Journal of Management in Engineering

The 4th industrial revolution and its inherent digitalization are changing every aspect of the construction industry. However, these technological transformations bring new challenges, including cybersecurity. Although a number of studies in recent years have aimed to analyze cybersecurity issues in the construction sector, further research investment in this area is vital. The first step towards identifying needed and promising research directions in this domain is to systematically analyze the existing body of knowledge and detect knowledge gaps. This study addresses this need through a systematic scoping review designed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Review (PRISMA-ScR). To synthesize the existing knowledge, the extracted information is mapped with multiple benchmarks, including the National Institute of Standards and Technology (NIST) framework for cybersecurity management. The outcomes of the scoping review indicated that, from the construction perspective, the existing literature mainly focuses on a few areas, including Building Information Modeling (BIM), while other digital systems such as construction robots and prefabrication platforms need to be considered in future works. In terms of cybersecurity management, most of the existing studies focus on identifying risks and protecting assets. Other aspects of cybersecurity management, including detecting intrusions, responding to threats, and recovering from cyber-attacks need to be addressed in future studies. Regarding the technology-mediated countermeasures, the existing studies have explored only a limited number of potential solutions with a considerable focus on Blockchain-based systems, while the application of other countermeasures such as network virtualization can be a basis for future studies. Finally, the existing studies focused on procedural guidelines lack systematic solutions for areas including integrated cyber risk management and organizational change.

The unexpected increased cases of COVID-19 pressured the healthcare system to be exposed to unprecedented challenges, where healthcare management is a complex but essential process to manage and coordinate all information and resources. Critical problems are emerging in this new routine and demanding a higher efficiency of medical data sharing. It is becoming necessary to improve the performances of city systems and create an open data city for reliable information and data sharing (e.g., medical and testing) for better public awareness and healthcare services. This research aims to critically review past efforts in open data city for healthcare from the perspective of data management strategy and development. China was selected as a representative due to its fast development in healthcare infrastructure and medical big data, and 79 out of 4,611 articles were selected, reviewed, and analyzed. A mixed-method approach was implemented to review and assess the existing efforts of open data city for healthcare through latitudinal and longitudinal analyses from five aspects based on sociotechnical systems: technology, people, infrastructure, processes, and culture. The gaps, missions, and challenges of developing an open data city for healthcare are also summarized, and an open data city healthcare reinforcement framework is proposed accordingly. This research contributes a new multidimensional way to rethink the development of open data city in healthcare and helps establish the state of the art of open data city.

Decisions made at every stage of the built environment life cycle impact how materials in that environment are eventually managed at the end of their life, whether in a linear or cyclical manner. Linear fates often terminate in landfills, whereas more sustainable cyclical fates are continuously extended through reduction, reuse, repair, recycling, and recovery (5Rs) practices. Despite a notable increase in the literature on sustainable construction and demolition waste (CDW) management, there is still a lack of studies comprehensively addressing the full life cycle of the built environment. A gap also exists in the understanding of the root causes underlying unsustainable CDW management practices. To address these gaps and shortcomings, this study synthesizes the literature on CDW management to identify the causal factors contributing to the current unsustainable management practices, their root causes across the life cycle of the built environment, and sustainable strategies with proven efficacy in mitigating the detected root causes. Considering the implications of these strategies on the management of CDW, as well as their applicability across different stages of the built environment life cycle, this study characterizes them using the 5Rs waste hierarchy and using a five-stage built environment life cycle framework: interim, planning, construction, use, and end of life. All such information is integrated into a single ranking framework that proposes prioritized sustainable strategic pathways to address the most relevant root causes of unsustainable CDW management across the entire built environment life cycle. The actual implementation of the proposed framework for the sustainable management of the built environment is demonstrated through an analysis of Louisiana, in which peacetime construction/demolition and disaster debris generation and reconstruction/demolition have resulted in large amounts of CDW that often ended in landfills. Future decision makers can avoid similar fates by mitigating their own root causes of unsustainable waste management by better utilizing relevant mitigation strategies.

Workspace, project duration, and workforce are three critical resources for construction projects. Project managers need to expend time and effort reviewing, comprehending, and coordinating these resources. However, the space–time–workforce interactions and their impact on decision-making in project scheduling are not fully known. Therefore, the objectives of this research were to understand the impact of workforce shifts on space, time, and labor cost performances, develop a three-dimensional (3D) visualization tool to reveal activity-level resource dynamics, and associate the risk aftermath with the occurrence probability to balance subjective risk tolerance and objective system reliability. This research developed a simulation model based on a case project to compare the workflow of five major specialty trades (i.e., bar placer, carpenter, scaffolder, pipefitter, and concreter) in 267 scenarios. A resource-oriented 3D visualization tool was developed to help project managers monitor project schedules. The research established a risk control framework using value-at-risk (VaR) and conditional value-at-risk (CVaR) approaches to associate extreme outcomes with their occurrence probability. Simulation results indicated that pipefitters significantly affected workspace overlap, whereas bar placers and carpenters predominantly impacted project duration. The scholarly contributions are (1) the creation of an intelligent system to generate a project schedule from workforce assignments specified by project managers, (2) development of a tool to visualize the three-way resource dynamics of workforce, time, and space on a 3D model for all possible scenarios, and (3) development of a framework for project managers to balance planning strategies between subjective risk tolerance and objective system reliability. This research provides project managers with a dynamic 3D visualization of space, time, and workforce utilization and interaction in uncertain environments, further facilitating reliable project scheduling decision-making.

Construction disputes have long been identified as epidemics in the construction industry worldwide, which has become a more serious problem due to the impact of the COVID-19 pandemic. Previous studies on the dispute causes have primarily focused on country- or region-specific contexts and hence the results cannot be generalized in solving this chronic problem in a broader construction project worldwide. This study aims to explore and evaluate the critical dispute causes in construction projects through a comparative study between China and the United Kingdom. A total of 33 common dispute causes were identified through a comprehensive literature review and further consolidated by pilot surveys in the two countries. An online questionnaire survey was administered among construction professionals in China and the United Kingdom, with 170 valid responses returned for data analysis. Principal component factor analysis, mean score ranking approach, quartile analysis, and Mann-Whitney $U$ testing were employed to identify the most critical dispute causes. Similarities and differences were mapped between the two countries. It was found that the five most critical categories of dispute causes in the two territories are: delay-related problems, lack of communication, contractual problems, site conditions, and design problems. The importance of variation in quantities, breach of contract, misinterpretation of contractual terms and conditions, and poor contract management was perceived significantly differently by the respondents in China and the United Kingdom, whereas design defects and failure to make compensation for additional work were the most critical common dispute causes in both countries. The research provides important findings for both academics and practitioners to holistically understand the similarities and differences of dispute causes in China and the United Kingdom and aids in preventing disputes more effectively in the global construction industry.

Despite the high spending and high employment, the construction industry struggles to agree how to go about project performance assessment and what constitutes a successful project. This does not allow stakeholders to address the issues in and drawbacks of their respective projects. Hence, this research outlines performance metrics upper limits, which can be used in defining a successful project. Data were collected from Construction Industry Institute (CII) members and multiple construction firms through University of Wisconsin–Madison active research projects. Two classification and regression tree (CART) models were developed using 4-fold randomized cross-validation to provide the metrics thresholds of success with a classification accuracy of 81% and 85%, respectively. CART models revealed that a successful project can be defined as having construction schedule growth less than 10.4%, construction cost growth less than 9.8%, requests for information (RFIs) per $million less than or equal to 8.6, and RFI processing time less than or equal to 7 days. Additional thresholds showed that a high-performing successful project would have a change orders per $million of less than 0.39 and rework of less than 1.5%. These thresholds can serve as a step toward a quantitative definition of success by translating qualitative success status into quantitative success status. Additionally, the established thresholds can be used as benchmarks for acceptable construction performance, which can then be adopted as risk thresholds or tolerances during project planning.

Urban rail transit (URT) public–private partnership (PPP) projects are long-term, complex projects involving numerous stakeholders. Various unforeseeable events make the benefits of these projects uncertain, and disputes about the distribution and adjustment of benefits can seriously threaten project success. Therefore, there is a pressing need to effectively evaluate the project benefits available to various stakeholders and design a dynamic benefit-adjustment mechanism to ensure the successful completion of URT PPP projects. In full consideration of the complexities of projects of this nature and the adaptive behaviors of different stakeholders, a modeling framework was proposed to simulate the realization and dynamic adjustment of project benefits as a complex and comprehensive system defined by the microlevel dynamic behaviors of stakeholders and interactions among these behaviors through agent-based modeling (ABM). The framework was utilized to emulate a real-world URT PPP project for demonstration purposes. Next, two scenarios were simulated that considered the effects of the purchase behavior of the public and decisions concerning benefit adjustment on project benefits. The framework is a powerful decision support tool with regard to the benefits of URT PPP projects in that it can promote the maximization of overall benefits and the sustainability of URT projects.

Abstract

Reinforcement learning (RL) has recently been adopted by infrastructure asset management (IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision-making. However, this relatively recent line of research has not incorporated other sources of uncertainties, such as hazards apart from deterioration patterns, nor has such research considered managerial aspects of IAM, such as stakeholders’ utilities. This paper aims to provide a holistic framework that draws upon recent developments in IAM systems and microworlds, employs RL model training, and considers deterioration, hazards, and cost fluctuations as the main sources of uncertainties while also adopting managerial aspects into decision-making. Consistent with the existing practice of IAM, this framework brings flexibility in the face of uncertainties to the IAM decision-making process. Multi-agent RL models based on deep Q networks and actor-critic models are constructed and trained for taking intervention actions regarding elements of a real bridge in Indiana through its life cycle. Both models could lead to higher expected utilities and lower costs compared to the optimal maintenance, rehabilitation, and reconstruction (MRR) plans obtained by Monte Carlo simulation and heuristic optimization algorithms. The proposed framework can assist decision-making bodies and managers in the IAM domain with making updateable optimal and more realistic decisions based on the updated state of various complex uncertainties in a negligible amount of time.

Practical Applications

Long-term strategic intervention actions are vital for maintaining the safety of degraded infrastructure assets. Traditionally, these strategic plans are derived through using optimization methods for maximizing stakeholders’ utilities, improving the safety of the network, and minimizing costs and risks associated with catastrophic events. The main drawback, and perhaps the biggest reported challenge, of this approach is the lack of flexibility in the face of the updated state of uncertain phenomena, such as the condition of assets and volatile costs. Simply put, the rare outcomes of unprecedented events can render optimal strategies useless. This study promotes the application of artificial intelligence-based agents for taking updateable decisions given the newly-observed state of all uncertain factors. Based on the results of the case study, the proposed framework was found able to reduce costs by up to 7% during the management horizon. This reduction in costs can be translated into saving millions of dollars of taxpayers’ money on a community scale. The theoretically well-founded and practically applicable proposed framework could measurably enhance the management of various infrastructure assets. Given that it can be tailored to other decision-making processes under uncertainty, the proposed framework can be similarly applied to other complex managerial problems.

The resilience of interorganizational projects has attracted increasing attention from researchers and practitioners. However, the impact of governance of interorganizational relationships on project resilience has not been understood or empirically investigated. Using dynamic capability theory, this study investigated the mechanism by which interorganizational governance influences project resilience. According to the literature and theoretical analysis, resource reconfiguration is identified as a mediator between interorganizational governance and project resilience. It was conducted by a quantitative analysis of a survey of the engineering industry in China using structural equation modeling. The findings show that both contractual governance and relational governance are enablers of project resilience. This unpacks how this relationship can be achieved through resource reconfiguration as the mediator and contract governance partially function through relational elements. Drawing on the dynamic capability view, our findings enrich the body of knowledge of resilience in the project realm by exploring the impact of contractual and relational governance mechanisms on project resilience.

Highway agencies strive to carry out maintenance of traffic during road project implementation in a bid to reduce the safety and mobility adversities that typically accompany work zones. In certain cases, the agency resorts to a full road closure strategy, where the project location is blocked from traffic use and traffic is detoured to an alternate route, thus bypassing the project location. Often, there exist a number of alternative paths that could be used for the detour. Few specific guidelines exist for defining a detour path, resulting in technical, financial, and operational constraints dominating the detour planning process. The challenge, therefore, is how to identify the best candidate detour path based on several factors including, but not limited to technical, financial, and operational considerations. This paper proposes a methodology to address this question. The methodology is based on a multicriteria decision-making technique and is demonstrated using two case studies. The case study results are discussed in the context of the relative weight specifications of the key performance indicators (KPIs). The results highlight the impact of the KPIs and their weights on detour route choices. The methodology can be used as a basis for developing a decision-support tool to assist practitioners in selecting the best detours based on a comprehensive range of KPIs. This research contributes managerial aspects that can reduce the subjectivity in maintenance of traffic (MOT) strategy selection and lower the inconsistencies of prescribed detours for similar project conditions.

Globally, women are a minority in the engineering profession. Various initiatives are adopted by employers to support women’s career progression and retention in the profession. This paper examines the effectiveness of the common initiatives. Focus groups were conducted with 25 New Zealand based female engineers to explore their experiences and perceptions of women in engineering initiatives. Women discussed the effectiveness, and unintended consequences, of the initiatives. Their responses were coded using the NVivo software to identify key themes, which were then categorized using the universal/selective/indicated model. The findings show that universal initiatives which encompass men and women are well supported with few unintended consequences. These initiatives (e.g., subconscious bias training) focus less on changing or supporting women and more on changing the environment. In contrast, selective initiatives (e.g., female leadership programs) which focus on women, had mixed support and more unintended consequences. Specifically, they emphasize societal stereotypes that men are a better fit for the role of engineer and that women need help to adapt to the male-dominated environment. This study concludes with recommendations for employers to implement that will increase the participation and advancement of women in engineering.

Abstract

Because construction noise is one of the major factors affecting the health and safety of workers and nearby residents, it must be managed appropriately. Thus, this study aimed to map real-time noise information generated during construction using a sensor network and spatial interpolation. An optimal sensor was selected in terms of cost and performance by comparing noise measurements with an officially certified sensor in a semianechoic chamber. Field constraints that required or avoided placing the sensors were identified to secure satisfactory noise-estimation performance without interfering with fieldwork. A noise-estimation model was developed based on spatial interpolation, and its performance was evaluated in an experimental environment. The experiment was conducted in two different cases: the first case with only one noise source and the second case by adding another extra noise source. For each case, the optimal sensor combinations were found by considering the noise-estimation performance and the number of sensors installed. As a result, for the first case, the model showed an accuracy of 96.90% and a root-mean square error (RMSE) of 2.19, whereas the second case showed 97.40% and 2.31%, respectively. The noise information estimated by the model was visualized through simulation software for better field applicability. Detailed spatial and numerical noise information using the proposed system will help practitioners intuitively understand the real-time tendency of noise propagation, contributing to the determination of hot spots where reduction measures should be implemented. This information can also be used to more accurately identify workers continuously exposed to noise above dangerous levels and evaluate their actual cumulative noise exposure causing possible hearing damage.

Practical Applications

This paper presents a real-time construction site noise mapping system based on spatial interpolation. The results can be applied for three main practical purposes: (1) to provide the real-time noise level information of the entire construction site to establish effective reduction measures; (2) to evaluate the long-term hazards to workers caused by construction noise; and (3) to support prompt dispute settlement by presenting reliable data evidence such as a map of onsite noise pattern changes in response to civil complaints. These practical applications are expected to help field managers and workers reduce noise-related risks during construction and eventually support enhancing safety and productivity, two main issues with which the construction industry has struggled.

Recent initiatives from the government, nongovernmental organizations, and professional bodies in the Nigerian construction industry to encourage pro-environmental behaviors (PEBs) have created awareness on the benefits of engaging in PEB. However, PEB on construction sites in Nigeria remains low. This study explored the effect of perceived pro-environmental benefits (PPEBs) on PEB among construction site managers in Nigeria, and the mechanism through which PPEBs can lead to PEB through pro-environmental training (PET) and green work climate (GWC). A total of 257 usable responses were obtained from a survey of construction project managers and evaluated using the structural equation modeling method. The results obtained indicate that PPEBs had no significant effect on PEB, whereas PET and GWC fully mediated the relationship between PPEBs and PEB. This study’s findings fill existing gaps in the literature and provide academicians, policymakers, and construction industry practitioners with in-depth knowledge of other variables such as PET and GWC, through which PPEBs can translate into PEB on construction project sites.

Poor mental health has become rampant in the construction industry globally, causing productivity and profit losses for organizations as well as disabilities and diminished quality of life for workers. Addressing the challenge is critical for a progressive industry and healthy workforce. Existing literature discusses the causes and effects of poor mental health among construction workers. Yet, it does not, based on real-world incident data, explain vulnerable trades, common incident mechanisms, and recurring psychological injuries, nor does it examine variations of lost time within the incident severity outcome due to psychological injuries. The present study addresses these gaps by analyzing workers’ compensation offered to construction trades workers from 2008 to 2019 for psychological injuries in the Australian construction industry. Carpenters and joiners, electricians, plant operators, structural steel workers, and construction and plumbing laborers suffered more permanently incapacitating psychological injuries than other trades. Workers in projects located in metro, small rural towns, and very remote regions were more heavily represented in permanently incapacitating psychological injuries than workers in other locations. The worst psychological injury, which resulted in a combination of permanent incapacity and an extended period of lost time (over 3,000 h), was caused by anxiety combined with depression or stress, or posttraumatic stress disorder from experiencing traumatic incidents, workplace violence and bullying, or vehicle incidents. Age and gender of workers did not show a statistically significant association with psychological injuries. The findings offer practical insights for developing optimized occupational health and safety management programs for improving the mental health of construction workers.

Reliable construction workflow relies on timely discovery, analysis, and checking of compliance with contract terms, which are time consuming and inefficient tasks. Smart contracts enabled by blockchain technology have demonstrated promise in addressing the inefficiencies of data communications due to their merits of traceability, immutability, transparency, and self-enforceability. However, a smart contract’s inability to interact with real-world data is the main issue that impedes further implementation. Today’s increasing availability of as-built data provides automatic condition assessments that have great potential to automate smart contract executions. This research area is uncharted territory for the industry. This research selects a case study to present an automatic decentralized management framework by exploring image-based deep learning solutions to automate and decentralize the conditioning of smart contract executions enabled by a web3.js-based decentralized blockchain application. It was found that the model can automate management intelligence with minimal workflow interruptions by timely identification of bottleneck activities and enforcement of mitigation strategies. Project managers can use the blockchain prototype to enhance information sharing, remove key risks, and enable a reliable workflow with minimal management efforts.

Modular construction increasingly is used for building projects to improve productivity, quality, and sustainability. However, interfaces between modules and site-based works are complex, and often cause problems that hinder the wide application of the modular approach. This paper investigated problematic interfaces in modular construction and their causes, and developed prevention strategies. The research was carried out through a 1-year participatory case study with a modular building project, coupled with an interview-based survey with leading module suppliers in the United Kimgdom. Wide-ranging problematic interfaces were identified in physical, contractual, and organizational groups from the case study, and their interconnections and complexities were examined using social network analysis. The case study results highlighted the significance of physical problematic interfaces and indicated the centrality of the module supplier in both facing and causing the problematic interfaces, which was echoed by findings from the module supplier survey. Likewise, a variety of causes were identified in the technical, contractual, and organizational aspects, markedly, the lack of management and coordination, and poor communications. Effective communication between the module supplier and installer and all other subcontractors was found to be most critical to preventing problematic interfaces, for which the module supplier should advocate and the principal contractor should coordinate. This research demonstrated the interconnections and complexities of problematic interfaces and their causes in modular construction with empirical evidence, and highlighted the leading role of module suppliers in preventing problematic interfaces. The findings contribute to a better understanding of complex problematic interfaces in modular construction and provide valuable insights into interface management of modular building projects.

Public–private partnership (PPP) has been positioned as a relevant contracting method for developing large-scale infrastructure projects that entails potentially high-magnitude negative impacts on the environment. The effectiveness of the environmental impact assessment (EIA) is crucial to achieving sustainable development of these large-scale infrastructure projects. To unravel the drivers for the EIA effectiveness and the multiple combinations built by the complexity of these drivers, this paper analyzes 28 road PPP projects from Colombia employing a fuzzy-set qualitative comparative analysis (fsQCA) approach. This paper decodes conjectural causal links between specific conditions grouped in superordinate clusters (i.e., consultants’ capability, project features, and communities’ participation) and EIA effectiveness dimensions (i.e., normative, procedural, substantive, and transactive). Findings revealed that no single combination of causal conditions ensures multidimensional EIA effectiveness. This study demonstrated that EIA effectiveness relies significantly on the integration of specific features of three external stakeholders: consultants, nonpreferred proponents, and communities. This study constitutes the first empirical multidimensional identification of the combination of conditions that generate EIA effectiveness in road PPPs.

Despite the wealth of research that has sought to understand the effects of highway work zones, very little definitive information is available concerning the determination of work zone length (WZL) of rural highway rehabilitation projects. Despite its significant impact, adaptive models that holistically estimate reasonable WZLs are very rare. To fill this gap, this study first created a high-confidence data set through a series of scheduling and traffic simulations and subsequently identified critical factors affecting WZL through a descriptive factor analysis. Based on these data sets and findings, a novel decision support framework was developed to determine the most economical WZL in a balanced trade-off between motorists’ inconvenience level induced by traffic disruption and their opportunity cost. The practical applications of the WZL determination framework were then demonstrated through a systematic eight-step procedure, followed by an illustrative use case of an actual project. The results revealed that traffic loading and work zone duration are critical factors, with an important benchmarking point being traffic loading at approximately 41,000 vehicles per day. As the first of its kind, this study will help state transportation agencies devise sounder construction phasing plans by providing a means of establishing WZL that strikes a balance between travelers’ inconvenience and constructability.