Journal of Construction Engineering and Management

Abstract

Previous studies revealed that Building Information Modeling (BIM) has the potential to reduce project uncertainties, design errors, change orders, and delays, which might facilitate achieving effective dispute management in the construction industry. However, research into the adoption of BIM to holistically enhance effective dispute management is limited compared with the plentiful BIM research in the construction management field. This study explored whether and how BIM adoption can help minimize the chronic problem of dispute in the industry. A structured critical literature review method was employed in this study which involved 102 papers in the fields of BIM and construction disputes. Nine main common causes of disputes (e.g., change order, design error, site problem, contractual problem, payment problem, and delay) and eight primary benefits of BIM application (e.g., improved visual management, design optimization, improved information management, and enhanced collaboration) were identified. A conceptual framework was developed illustrating the mechanism of adopting BIM to facilitate dispute management in the overall life cycle of construction projects. The framework indicates that design error, delay, and change order can be reduced most significantly by most of the BIM benefits, whereas improved visual management, improved information management, and enhanced collaboration are three of the most frequently adopted BIM benefits that can settle the majority of dispute causes. This study contributes to dispute management with a more holistic view of adopting BIM in the life cycle of construction projects, as illustrated in the conceptual framework. In addition, the identified common causes of disputes and primary benefits of BIM application are valuable for on future research in these two areas.

Practical Applications

The research findings are valuable for practitioners to obtain a holistic understanding of the two topics of dispute causes and BIM benefits, both independently and integrally. First, the identified common causes of disputes can be used by project managers to develop more-targeted strategies for reducing potential disputes in their projects. Second, the identified BIM benefits provide a comprehensive view of the advantages of BIM application for the clients, which will enable them to use BIM in their projects more proactively. Finally, and more importantly, the research findings indicated that BIM could be used to manage disputes in the global construction industry more effectively. The identified BIM features and other technologies or tools in dispute management present comprehensive results illustrating how to use BIM directly or indirectly for dispute management. In addition, the proposed framework is valuable for practitioners to find better solutions to prevent and control construction disputes via the application of BIM throughout the project life cycle.

Recently, there has been increasing interest in the research of blockchain applications in construction contract management (CCM). CCM is currently flawed, mainly lacking in information sharing, on-time payment processes, and an easily traceable supply chain. Applying blockchain-based applications can achieve autotransaction processes, create a secure information delivery environment, and promote collaboration among organizations. This research aims to discuss the application of blockchain technology in construction contract management, evaluate the benefits and drawbacks associated with blockchain application, and propose recommended strategies to facilitate the smooth implementation of blockchain technology for improved construction contract management practices. A systematic literature review is used as the main research, and content analysis is employed for analyzing the literature. The literature analysis shows that blockchain application in CCM mainly falls under three areas: information management; payment management; and supply chain management. Under each area, the current drawbacks in CCM, as well as the opportunities and challenges associated with blockchain applications, are presented. This research is one of the first attempts to examine the application of a blockchain in CCM systematically; it also provides construction contract managers or administrators with practical guidelines on leveraging blockchain technology to improve CCM practices.

Despite the huge amount of research and diversity of project management topics, there is no well-established research on success of projects. Thus it is necessary to create models of project success and rate project performance for specific fields. Dam engineering projects have significant economic, social, and environmental impact. However, their performance over the life cycle, including strategic planning, design, construction, and operation, has not been systematically studied. Improving the performance of dam engineering projects is an area unknown to researchers and project managers. Hence, the purpose of this paper is to fill in this knowledge gap with an extensive literature review, looking at primary studies for extraction of project success criteria. Systematic review of the literature reveals that 13 items are candidates for success criteria of dam engineering projects in Australia. In this systematic literature review process, data extraction and analysis and reporting, have been tailored to answer the research question. Analysis of the selected articles using two dimensions of publication and content characteristics offers a more comprehensive perspective of the publication set in this area of research. A growing trend in the publications indicates a still emerging and growing research area, as more researchers and project practitioners are interested in the area of project success. Analysis of the content characteristics shows that despite the still-dominant Iron Triangle (time, budget, and quality) cited most often as project success criteria, new criteria are emerging, because the researchers see project success as achieving long-term objectives of the projects as well. This state-of-the-art review paper presents a comprehensive list of potential success criteria related to success of the dam engineering industry.

In traditional design-bid-build (DBB) mode, due to the sequential execution of design and construction, the review method of construction documentations is a one-time review at the beginning of the construction phase. However, in design-build (DB) mode, to realize the concurrent execution between design and construction tasks, the review method of construction documentations is multiple reviews during the concurrent execution, and the problem of when and how to review under this review method has not been solved yet. Therefore, dynamic and static decision models are creatively constructed to solve when and how to review. Based on the dynamic decision model, the influence of the execution of the review strategy on the preparation duration, review duration, executable tasks and rework duration during construction was discussed. Based on the static decision model, the influence of design accuracy, construction sensitivity, and review efficiency on the review method under different time ratios was discussed. The research achievements provide operational countermeasures for exploring the principles of developing review strategies in the concurrent execution process from the perspective of quantitative analysis, and also provide directional suggestions for the way of applying concurrent engineering in DB mode.

Building information modeling (BIM) adoption in construction remains a challenge, yet no empirical studies were conducted to gauge adoption behavior changes over time. Such a longitudinal study of BIM adoption would be particularly significant at this pivotal point in the history of construction, as it undergoes a worldwide digital transformation. In some contexts, BIM adoption has been led by contractors without policy intervention; thus, contractors’ adoption behaviors must be understood before policymakers enact top-down interventions for public construction in entire supply chains. Two cross-sectional data sets were collected in 2017 ($N=303$) and 2020 ($N=171$) in Lima, Peru, to evaluate changes in a modified technology adoption model (TAM) regarding BIM adoption. Confirmatory factor analysis (CFA), structural equation modeling (SEM), and ANOVA were used to analyze the data. This study shows that contractors adopt BIM if they believe that BIM helps to improve project performance, rather than their own individual performance. Similarly, contractors are concerned with the easiness of implementing BIM in firms and projects rather than the ease of learning BIM. Overall, perceived usefulness to project, perceived ease of implementation, and intention to use BIM had a significant change between 2017 and 2020. Although BIM implementation increased from 25% to 39% at the project level, the adoption at the user level has not significantly changed. This finding suggests that contractors value supply chain/project level implementation benefits rather than successful individual adoption. The results also show that contractors rely on in-house BIM teams or outsourced consultants to carry out BIM-related tasks. However, this would depict a within-firm digital divide between BIM teams and site management. The results are discussed in the context of national change as a BIM policy for public construction was announced by the Peruvian government in 2019. The findings of this research may be used by decision makers to target specific groups of professionals on educational programs. Policymakers could use the findings to inform roadmaps for BIM adoption in Peru and similar countries.

Collaborative innovation among different stakeholders is essential for the successful implementation of megaprojects. As collaborative innovation challenges in megaprojects are complex to foresee and plan, previous research adopted mediated power (i.e., contracts) to understand the coordination process in traditional construction projects, which may not be applicable to explain collaborative innovation in megaprojects. This study explores the role of perceived partners’ non-mediated power (expert and referent power), shared mental models, and team creative efficacy in improving collaborative innovation performance based on organizational identification theory and social cognitive theory. We surveyed 490 managers of Chinese construction megaprojects and used the SPSS PROCESS Macro mediation model and hierarchical multiple regression to test our research hypotheses. Results showed that perceived partners’ expert power and referent power positively affect participants’ collaborative innovation performance via dual-mechanism: shared mental models and team creative efficacy. Findings also demonstrated that identification with partners enables megaprojects participants to voluntarily coordinate the innovation activities of others and to participate in their innovation activities by developing a cohesive cognitive structure and boosting innovation confidence. These findings enrich the theory of megaprojects innovation management and provide suggestions for how megaprojects partners can make informed decisions to foster participants’ collaborative innovation.

Construction sites have complex environments and high accident rates. Human intrusion into static hazardous areas is a significant cause of accidents. Traditional engineering safety management mainly relies on manual methods, such as patrol inspection by safety supervisors, which is time-consuming and labor-intensive, and it is difficult to achieve a complete safety supervision. In recent years, the emergence of artificial intelligence technology and computer vision has provided a new scheme for intrusion detection. However, existing studies have used a single method for human intrusion judgment in static dangerous areas, without in-depth consideration of the influence of human posture, intrusion direction, and other factors. In this study, a computer vision–based intrusion detection method was developed, mainly aimed at static hazardous areas. The object detection was based on the You Only Look Once (YOLO) V5 module to extract the image feature information. Subsequently, the basic rule of intrusion judgment based on the key points of bounding boxes was formulated, in which the workers’ intrusion direction was recognized and postured using two auxiliary detection modules. Finally, the intrusion rule base was constructed as the basis for human intrusion detection, containing rules with different sensitivities for different intrusion states. The case study indicated that the precision and recall rate of the algorithm were 96.05% and 90.05%, respectively. Overall, this method can effectively address the defects of manual supervision in engineering safety management, reducing the probability of accident occurrence and enhancing safety at construction sites.

Offsite construction (OSC) is an innovative approach where building components (e.g., panels or modules) are manufactured in a shop floor environment, then transported to, and installed at the site. Although there are numerous benefits inherent to the OSC approach, practitioners still struggle to provide tailored projects to their clients due to the low level of flexibility in production caused by uncertainty, multiple projects, and variable market demands. Indeed, the lack of production flexibility limits shop floors to manufacture projects efficiently in an ever-changing environment, especially when processes are still labor-intensive and are not leveraged by autonomous systems, such as a digital twin (DT). Hence, this paper proposes the use of a DT to improve production on OSC shop floors by increasing flexibility, i.e., the ability to adapt to uncertainty, through the automated reassignment of multiskilled workers based on data pertaining to production status that are updated in near real-time. The present study presents key metrics adopting a lean thinking approach for waste identification that quantifies the improved production performance attributable to the proposed DT. Using simulation as a surrogate system, this research evaluates the production performance on the shop floor according to different simulated scenarios varying the number of interventions made by the DT and multiskilling configurations. Moreover, this research considers significant aspects of multiskilling such as reduced productivity, increased cost, and the time spent when moving between workstations during reassignment. The primary findings from the system’s practical application indicate a significant improvement in production due to the reduction of waiting waste, total production duration, and total production cost being reduced by 62%, 40%, and 25%, respectively. Finally, the present study presents a novel approach to increasing flexibility on shop floors while also demonstrating the benefits attributable to the use of a DT to manage multiskilled workers in OSC.

The aim of this paper is to understand the formation mechanism of and establish a predictive model for payment issues faced by contractors in China’s construction industry. Factors critical to such issues were first identified under different categories from literature and verified by a Delphi survey. This paper identified critical factors under three categories, i.e., culture, client’s financial management, and interactions and processes. A focus group was then conducted to understand the roles played by each category of factors. Specifically, the cultural factors explained the origination of payment. By taking an analogy, the current unhealthy culture acted as pathogens of payment issues as a disease in the body of the construction industry; the financial management capability of the client acted as the defense system; and the interactive and process factors played the role as trigger and catalyst. It was followed by a logical deduction assisted by another Delphi survey to understand the associations among the identified factors. The model structure was hence constructed upon the factors and their associations and was then quantified by Bayesian belief network parameter learning with quantitative data collected from a questionnaire survey. As such, the formation mechanism of payment issues was investigated, based on which a Bayesian classifier was established from the perspective of Chinese contractors. The model demonstrated a high accuracy rate of more than 90%. This paper systematically investigated the formation mechanism of payment issues in China’s construction industry and revealed that root causes of payment issues could not be eliminated in a short term. The predictive model endows contractors with advantages in proactively evaluating if they are capable of tackling the risks of potential payment issues without disturbing the current power balance of the industry.

The use of robotics and automation (RA) within construction operations to combat stagnant productivity, high rates of injuries and fatalities, and a declining workforce is increasing. However, utilizing RA for construction operations creates human-robot interactions (HRIs) that can lead to the emergence of novel hazards or exacerbate existing safety risk levels. These hazards, if ignored, could worsen worker safety performance. This paper presents a study intended to identify critical HRI safety risks and develop a pre-task planning protocol for the assessment of safety risks associated with the use of RA. To achieve this goal, the authors utilized a sequential multi-phase approach relying on information obtained from a literature review, a 3-round Delphi process consisting of a panel of experts from industry and academia, and interviews with safety experts. Forty hazards associated with RA and 20 potential strategies for the mitigation of HRI safety risks were identified and evaluated. This information was used to develop a safety risk assessment tool that yields an overall risk rating of using RA. This study contributes to knowledge and practice by identifying and quantifying critical safety risks associated with HRI and developing a tool to assess and mitigate unique HRI safety risks. Utilizing insights developed in this study could improve construction worker safety and health and aid the industry’s increasing push towards a smart and safe workforce.

Currently, the published methods used to optimize the number of beam pedestals in a precasting yard for bridge beams or segments do not consider all relevant constraints. Therefore, an optimal calculation method based on multiobjective constraints is proposed. The constraints used are the demand plan for erecting beams, the minimum beam storage time, and the actual beam storage time. Two cases were considered based on the relationship between the minimum and actual storage times. When the minimum storage time of the finished beams was equal to the actual storage time, the minimum number of pedestals could be determined through the stage of maximum demand. When the actual storage time of the finished beams exceeded the minimum storage time, multiobjective constraint equations for the production and demand plans of each stage were established, and a logic algorithm is proposed to calculate the minimum number of pedestals. In addition, to identify the appropriate production plan, an optimization method is proposed based on three evaluation indexes: vacancy rate, dispersion, and core area. Taking one project as an example, the minimum number of platforms and optimized production plans were obtained using the proposed method. A complete set of planning processes, including calculation analysis and scheme comparison, is presented in this paper. The results demonstrate that the proposed methods are applicable and can provide a theoretical reference for the optimization of precast yards for bridge construction projects.

Volumetric modular construction (VMC) needs streamlined supply chain coordination to achieve its full potential benefits, such as reduced schedule and cost, with consistent quality. Studies have noted that blockchain technology has significant potential for increasing the efficacy of supply chain coordination by providing reliable information sharing among VMC stakeholders. However, it is not well known how blockchain technology’s enhanced information sharing ultimately can lead to better supply chain coordination in VMC. To address this issue, we developed and tested a blockchain-based information collecting, storing, and sharing system with an automated contract execution method. A sensor-integrated decentralized application collects project information and sends it to the blockchain network to be shared among project participants for data-driven actions in VMC. We demonstrated the system through a hypothetical case project in which stakeholders took photos with their mobile devices to share the module’s status information to trigger an automated payment promptly, facilitating efficient supply chain coordination. The results show that any photos collected from the stakeholder’s mobile device can be traceably stored and shared in the suggested blockchain system regardless of their file size, triggering nearly real-time automated payments. The primary contribution of this study is the development and demonstration of a blockchain system that realizes accountable project-related information sharing with automated contract execution functions across VMC stakeholders for better supply chain coordination.

Estimating the compressive strength of concrete at an early age is critical for quality control and management of concrete construction. Although previous studies have made significant efforts to automate this process, they all have shortcomings regarding the long transmission range needed for a typical construction site and the corresponding power consumption. The present study uses a long-range wide-area network (LoRaWAN) system, a relatively new wireless sensor network technology, to overcome these limitations. In addition to the long transmission range, the features like low power consumption and cloud connectivity available with the system are essentially required for large construction sites. An energy harvesting unit is integrated with the proposed system to make it environment-friendly by reducing the carbon footprint significantly. The efficacy of the proposed system is demonstrated for real-time monitoring of early-age concrete compressive strength using the maturity method through laboratory and field experiments considering a typical concrete mix.

Transaction cost economics is attracting considerable attention for establishing governance structures in institutions based on the so-called transaction dimensions (specificity, uncertainty, and frequency). However, few studies have looked at project governance based on transaction dimensions in construction. This research remedies this gap by providing an in-depth evaluation of the effect of transaction dimensions on the choice of project delivery and contract mechanism of a set of 84 construction projects. The study adopts data analysis through the Delphi method with the support of multinomial logistic and binary logistic regressions. The results support transaction cost economics for design-build versus design-bid-build and relational versus nonrelational choices. The results also show that project specificity is the primary driver in choosing project governance. This study gives empirical evidence for applying transaction cost economics in construction management. The research contributes to a further understanding of the choice between markets, hierarchies, or hybrids as alternative governance structures in construction projects.

Masons are aided by ergonomic inventions such as tools, processes, and equipment, yet they are still subjected to performing physically demanding and hazardous tasks at the worksite. Experienced masons have a high level of job satisfaction; however, they also experience a high rate of attrition during their training phase and do not, on average, experience as long of a working life as nonconstruction workers. By analyzing expert masons’ performance, in terms of body kinematics and load levels, during various masonry activities, guidelines for the training of a safer and more productive generation of masons can be formulated. This study investigated expert masons’ performance and ergonomic characteristics during seven common masonry activities. Specifically, eight expert masons with over 20 years of experience laid out 16.6 kg concrete masonry units (CMUs) to construct a standard wall, a reinforced wall, a wall in constraint space (under ceiling), and the first course. They also utilized individual and collaborative lifts to build a five-course wall using 23 kg CMUs and collaborative lifts to build the same configuration using 35.2 kg CMUs. Inertial motion capture systems captured their motion, and a biomechanical analysis determined the load experienced by major body joints in each activity. The present study contributes to the body of knowledge by providing insights into expert masons’ distinctive ergonomic characteristics in seven common masonry activities. Our findings open the door to providing apprentices with improved training based on those characteristics. We quantified the impact of optimized work configurations (working height for picking up and laying down material at about waist level) on minimizing musculoskeletal risks. A more significant impact on masons’ safety, health, and productivity is expected by applying expert masons’ biomechanical strategies in designing and/or redesigning work systems for a safer generation of masons.

There is a dire need to rebuild existing infrastructure with strategic and efficient methods. Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more time and cost-efficient project delivery method. Past research studied factors influencing DB but without providing a holistic analytic approach. This paper fills this knowledge gap. First, a systematic literature review is performed using the preferred reporting items for systematic reviews and meta-analyses techniques, and a set of factors affecting DB projects are then identified and clustered, using $k$-means clustering, based on the whole literature discussions. Second, a graph theory approach, social network analysis (SNA), is conducted methodically to detect the understudied factors. Third, the clustered factors are analyzed using association rule (AR) analysis to identify factors that have not been cross-examined together. To this end, the findings of this research highlighted the need to investigate a group of important understudied factors that affect DB decision-making and procedures that are related to management, decision-making and executive methods, and stakeholder and team related aspects, among others. Also, while the majority of the existing research focused on theoretical efforts, there is far less work associated with computational/mathematical approaches that develop actual DB frameworks. Accordingly, future research is recommended to tackle this critical need by developing models that can assess DB performance, success, and implementation, among other aspects. Furthermore, since none of the studies evaluated DB while factoring in all 34 identified relevant factors, it is recommended that future research simultaneously incorporates most, if not all, these factors to provide a well-rounded and comprehensive analysis for DB decision-making. In addition, future studies need to tackle broader sectors rather than focusing over and over on the already saturated ones. As such, this study consolidated past literature and critically used it to offer robust support for the advancement of DB knowledge within the construction industry.

This paper is the first attempt to investigate the effect of magnetized water (MW) on the mechanical properties, microstructure, and chloride penetration resistance of concrete containing glass waste nanopowder (GWNP). Various replacement levels of GWNP (0%–5%) were adopted. MW of 1.2 T was used in magnetic mixtures. The considered properties of GWNP concrete were tested using compressive strength, split tensile strength, flexural strength, desorption, thermo-gravimetric analysis (TGA), X-ray diffraction analysis, scanning electron microscopy (SEM), and chloride profile and penetration approaches. Results showed that using MW instead of regular tap water (TW) improved the mechanical behavior, microstructure, and chloride diffusivity of GWNP concrete. Magnetized concrete with 5% GWNP had the highest decrease in calcium hydroxide content, capillary porosity, and chloride penetration depth (24%, 24%, and 18%, respectively) and the highest increase in compressive strength and calcium silicate hydrate (22% and 35%, respectively) compared with 0% GWNP nonmagnetized concrete. SEM results supported the mechanical and chloride diffusivity observations, in which magnetically treated concrete exhibited a better morphology. This leads the author to assume that using MW instead of TW could be an effective solution for extending the service life of GWNP concrete structures.

Logistics parks served as critical logistics facilities during the coronavirus disease 2019 pandemic. Constructing these large-scale projects including their subsequent operations involved unusual risk factors that required comprehensive risk evaluations. Therefore, it is vital to examine the potential risk factors across the entire life cycle of these projects in accordance with the complexity of these large-scale logistics park projects (LLPPs) during the pandemic. This study proposes a new risk analysis model that evaluates a comprehensive list of risk factors based on the failure mode and effects analysis (FMEA). The hesitant uncertain linguistic Z-numbers (HULZNs) and fuzzy C-means (FCM) clustering algorithm are incorporated into the FMEA model to evaluate the risk. The practicability of the model was assessed in a case study. The findings identified and distinguished risk factors based on their levels of influences and priorities in dealing with uncertainties during the decision-making process in LLPPs. The FMEA model’s new methodological assessment also contributes to the efficient risk evaluation process through its more reliable and reasonable failure mode risk clustering, particularly for complex group decision-making environments during the pandemic.

A common problem in construction is that disruptions emerging in the off-site supply chain adversely influence on-site activities. Off-site disruptions may cause substantial construction delays if they are not identified in time. Based on the production theory and four-dimensional (4D) building information modeling (BIM) applications in construction, this paper proposes a novel framework that assists in developing look-ahead supply schedules to identify potential construction delays caused by off-site disruptions. The framework adopts an extended customer order decoupling point (CODP) typology to define off-site process patterns, reflecting the production features and supply characteristics. The early warning signs of potential construction delays are captured by checking the integrated on- and off-site process and further visualized in a BIM-enabled environment. A case study is carried out to verify the feasibility of the framework and identify practical implications. Guided by the framework, construction project teams and suppliers can identify the early warning signs of potential delays, aided by specific location and duration instructions from the 4D BIM. This framework will help prevent off-site disruptions and offer adaptability for construction and logistics scheduling and planning.

Current index factors considered in tunnel blasting quality evaluation are relatively onefold, as well as the unrefined evaluation standards, which may debase the reliability of quality evaluations. In this study, an evaluation index system of tunnel-controlled blasting quality was first established considering the characteristics of geometry and safety. The fractal dimensions of blasting contours were calculated based on the field over/underbreak data using wavelet analysis. Then, the index grades and the index weights were obtained via cluster analyses and an innovative combination weighting method, respectively. Finally, an unascertained measure model (UMM) was established and applied in Zigaojian Tunnel blasting evaluation. The results indicate that the evaluation grade of tunnel blasting quality turns down with the increase of the intensive degree of discontinuities in rock mass. The UMM makes evaluation results more refined compared with the traditional scoring method, which prepares an important basis for the quality optimization of tunnel blasting construction.

Although truck cranes commonly employ outriggers for the horizontal attitude adjustment prior to operation, the time-varying operation of the upper-structure, e.g., telescoping, pitching, rotating, loading and unloading, constantly influences the attitude stability of the chassis. In extreme operating conditions, a tiny attitude change can have disastrous consequences, resulting tip-over accidents. Although there are monitoring and warning systems of the chassis attitude in practice, the research on its active control is rarely seen. In response to this, a synchronous control theory of the upper operation and the chassis attitude maintenance were proposed. First, the expression of the center of mass of a typical truck crane was built according to its mechanism and motion parameters. Then, the quasi-static load bearing model of the outrigger system was built in combination with the deformation compatibility condition, by which the mathematical relationship between the upper operation and chassis attitude disturbance, i.e., the compensatory length of each outrigger that is required to maintain the attitude stationary of the chassis, can be determined. Based on the above models, the tip-over early warning model of truck cranes was deduced, and a synchronous control strategy of time-varying upper operation and chassis attitude maintenance was proposed and preliminarily verified by tests based on an electromechanical experimental platform. Results showed that the attitude stability of the chassis is improved by about 80% after control, which is beneficial to improve the safety of the truck crane, the operating efficiency, and reduce the operating burden of the driver.

Buildings in high-rise high-density cities are principal contributors to global anthropogenic carbon emissions. Notwithstanding the many commitments to delivering low-carbon buildings (LCBs), various constraints have hampered their delivery. Despite their significance, very few studies have identified these constraints from the perspective of high-rise high-density cities. Furthermore, there has been no specific identification of stakeholders’ engagement needed to address the constraints. The reported study addresses this gap by exploring the stakeholder engagement required to overcome the constraints of delivering LCBs. First, a list of 21 common constraints for delivering LCBs in high-rise high-density cities was identified by building on a precursor research study. In addition, 19 stakeholders with the ability to help overcome these constraints were identified through a literature review, and their engagement in addressing the identified 21 constraints was analyzed by employing social network analysis (SNA) approaches. This analysis was based on a two-round Delphi survey conducted with the participation of 15 experienced practitioners. The findings provide a systematic mapping of the constraints and associated stakeholders. Accordingly, clients, policy makers and regulators, property and facility managers, educational training and research institutes, and accreditation and standardization bodies showed an ability to address more than 60% of the identified constraints, whereas financial institutions, end users, project managers, and suppliers showed an ability to address less than 30% of the constraints. This study contributes to the theory by providing useful insights into developing and utilizing suitable SNA structures to analyze stakeholder engagement. It contributes to practice by identifying common constraints in delivering LCBs, identifying the significant stakeholders involved, and alerting industry practitioners to the imperatives and suitable modalities for their engagement to strengthen the delivery of LCBs in high-rise high-density cities.

Offsite construction has been shown to possess many advantages and benefits in dealing with the construction industry’s challenges, which include poor labor productivity. Nevertheless, all previous productivity-related research studies have focused mainly on factors impacting labor productivity in traditional stick-built construction methods. This paper filled this knowledge gap by following a multistep interrelated research methodology. First, a research team of 19 construction professionals and academics developed and distributed an industry survey to (1) quantify the likelihood of occurrence and relative impact of risk factors that adversely impact labor productivity in offsite construction projects; (2) identify and prioritize key risk factors that adversely affect labor productivity in offsite construction operations; and (3) examine how labor productivity risk factors are perceived by various project stakeholders. Second, different statistical analysis tests and methods (i.e., internal and external reliability, statistically significant differences, clustering analysis, and concordance analysis) were used to critically analyze the results and draw conclusions. Based on a total of 100 responses and 20 labor productivity risk factors, the findings reflected that the top five risk factors adversely affecting labor productivity in offsite construction projects included (1) unskilled labor and improper workforce training and development; (2) poor logistics; (3) errors, omissions, and rework; (4) work area congestion and overcrowding; and (5) insufficient coordination. Also, the findings indicated that labor productivity factors can be clustered into two groups: factors with high overall risk and factors with low overall risk on offsite construction labor productivity; 80% of the risk factors were found to fall into the first category. The results of this study also reflected the need for offsite construction companies and industry practitioners to carefully establish mitigation plans and corrective actions for the identified key risk factors adversely affecting offsite construction labor productivity. This study adds to the body of knowledge by exploring and ranking productivity factors in offsite construction projects. Ultimately, this study will help the industry and research communities better understand factors affecting offsite construction labor productivity, more effectively direct future efforts to enhance labor performance, and devise productivity improvement strategies.

The ability to hear auditory safety cues of mobile equipment while wearing hearing protection equipment (HPE) is critical to preventing injuries and deaths in construction. Existing collision hazard detection models using proximity technologies have limited applicability due to the need for an expensive and complex deployment of sensing devices on every piece of construction equipment. This study proposes a more affordable collision prevention technology that uses audio signals to detect the presence of mobile equipment. The study addresses the problem by improving the auditory situational awareness for construction workers exposed to loud noises with a novel sound detection model that uses artificial intelligence (AI) to detect the sound of collision hazards buried in a great deal of ambient noises. This study included three phases: (1) collecting audio data of construction equipment, (2) developing a novel audio-based machine learning model for automated detection of collision hazards, and (3) conducting field experiments to investigate the system’s efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

The increasing awareness of sustainability has resulted in the progressive regulation of contractors for public projects to lower the pollution associated with their equipment fleet. Nevertheless, such implementation has not been fully developed yet due to the challenges this presents to contractor profitability and the practical usefulness of decision-support tools. This paper developed a sustainable equipment fleet (SEF) decision-support optimization model with the capability to reduce the ${\mathrm{CO}}_2$ emissions of construction equipment and to minimize the equipment fleet modification cost, subject to the contractor’s budget and time constraints. The model utilizes a multiobjective genetic algorithm (GA) to search for the combination of equipment utilization, equipment model, and sustainable options that provide the optimal equipment fleet options to contractors. The results from the case study of a road construction project demonstrated the tradeoff between emissions and costs, with a wide range of optimal sustainable equipment fleet options. In addition, the fuel equivalent ratio (FER) and the equipment cost-effective ratio (CER) were newly established to normalize pollutant generation and equipment investment and to enable equipment comparisons for facilitating equipment selection by the contractor. The findings suggested that equipment selection involving a lower FER is preferable for sustainability and likewise, a lower CER for the cost aspect. However, effectiveness was measured as the proportion of FER to CER, with higher effectiveness when the proportion was lower. This paper facilitates the assessment and prioritization by construction companies of their equipment investment options to satisfy sustainability requirements.

This article aims to present the results of a small research program carried out within the Norwegian context. It endeavors to present main lessons gathered from separate studies carried out within an overall building process perspective. The material examined consists mostly of peer-reviewed publications based on qualitative approaches. Case studies constitute the main bulk, with the addition of some desktop studies. The current fight against criminal activity in the Norwegian architecture, engineering, construction (AEC) industry mainly takes place at a workplace level. It is mostly targeting the lower echelons of criminal value chains. Little concern seems to be given to the material supply chain. The analysis proposed indicates that current crime-fighting measures employed within the Norwegian context do not take into consideration the implications of the processes involved in the AEC industry. The analysis provides an optic for reconsidering the scope of the measures chosen. As a practical implication of this, the controlling agencies need to review their scope of action. Reconsidering the potential for criminal activity within the AEC industry enables the controlling authorities to develop more efficient countermeasures. Using insights from the building process literature can provide the optics necessary to carry out such a reconsideration. Further research endeavors ought to buttress necessary efforts in this vein.

Project close-out is one of the critical phases for any construction project. Quality of close-out data, timeliness of completion, and responsiveness to owner expectations affect the overall client–contractor relationships. Delays in the close-out process can also affect project profitability and delay moving resources to the next job. Because most of the construction scope of work is performed by the subcontractors, it is critical for the close-out process to be performed efficiently at the subcontractor level. One of the critical subcontractors that are involved in every construction project is the HVAC mechanical contractors. The purpose of this study is to evaluate and document the existing close-out processes that are currently being utilized by the HVAC mechanical contractors in the construction industry. Professionals from 14 different HVAC mechanical contracting companies participated in the semistructured interviews for a multiple case study analysis. The qualitative analysis in the form of constant comparison and thematic coding using transcripts from the interview was used to identify the general structure of the close-out process, various close-out strategies, project control and success measures for project close-out, use of technology, and the critical close-out documents. The key findings of the study include the importance of planning for project close-out in the planning phases of the project, clear understanding of the owner and general contractor expectations, and proactively implementing methods to collect close-out data and documentation as soon as it becomes available instead of delaying until the end of the project.

Prefabrication is often considered as a potential solution to address the challenges of building construction in remote communities. However, methods to find the best modularization for mechanical, electrical, and plumbing (MEP) systems are scarce. This prevents a proper assessment of cost reductions and local benefits, two key metrics for decision makers in remote communities. Therefore, in this study a computational framework is proposed for identifying the optimal modularity of piping to be assembled in residential buildings located in an isolated region, namely Nunavik (Quebec). The framework contributes to advancing existing modularity optimization approaches for MEP systems by integrating two objective functions, the impact of module characteristics on modularization, collision constraints during assembly, and the context of remote communities. The algorithm simultaneously minimizes system installation cost and maximizes local job creation, an important socioeconomical outcome of construction in remote areas. Fuzzy logic models and nondominated sorting genetic algorithm (NSGA-II) algorithm were used to evaluate configurations and identify nondominated solutions. Type of work, height of the assembly, direction changes, and stiffness of the modules were considered in estimating assembly time. The dimensions and weight of each module were used to estimate handling time. The optimization framework considers two possible prefabrication sites, outside and inside the remote region. By applying this approach to modularize an MEP system in typical Nunavik housing units, it was possible to demonstrate a reduction in installation cost and an increase in local job creation compared to the current situation (i.e., without MEP prefabrication). In particular, for a case study with 40 components, the proposed framework found a solution that can reduce installation cost by 81.9% and generate 23.4 h of local employment.