Journal of Management in Engineering

Debris generated and left in the wake of a disaster is one of a community’s greatest obstacles in the ability to restore function. The objectives of this paper are (1) to provide systematic procedures to develop and visualize the vulnerability index (VI) based on three dimensions, and (2) to prioritize debris removal by integrating all dimensions into a geographic information system (GIS). The proposed methodology for vulnerability heatmapping consists of three dimensions: (1) physical, (2) infrastructure, and (3) environmental. An unmanned aerial vehicle (UAV) is used to quantify the debris three-dimensional (3D) volumetric information as part of the 11 metrics selected and evaluated to establish the vulnerability of a location. The proposed process incorporates all systems into a process to allow decision makers to quickly determine scope, complexity, potential impact, and recovery needs. Therefore, the outcome of this research will ensure maximum and timely utilization of limited resources to reach a greater number of people who might be facing life-threatening situations, enhancing the community’s resilience.

Abstract

In recent decades, there has been considerable research into how infrastructure systems and sectors mutually rely upon each other, how a single failure in one system can cascade and affect numerous systems, and how collective restoration sequences should be conceived. However, lacking in these discussions are how individual decision-making heuristics, intraorganizational structure and priorities, and interorganizational conflict influence infrastructure operator decisions, and how this, in turn, influences the collective performance and reliability of coupled infrastructure. Building upon literature on intra- and interorganizational conflict and governance, we develop a conceptual framework for contextualizing the operational and strategic decision patterns of operators of complex interdependent systems during situations of high uncertainty. This enables us to pinpoint (some) sources of conflict that, if addressed and resolved, could induce multiple beneficial collective outcomes. These benefits, in principle, include faster outage recoveries and better reliability. The framework is demonstrated in proof-of-concept case study of outages of an interdependent infrastructure system (electrical power, steam, water, and information technology) at a large university campus in the US. Using cognitive task analysis, system operators were interviewed about their experiences with outage events. The resulting qualitative data were assessed for patterns using the framework. We find that (1) improved communication to reduce ambiguity, (2) stronger relational norms, and (3) alternative contractual incentives and penalties may induce better coordinated maintenance and operations across the systems. Because interdependent infrastructure modeling often examines only incentives and penalties, our results highlight how qualitative analysis of individual and organizational conflict and governance can provide complementary insights.

Practical Applications

Interdependent infrastructure systems, which are coupled via physical, geographic, institutional, and cyber linkages, are becoming increasingly critical to modern society while at the same time being exposed to greater threats, such as climate change. Thus, understanding their resilience to hazards is of great importance to the continuation of the reliable services they provide. Much of the scientific work in the area of interdependent infrastructure resilience has focused on creating optimal system configuration and response strategies, which provides useful guidance, but often neglects that such configurations and strategies will have to be operated and implemented by managers and operators, who by nature of being human, do not always act optimally. Thus, a gap in understanding of how managers and operators might behave when given the opportunity to act only in their personal or their system’s interest potentially leaves optimal plans vulnerable. This study outlines a framework for identifying ways in which noncooperative behavior among managers and operators might lead to interdependent infrastructure systems deviating from optimal behavior. Furthermore, the framework links these potential areas of conflict with management and governance responses that can be implemented to foster resilience across systems.

Abstract

Postdisaster housing reconstruction (PDHR) requires robust, efficient planning, and coordination among dispersed prefabrication facilities and jobsites to maximize prefabrication benefits. Manual construction methods and inherent risks often lead to unforeseen incidents and delays. Previous off-site construction studies focused on specific factors, neglecting uncertainties and the improvement of the Social Vulnerability Index (SoVI). Considering all critical parameters, this study proposes a real-time optimized allocation using deep reinforcement learning (DRL) for PDHR projects. The framework employs the Q-learning algorithm to generate the best real-time schedules for each prefabrication facility based on a current work status to complete a planned project. This is illustrated through a case study with ten project sites, four types of module layouts, and four prefabrication facilities. To demonstrate the superiority of the DRL-based method, the model was compared to the Monte Carlo Simulation and the Genetic Algorithm (GA) for the nine criteria related to time, cost, and social vulnerability. The DRL-based algorithm optimized the time parameter by reducing delay by 33.6% compared to the Monte Carlo simulation and by 46.4% compared to the GA. Similarly, it reduced missed deadlines by 35.1% compared to the Monte Carlo Simulation and by 13.3% compared to the GA. When comparing the cost parameter, the DRL model reduced overall cost by 3.4% compared to the Monte Carlo simulation and by 18.6% compared to the GA. In addition, it was able to prioritize the more vulnerable jobsites over less vulnerable ones to reduce delays and missed deadlines. In this regard, the proposed approach contributes to the body of knowledge by introducing a new automated model for PDHR project work distribution, considering productivity, cost, time, resources, uncertainties, and SoVI, thereby improving informed decision-making and overall project performance.

Practical Applications

This research presents an artificial intelligence (AI)-powered system tailored for managing large construction projects, especially crucial in postdisaster rebuilding efforts. By harnessing advanced AI techniques, this system streamlines operations in prefabrication factories, ensuring timely and cost-effective completion while considering critical social needs. The key factors that set this approach apart are adaptability and continuous learning. By constantly analyzing and learning from real-time project data, the system dynamically allocates resources, minimizing delays and cost overruns while also prioritizing areas with higher social vulnerability. However, it’s important to note that the system’s effectiveness relies on specific factory layouts and the availability of up-to-date information. In practical terms, implementing this system could revolutionize how construction projects are managed. It offers real-time insights, enabling informed decision-making, streamlined operations, and enhanced collaboration among all involved stakeholders. Beyond construction, the potential applications of this technology extend to thoughtful urban planning and expediting recovery in disaster-stricken areas. Despite existing challenges, this research paves the way for AI-driven solutions that not only optimize project efficiency but also integrate social responsibility into decision-making processes. It sets a promising path toward a future where AI plays a pivotal role in optimizing projects while concurrently addressing the critical needs of society.

Low-carbon procurement policies are growing rapidly at the local, state, and federal levels within the US and globally. Despite the prevailing recognition of the importance of low-carbon procurement, the research effort to investigate the impact of public procurement on different project delivery aspects is notably limited. To this end, this study investigates the impact of low-carbon procurement policies on the bidding dynamics within the construction industry. First, parametric distributions were fitted to competitors’ historic markup records. Second, two low-carbon public procurement policies were considered, the lowest eligible bid and the performance discount rate. Third, the environmental considerations of different procurement approaches were integrated into Friedman’s and Gates’s bidding models to estimate future bidding outcomes such as optimum markup value, probability of winning against all competitors, and expected profit. Finally, to showcase the practicality and reliability of the proposed framework, it was applied to a case study from the literature. Moreover, a scenario analysis was conducted to investigate the impact of environmental considerations, such as varying the global warming potential (GWP) threshold limit, on the bidding outcomes. The findings provided valuable insights into how the integration of environmental considerations, coupled with the traditional cost factor in bid evaluations, serves as a powerful incentive for contractors to prioritize the use of low-carbon construction materials. Also, the findings highlighted an asymmetrical impact of varying the GWP threshold limit on low-carbon procurement approaches, revealing substantial shifts in the outcomes of the lowest eligible bid approach compared to marginal variations in the performance discount rate approach. Moreover, results showed that Gates’s model consistently tends to predict superior bidding outcomes compared to Friedman’s model. This study contributes to the field of low-carbon procurement by providing insights into the bidding dynamics influenced by low-carbon procurement policies and the potential effectiveness of these policies in improving the profitability of contractors. Overall, this research offers practical insights and actionable strategies that contractors and industry professionals can implement to navigate the complexities of various procurement and bidding scenarios.

Mega construction projects (MCPs) have long periods, complicated environments, a multiplicity of mechanical equipment and personnel, and a large number of safety risk factors on sites. The different safety risk factors of MCPs have coupling effects among them, which can produce new risks and even lead to an accident. These factors present a major challenge for project managers to take on risk management in MCPs. Although previous research has done much work on risk factor identification and control in MCPs, less attention has been given to the coupling effects among risk factors, which affect the identification effect of key safety risk factors and their coupling paths. To compensate for this gap, this research used the latent Dirichlet allocation (LDA) topic model to identify risk factors in mass accident reports, the N-K model to calculate the coupled style, and then coupled the N-K model and the social network analysis (SNA) model to identify the safety risk factors and determine the coupling paths in MCPs. The results reveal the following: (1) six first-level subsystems are identified, and there are 31 second-level safety risk factors included in MCPs; (2) $H_1$ (low safety awareness), $H_2$ (CAT operation), $H_6$ (improper safety protection measures), $P_4$ (improper safety supervisors), $P_5$ (imperfect management system), $S_3$ (owner’s mistakes), and $M_2$ (improper maintenance of equipment) are the key safety risk factors in MCPs; and (3) unfavorable environmental factors can easily lead to the emergence of the multirisk coupling of $G\text{-}S\text{-}P\text{-}H\text{-}M\text{-}E$, which should be carried out by special management. This research provides a reference for the identification and control of safety risks in MCPs and contributes to the development of complex system risk management and control research.

This study presents a method for estimating construction costs, even when dealing with limited and unreliable data, to enhance decision-making in the early project stages. Owners, particularly in green building projects, often face challenges due to the scarcity of usable data, making cost estimation a complex task. They struggle to differentiate between costs associated with existing buildings and green buildings. To address this issue, we introduce a novel approach that leverages conditional tabular generative adversarial networks (CTGANs) for data augmentation, overcoming the limitations of relying solely on historical data. This involves training an artificial neural network (ANN)–based model using synthetic data, effectively addressing the scarcity and imbalance present in the original small data set. Compared to models trained exclusively on the original data set, our approach yielded a remarkable reduction of approximately 66% in root-mean-square error (RMSE), while increasing the validity from 0% to 15.09%. This study not only improves construction cost estimation but also facilitates more informed decision-making for owners, even in cases with limited and unreliable data, ultimately contributing to the efficiency of the construction project planning process.

Abstract

Facing budgetary limitations and deteriorating infrastructure, government agencies and municipalities are contemplating a shift from reactive maintenance and repair (M&R) approaches to preventive M&R for road infrastructure. However, ambiguities concerning the long-term returns of preventive M&R for road systems deter their commitment to this transition. Although previous studies have demonstrated the advantages of preventive M&R, a comprehensive understanding of its value remains insufficient, particularly considering budget restrictions, varying levels of required serviceability, the types of preventive treatments, and the extent of preventive M&R. To bridge this knowledge gap, this study evaluated the economic impact of preventive treatments over the lifespan of urban highways, taking an urban highway in South Korea as a case study. The analysis results reveal a significant decrease in costs beginning after the first decade of the systematic applications of preventive M&R, with potential savings ranging between 20% and 30% over a half-century period. The findings will inform road M&R agencies or municipalities about cost profiles when employing preventive treatment methods (PTMs) for road M&R, thereby facilitating PTM implementation to reduce long-term road M&R budgets.

Practical Applications

Government agencies and municipalities, grappling with limited budgets and aging road infrastructure, increasingly are considering preventive maintenance and repair (M&R) strategies over traditional reactive approaches. This shift, although promising, faces uncertainties regarding its long-term economic benefits. Prior research has underscored the advantages of preventive M&R, but a thorough understanding of its financial implications, particularly in the context of budget limitations, diverse serviceability needs, and various preventive measures, still is lacking. This study bridges this gap by evaluating the economic impact of preventive M&R on urban highways, using a South Korean urban highway as a case study. Our findings indicate a notable reduction in costs, especially after the first decade, with potential savings of 20%–30% over 50 years. These results offer crucial insights for road maintenance agencies and municipalities, presenting a clear cost–benefit analysis of implementing preventive M&R strategies. Such information is pivotal in aiding these entities to make informed decisions, potentially leading to significant reductions in long-term road maintenance budgets.

Abstract

Construction waste causes severe environmental problems. Reducing construction waste generation becomes a major policy measure of governments to achieve sustainable development. Offsite construction approaches are effective for reducing onsite construction waste. Nevertheless, there is a lack of a clear understanding of the types and amounts of construction waste that different offsite construction approaches can reduce. This study aims to investigate the performance of the modular construction (MC) approach in construction waste generation and reduction and how it differs from that of the nonmodular offsite construction (NMOC) approaches by comparing 59 selected cases of building projects that adopt these offsite approaches compared with stick-built construction. Results indicated that the adoption of offsite construction contributed to significant onsite construction waste reduction. On average, adopting MC reduced 78.8% of overall construction waste, outperforming other construction approaches. The level of prefabrication influenced onsite construction waste generation and reduction the most, followed by the other properties like building typology, density, and material used. Seven knowledge gaps were identified in the area of waste identification, quantification, and assessment. The findings demonstrate the advantages of using offsite construction in waste control and management and add to the existing body of knowledge on onsite construction waste generation and on waste reduction through adopting MC. The identified knowledge gaps point directions of future research on waste reduction through offsite construction.

Practical Applications

The results of this paper suggest that the adoption of offsite construction can contribute to significant onsite construction waste reductions compared with conventional stick-built construction. Specifically, buildings adopting the modular construction (MC) approach can reduce overall construction wastes by around 80% and outperform other nonmodular offsite construction approaches, including those that adopt one-dimensional prefabricated beams and columns, two-dimensional prefabricated panels, or three-dimensional prefabricated pods, based on the analysis of 59 building cases covering different levels of prefabrication, building typologies, building densities, and building materials. This study contributes to the investigation of onsite construction waste by proposing a systematic assessment method addressing different construction waste types and assessment criteria. It facilitates a better understanding of the advantages of offsite construction in terms of waste control and management, especially MC, which was underexplored in the literature. Seven knowledge gaps were identified in the area of construction waste identification, quantification, and assessment. Addressing these knowledge gaps will contribute to better construction waste control and management, especially in MC projects.

Comprehending the cognitive processes underlying hazard identification is crucial for enhancing worker safety behavior in construction. Recent studies have explored eye-tracking technology’s potential in understanding human cognition across contexts. However, limited research delves into the intricate cognitive processes linking eye movements and hazard recognition, particularly in the context of situational awareness (SA). Thus, this study investigates the relationship between eye movement data and SA’s cognitive processes in hazard recognition virtual reality (VR) scenarios at construction sites. The study employed experiments with 36 participants identifying construction hazards across six VR scenarios, yielding 216 trials. Eye movement data were collected via the VR headset’s eye-tracking device, concurrently recording hazard recognition performances. The results uncovered valuable insights into the correlation between eye movement patterns and global and local SA. In the context of global SA, time to and after first fixation elucidated the distinct variations among individuals in terms of perception (Global Level 1 SA) and comprehension times (Global Level 2 and Level 3 SA) across various hazard scenarios. In the realm of local SA, more fixations and saccades (Local Level 1 SA) were observed during the first dwell, underscoring the significance of the first encounter with a hazard. Additionally, pupil dilation, indicative of increased mental workload, occurred upon successful hazard recognition (Local Level 2 and Level 3 SA). These findings highlight the explanatory potential of various eye movement data types for diverse SA levels. They can serve as effective SA indicators in hazard recognition contexts, enhancing understanding of cognitive processes and refining assessment and training for SA in hazardous settings.

Infrastructure development is crucial for any country because it has significant effects on both social welfare and economic growth. Due to limited financial resources, the government prioritizes the infrastructure investment that maximizes impact and resource efficiency. However, many methods to support the decision-making undermine to derive insights from project experiences and ensure time- and cost-effectiveness. This study aims to identify the critical factors for the preliminary feasibility study (PFS) of large-scale infrastructure investment in South Korea by developing decision-tree classifiers based on the type of infrastructure (point, linear, and plane). The classifiers use data from 1,905 evaluations of 354 projects in Korea, classifying them as go, pending, or no-go with accuracies of 84.64%, 82.08%, and 87.88% by type of infrastructure, respectively. The critical factors affecting PFS are benefit-cost ratio, city-level development rank, regional development level, and total project cost among the 13 decision factors of PFS. Although multicriteria decision-making is supposed to comprehensive evaluation of various factors, some factors have governed the result, whereas others do not affect. The impact of these factors is discussed depending on the type of infrastructure. The results show the potential for this approach to improve efficiency in terms of time and cost by enabling early and reliable project screening. Although the study focuses mainly on South Korea, findings can provide valuable insights for more informed and efficient decisions to other countries.

In this article, we discuss the concept of safetywashing defined as the strategic practice of promoting, marketing, and branding of safety practices without full disclosure of negative information to improve the image of the organization. The research seeks to answer two questions: first, what are safetywashing strategies? Second, what are the effects of safetywashing strategies? To study this, 106 news articles relating to construction safety in India, as well as 439 reader comments on them, were systematically collected and their contents analyzed to compile multiple case studies which had evidence of safetywashing. We analyze multiple instances from these case studies to build theoretical insight into these strategies and their effects, using an approach anchored in a social exchange theoretical framework. We highlight different safetywashing strategies employed in the construction sector, such as safety as a project objective, explaining safety initiatives, associating with pioneers, as well as investing in safety. These strategies lead to accepting of organizations, prioritizing safety, and diverting focus, all of which have different implications for safety practice in the construction industry.