includes (1) automatic segmentation of 4D BIM, (2) collection and mapping of quality data, and (3) real-time monitoring and assessment of quality. We validated our framework in a large-scale building project in Wuhan, China, focusing on concrete engineering quality acceptance. The results demonstrate that our integrated framework effectively captures and integrates live quality data in real-time into 4D BIM models for ongoing monitoring and assessment. The contribution of our research lays the foundation for the development of an integrated BIM-IoT framework for real-time quality monitoring, improving its efficiency and accuracy during quality acceptance in construction.

Practical Applications

This study presents a practical solution for real-time quality monitoring and acceptance at construction sites through the integration of 4D Building Information Modeling with IoT technologies. By facilitating real-time quality monitoring and data capture, the framework significantly enhances decision-making processes. Construction managers can use this system to promptly detect and resolve quality issues, thereby reducing rework costs and likelihood. Additionally, this integrated framework ensures compliance with stringent quality standards, providing a reliable method to meet all construction project specifications. The capability to continuously update the 4D BIM model with live data enables dynamic resource management and optimizing workerpower. Consequently, this approach leads to improved project outcomes, higher-quality buildings, and greater client satisfaction. In a case study involving a large-scale high-rise building project, the framework demonstrated its effectiveness by integrating live quality data into 4D BIM for ongoing monitoring and acceptance. The results underscored the framework’s potential to provide stakeholders with timely information and implement corrective actions, significantly enhancing the efficiency and reliability of construction quality acceptance.