Journal of Construction Engineering and Management

This study investigated the myriad risk factors contributing to cost overruns in healthcare construction projects. It determined the impact of managerial, financial, workerpower, and environmental considerations, focusing on the intricacies of healthcare facilities. By employing a modified fuzzy group decision-making approach (MFGDMA), the research methodically quantified the subjective perceptions of diverse stakeholders involved in project execution. Results reveal that labor-related issues—such as shortages, productivity, and skill deficits—along with managerial delays, particularly in decision-making and change orders, significantly contribute to cost escalations. The study’s novelty lies in its integration of fuzzy logic to defuzzify risk perceptions, providing a clear hierarchy of risk factors, which assists in the strategic prioritization of risk mitigation efforts. Additionally, it demonstrates a risk response plan considering the fuzzy-based risk assessment findings, which can serve as a hands-on tool guiding healthcare project practitioners to develop their risk management plans and monitor cost overrun risks throughout the execution phases of a project. The application of the research findings suggests policy implications for improved risk management frameworks toward the development of more-resilient and -efficient strategies for managing healthcare construction projects without prohibitive cost overruns.

Abstract

A successful construction project relies heavily on planning and control. The prevalent practice is to adopt a single control method throughout a project, such as earned value management (EVM), which integrates costs and schedules to monitor project targets throughout execution. Although a single control method may be effective at a certain project phase, it might overlook important perspectives and be insufficient for other stages. Therefore, this study introduces a framework to evaluate the necessity for a multidimensional alternative perspective in project control. The study aims to diagnose some limitations of using a single method like EVM and demonstrate how adding another perspective, such as the last planner system (LPS) for project control, yields better insight into project performance. A design science research (DSR) methodology is adopted to address five key questions by conducting quantitative data analyses and Monte Carlo simulation for a large-scale project. The results show that although EVM performs well when dealing with controlled performance variability, it may yield undesirable results in uncontrolled performance variability, impacting its forecast accuracy. Moreover, the aspects covered by LPS in the project proved to be complementary to EVM. Furthermore, the results indicate an inconsistent divergence between planned and actual activities, resulting in disrupted flow and a purging effect, which can be attributed to the lack of a multidimensional approach in project control. The practical implication of these findings is that using a multidimensional perspective offers a more robust and adaptable project control strategy that improves forecast accuracy and project flow, especially under uncontrolled performance variability conditions, where single-method approaches like EVM alone may falter. Hence, adopting a multidimensional perspective can significantly enhance the management of construction projects, leading to more reliable outcomes and efficient resource utilization.

Practical Applications

In the construction industry, project managers are continuously seeking more efficient strategies to ensure projects are completed on time and within budget. A widely adopted technique for tracking project progress is earned value management. However, this research indicates that EVM alone may not suffice, particularly in the face of unforeseen challenges such as supply chain disruptions or abrupt work stoppages. This study advocates for the integration of EVM with the LPS, a methodology that enhances planning and resource utilization. The authors found that relying solely on one approach may overlook critical issues, especially those arising unexpectedly. Combining EVM and LPS equips project managers with the tools to anticipate and navigate both foreseeable and unforeseen obstacles. Why is this finding important? It addresses the crucial issue of managing unpredictability in construction projects. By merging these two project management methodologies, project managers gain a more comprehensive understanding of project dynamics, enabling them to make more informed decisions and adapt more fluidly. This synergy not only promotes the efficient use of resources but also fortifies project management practices against the inevitable uncertainties of construction work. It is about enhancing the project management approach, ensuring resilience even when the unexpected occurs.

Project monitoring has become crucial to the success of organizations in an increasingly competitive industrial environment. As such, earned value management (EVM) is an essential part of project cost control. Over the years, the scientific literature has mainly focused on developing mathematical models to improve EVM, whereas very few researchers have examined the implementation of EVM in organizations. Although some standards provide general guidelines for implementing the methodology, they rarely consider the maturity of the organization or the strategy required to implement it. This paper aims to develop an EVM implementation plan method for organizations. The method identifies the activities to be carried out, their sequencing, and the corresponding roles, techniques, and deliverables associated with each activity. The analysis of this phase of the existing situation allows for a consideration of the organization’s maturity level based on a process mapping analysis and its audit of the organization. Relevant recommendations are then identified before defining EVM implementation strategies according to the organization’s needs, constraints, and resources. Interviews with professionals allowed for the verification of the relevance of the recommendations before assigning them to the defined implementation strategies at every stage of implementation. The impact of each strategy on the organization’s processes is mapped and analyzed to determine the expected costs and benefits. The implementation method was tested successfully within a firm.

Infrastructure workers experience high rates of psychological distress and suicide. Social capital (e.g., co-workers, friends, family) and social support (e.g., emotional, practical, informational) help to minimize distress. This study explores how social capital and social support contribute to psychological distress and if accessing social capital to provide social support is different for distressed compared to non-distressed workers. A sample of 220 infrastructure workers recruited online from Canada, the United Kingdom, and the United States of America was used. The study explored social capital (sum and diversity) along with social support and who the workers would approach first for each type of social support. It found that increased social capital was associated with higher distress, whereas lower social support was associated with higher distress. The primary contribution of this research indicates that although distressed infrastructure workers have more social capital available, they may not be obtaining the necessary social support needed from their networks. Also, as some distressed workers indicated they approach work colleagues to receive some types of social support, there may be an opportunity for workplaces to provide social support to co-workers to alleviate the gap in support and help improve psychological well-being.

Historical construction accident reports have been widely used to gain insights into the primary causes of past incidents in construction. Previous studies have successfully identified accident causes and affected body parts. However, there remains a gap in understanding the high-risk actions of workers. This study aims to fill this gap by conducting a novel investigation into the most prevalent sequential patterns between workers’ actions prior to severe accidents. The study extracted sequential accident patterns by applying the PrefixSpan sequential pattern mining algorithm on a large database of action-accident-consequences manually built from the Occupational Safety and Health Administration’s construction accident reports. Social Network Analysis was then performed to determine high-risk workers’ actions leading to severe accidents. Additionally, statistical tests were employed to explore the sectoral differences in the rank of high-risk actions. The study revealed the priority for 24 high-risk actions leading to severe accidents in construction. The ranking of these actions was found statistically different between construction sectors. Organizations can utilize the findings to develop targeted safety programs and interventions to mitigate future incidents in the construction industry. This study, however, was limited by the size of the sequential database, resulting from the manual data annotation process. This issue could be mitigated in future research by exploring semiautomated annotation approaches.

Abstract

Design coordination in hydropower projects is both complex and challenging due to their scale, environmental impact, or technological complexity. The current understanding of how to effectively enhance it is limited and often contradictory. This research explores five key conditions impacting design coordination: technical conditions, digital design capabilities, designers’ dynamic capabilities, partnering, and incentives. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) on 28 large-scale hydropower project cases, this study identifies five distinct configurations that facilitate high-level design coordination, which can be grouped into three types: capability-centric, stakeholder-centric, and a hybrid approach combining both capability and stakeholder focus. The findings reveal that both high- and low-level design coordination outcomes can be achieved through various configurations of these conditions. This study advances the understanding of design coordination by adopting a configurational perspective and contributes to the literature on the adoption of emerging technologies and practices.

Practical Applications

Design coordination in hydropower projects is complex but critical because it can influence the project’s feasibility, environmental harmony, and social acceptance. This research explores five key conditions impacting design coordination among project stakeholders: technical conditions, digital design capabilities, designers’ dynamic capabilities, partnering, and incentives. The results of this study offer significant managerial suggestions to project managers in the hydropower industry. First, enhancing design coordination by investing in improving technical conditions may be valuable but is only useful when combined with strengthening designers’ digital design capabilities and other strategies, such as developing partnering relationships with stakeholders to engage them in coordination. Even then, the effect may be marginal. This finding suggests that to fulfill the desired effectiveness when using new technologies, managers of design companies should pay attention to hiring capable personnel and improving organizational capabilities for developing partnerships with stakeholders. Secondly, achieving high-performance design coordination seems easier than avoiding low-performance design coordination because applying a few high-level strategies may result in high-level design coordination. The finding implies that even strengthening designers’ dynamic capabilities can achieve high-level design coordination given underperforming technical conditions. Managers should be more optimistic when improving design coordination even if financial support for advanced technologies is lacking.

Highway operation and maintenance (O&M) systems exhibit characteristics of multisubsystem correlations and interactions among multiple disaster-causing factors over time. Traditional risk-centered approaches, which focus on postevent effects and causes, fail to address these challenges adequately. This study aims to devise a methodological framework that is multisubsystem and multifactor dynamic in nature, intended for assessing, analyzing, and enhancing the resilience of highway O&M systems. This framework comprises a qualitative component, utilizing fault tree analysis (FTA) to identify resilience factors within each subsystem, and a quantitative aspect, employing the improved human factors analysis and classification system (HFACS) and fuzzy dynamic Bayesian networks (FDBNs) for resilience assessment. Subsequently, a case study of the Shenyang-Dalian Highway (SDH) in China was employed to demonstrate the application of this methodological framework. The findings highlight the need for enhancing the resilience of highway O&M systems, particularly by focusing on personnel and equipment factors within the absorption and resistance subsystems and prioritizing organizational and personnel factors within the recovery and adaptation subsystems. Furthermore, dynamic strategies tailored to different O&M objectives were proposed. These frameworks contribute to the existing knowledge base of highway O&M systems by integrating resilience theory into performance evaluation and improvement efforts amid uncertainty.

Prefabricated construction has gained traction in sustainable construction practices. As a modular construction method, the prefabricated component (PC) supply chain involves key participants—producer, transporter, and assembler—operating in a point-to-point cooperation pattern (i.e., single project–single producer–single transporter). However, a disparity in resource allocation between the supply and demand sides leads to inefficiencies within the PC supply chain. Thus, this paper introduces the platform service pattern concept combined with evolutionary game theory, constructing a tripartite evolutionary game model to analyze the dynamic evolution mechanism of their behavioral strategies. Extending from a point-to-point cooperation pattern, this paper analyzes a multi-to-multi network structure (i.e., multiple projects–multiple producers–multiple transporters) and utilizes evolutionary game theory to simulate a stakeholder’s strategies. Beyond maximizing the economic profits, we also include sustainable objectives into consideration, and we explore how project and environmental subsidies impact multiparty collaborations and their strategy selections. Specifically, three prefabricated components supply chain cooperation patterns (PCSCCPs) with multiparty dynamics are derived: standardized PC supply chain cooperation pattern (ST-PCSCCP), green and standardized PC supply chain cooperation pattern (GS-PCSCCP), and mixed PC supply chain cooperation pattern (MX-PCSCCP). They are developed and compared by using empirical data for model parameter selections. The results show that all patterns achieved simultaneous production and transportation for multiple project orders and all ensure that construction projects meet sustainability requirements. Additionally, MX-PCSCCP requires the minimum subsidies.

This study aimed to investigate and analyze the performance of twelve machine learning (ML) algorithms in estimating the construction time and cost of drill and blast tunnels. Thirteen tunnels located in different regions of Iran were selected, resulting in 900 data sets. Ten parameters were identified as influential factors affecting the construction time and cost of these tunnels. 80% of the data set was used for training, while the remaining 20% was reserved for testing. Additionally, 288 unseen data sets were utilized for evaluation purposes. All the algorithms demonstrated accurate performance on the test data sets, with R-squared values exceeding 0.93. However, only the Gaussian process regression algorithm achieved satisfactory results on the unseen data sets. Furthermore, a graphical user interface (GUI) was developed based on the trained ML models. This GUI allows real-time estimation of the time and cost of drill and blast tunnels and can be updated during construction.

Cable cranes serve as critical lifting equipment in large-scale construction projects such as dam construction, bridge building, and mining. Particularly in concrete dam construction, cable cranes are the preferred equipment for transporting concrete due to their flexible spatial moving capacity and large-span coverage. However, because of the narrow dam construction site, the intensive and concurrent transportation activities of the cable crane group are proven to cause space conflicts, resulting in low operation efficiency. Effective planning of cable crane operations is conducive to accelerating construction progress, improving the productivity of cable cranes, and ensuring a safe construction scenario. The planning of cable crane operations involves task allocation in collaborative construction (among several cable cranes) and parallel scheduling under complex spatial constraints. This paper develops an automatic planning system for cable crane operations in concrete dam construction that can directly output the optimal transportation plan of cable cranes, requiring only the input of relevant construction parameters. First, a two-sided matching model is proposed to address the task allocation problem among cable cranes. Second, a simulation model for the concrete transportation process of the cable crane group is established to solve the parallel scheduling problem and avoid collisions. Third, an improved ant colony algorithm is proposed that constructs initial solutions based on the two-sided matching model, calculates key construction indicators using the simulation model, and applies a multiobjective decision making method to find the optimal solution. Finally, an actual dam construction case validates the superiority and reliability of this proposed method. Compared with the conventional engineering approaches, the planning results of this proposed method demonstrate a 12% reduction in pouring duration and a 25% enhancement in the utilization balance of cable cranes, effectively increasing productivity and accelerating construction progress.

Engaging muscle groups in proper order is an essential skill for human motor tasks such as welding, contributing to musculoskeletal health and task performance. However, learning the correct muscle engagement strategy for a dedicated human motion task like welding can be challenging due to the difficulty of acquiring the implicit information and carrying it over to novice trainees, which is becoming the bottleneck of workforce training. This paper proposes to monitor and model the implicit muscle engagement strategies using surface electromyographic (sEMG) sensors and provide real-time feedback to novice trainees via vibrotactile devices according to the muscle engagement models. The differences between expert trainers’ and novice trainees’ muscle engagement strategies are processed to control the vibrotactile patterns and magnitudes. A human-subject experiment ($N=25$) was performed to validate the system design in welding training. Our results, which demonstrated the effectiveness of the proposed method in capturing motor control patterns and improving motor skill learning, provide a strong foundation for its application in real-world training scenarios. Furthermore, it was found that providing haptic feedback according to muscle engagement was more effective than visual feedback, especially in force control. It was also noticed that haptic feedback could alleviate the reliance on external feedback compared with visual feedback. This paper presented a novel method and its implementation for welding training, contributing to innovative training methods for the workforce and beyond.

Abstract

Enhancing construction worker performance, safety, and project management through automated activity classification is a promising endeavor. By extracting activity-level information, this technology provides valuable insights for informed decision-making, facilitating project schedule adjustments, efficient resource management, and improved construction site control. Previous studies in this domain focused on basic activities, neglecting optimal sensor placement and no regard for worker comfort. This paper extends beyond existing research, encompassing a broader range of complex construction activities and surpassing current methods. Utilizing unobtrusive wearables like a smartwatch and smartphone, the study determines optimal sensor positions (dominant/nondominant wrist, dominant/nondominant leg pocket). Notably, it introduces a novel deep neural network structure, merging long short-term memory (LSTM) and convolutional layers, offering an innovative solution for automated activity classification tasks in the construction industry. This model extracts activity features automatically reducing the need for manual feature engineering and performs classification with few model parameters indicating efficiency in terms of computational resources and memory requirements making the model more suitable for real-time applications and deployment on resource-constrained devices. By leveraging the strengths of both convolutional layers and LSTM, this approach offers a powerful and efficient solution for activity classification tasks. An experimental study was carried out to recognize four different activities: manual excavation, rebar stirrups, cement plastering, and bar binding. These were performed by four subjects (three males and one female) for 30 s each with different positions of smartwatch and smartphone producing 24,080 data points. Results indicate the optimal positioning of wearables to be smartwatch on dominant hand and smartphone on opposite leg pocket because of a balanced and effective coverage of the relevant movements and contextual information yielding 98.18% accuracy, 98.20% precision, 98.17% recall, and F1 score of 98.17%.

Practical Applications

The integration of LSTM and convolutional neural network (CNN) architecture and strategic sensor placement on wearables holds promise for revolutionizing construction project management. By optimizing sensor placement, we can improve the accuracy of activity recognition, allowing for better tracking of worker productivity and process optimization, ultimately leading to increased efficiency and productivity on construction sites. Additionally, strategically placing sensors on wearables enables real-time monitoring of worker movements, facilitating the early identification of safety hazards and ergonomic issues, thereby enhancing worker safety and reducing workplace injuries. Furthermore, integrating wearable technology with smart construction technologies enables real-time data exchange and analysis, promoting collaboration among stakeholders and enhancing progress monitoring and control. Leveraging data from strategically positioned wearables allows for comprehensive visualization of construction processes, aiding in the detection of project bottlenecks and streamlining project management strategies. Lastly, analyzing work allocation data from optimally positioned wearables improves project bidding accuracy, minimizing cost estimation errors and increasing profit margins. These findings highlight the transformative potential of LSTM-CNN architecture and optimal wearable placement in construction project management.

Ongoing roadway construction and maintenance activities are essential for sustaining and improving infrastructure. Nonetheless, work zones in the United States continue to experience concerning frequencies of occupational and traffic accidents. Previous research highlighted multiple factors influencing work zone safety, yet their relative impact and importance remain unclear. This study addresses this gap following a multistep methodology. First, 32 safety factors were identified through an extensive literature review in a previous study. Second, a survey was administered among 298 professionals from diverse backgrounds, with an average of 17.75 years of construction experience and 13.78 years of work zone experience, to assess the importance of identified factors on worker safety. Third, data reliability was assessed using Cronbach’s alpha, and survey responses were evaluated to uncover experts’ perceptions regarding the criticality of factors affecting worker safety. Lastly, clustering analysis was utilized to group the recorded levels of work zone safety factor criticality. Survey results indicate that driver-related factors, such as drivers’ level of attention and unsafe driving, are perceived as most critical to worker safety in work zones. Conversely, factors associated with motor vehicles, conditions of construction equipment, and technological sophistication are perceived as the least critical. Findings from the clustering analysis revealed three groups of factors with varying levels of criticality, where the most critical group comprised driver, work, and design-related factors. The findings of this study contribute to guiding state DOTs and safety practitioners in enhancing their safety practices and culture with a focus on worker-centered perceptions. Additionally, this analysis identifies key areas for future research that are vital for improving worker safety in work zones. These include exploring underexplored factors, such as driver impairment, level of attention, and clarity of signage and markings, among others.

Abstract

Project bundling is an innovative project delivery approach that combines several projects under a single contract. While previous studies have provided important information about different project bundling-related aspects, none have developed guidelines to choosing the best project delivery method (PDM) for bundled projects/contracts. Also, despite that some of the existing research efforts have offered tools to identify the optimal PDM, such studies were conducted for single projects rather than for bundled projects which significantly differ from a normal project in terms of complexity and implementation considerations. Hence, this paper develops a data-driven decision support tool that helps agencies in identifying the optimal PDM for their bundled projects by leveraging machine learning algorithms and domain knowledge while also considering the characteristics and goals of the bundled program. This proposed tool considers and compares the following 5 PDMs: design bid build (DBB); design build (DB); construction manager/general contractor (CM/GC); indefinite delivery/indefinite quantity (IDIQ); and public private partnership (PPP). First, data from previous project bundling case studies were used to identify bundling opportunities (on the program or strategic level) as well as bundling objectives (on the project or contract level). Second, a machine learning model (i.e., multinomial naïve Bayes classifier) was developed to generate a probabilistic distribution for the relative suitability of the five PDMs on the strategic bundling program level. Third, a survey was developed and distributed to collect expert’s domain knowledge on the importance of the different project bundling objectives (i.e., on the project or contract level). Lastly, an easy-to-use decision-support tool was developed to calculate individual scores for the different 5 PDMs so that the best PDM could be identified. Ultimately, this paper presents an intuitive and easy to implement tool for selecting PDMs for bundled projects based on the integration of machine learning algorithms and domain knowledge.

Practical Applications

This paper provides numerous practical applications. More specifically, this research equips project owners with a data-driven tool to select the optimal PDM for their bundled projects by considering factors and characteristics on the overall program level as well as on the project level. This tool also helps in properly allocating risks between the project parties or stakeholders involved in the delivery of bundled projects. The developed tool in this paper is integrated into user-friendly excel spreadsheets with automated calculations, and thus it automates the PDM-related decision-making process, which makes it easy to implement in practice. Consequently, by selecting the optimal PDM, the proposed decision-support tool would enable project owners to enhance their bundling practices, improve the performance of their bundled program, maintain costs within identified budgets, improve the schedule of their bundled projects, and better capitalize on the various benefits of the bundling approach.

Off-site construction projects have demonstrated the potential to drive transformation in the construction sector to achieve better outcomes in terms of high precision and efficiency of deliveries through the collaborative design and fabrication processes, long-term stakeholder engagement and relations across projects, product standardization, risk-sharing strategies, and investment in a strong integrated supply chain. However, repeating the successful off-site construction projects to scale the benefits can be challenging considering that construction projects vary across regions and contexts. The streamlined processes and a consistent collection of information requirements should be leveraged to alleviate the challenge and help stakeholders effectively adopt the off-site construction approach. To achieve this goal, this study defines the information requirements across multiple stages of the off-site construction supply chain, using the case study approach to delineate the information requirements for an off-site construction company located in Edmonton, Alberta, Canada. The evaluation and interpretation of the case study underscore the need for consistent collection, storage and utilization of logistics, construction, and postconstruction activities information to tie back to sales and design activities. To facilitate the activities integration, an information requirement framework is proposed for off-site construction processes based on the learnings collected from the case study and a digital twin technology platform. Although the reuse and interoperability of information among various systems remains a long-standing issue to prevent the wide adoption of off-site construction, the findings and the proposed information requirement framework can be an integral part of the potential guidelines for successful implementation of off-site construction.

Public–private partnerships are an important tool for infrastructure development, but variability in bids can undermine value for money. This study investigated bid variability in India’s initial two auction road assets under the Toll-Operate-Transfer (TOT) model. A comparative case study research design was utilized involving an in-depth analysis of the TOT I and TOT II auctions. Data was gathered from project reports, guidelines, news articles, stakeholder interviews, and then thematically analyzed. The analysis revealed substantial bid variability within and between the auctions. The findings showed bid variability within the auctions depended on the information gaps owing to the timing and quality of the information released by the auctioneer, the tender conditions and tender process, potential value enhancement and securitization of assets by the bidders, heterogeneous bidder objectives across asset types, and strategic bidding assumptions. The bid variability between the TOT I and TOT II auctions depended on asset quality, declared reserve price, sociopolitical factors, and the prevailing business conditions. While TOT I road quality and growth prospects encouraged strong bidding, overpricing, shifting business scenarios, and political uncertainty in TOT II states depressed bids below expectations. Results indicated that transparency around asset characteristics and traffic forecasts enables realistic bid assessments to control variability. It can inform bid rationalization and pricing mechanisms for future auctions of public infrastructure. Setting reserve prices reflecting project specifics also helped anchor bid ranges. Attracting larger bidder pools countered the influence of overly optimistic/pessimistic valuations. Gradual expansion into new geographies allowed adaptive policy learning.

Quality II is a nascent paradigm (i.e., a distinct set of concepts) for reducing and containing errors and mitigating rework in construction. It has emerged from empirical observations of best practices implemented in two large-scale infrastructure assets delivered using alliancing—a relational delivery strategy—underpinned by: (1) a contractual joint venture agreement (CJV) that fosters a collaborative “no-blame” culture where all parties aim to deliver best-for-project outcomes; (2) sharing of risks and rewards; (3) delivering value for money; and (4) a program of continuous improvement. Questions, however, surround the generalizability of Quality II beyond the alliances from which best practices were identified. This paper introduces the concepts of Quality II and discusses the need to validate and operationalize its use more broadly within the practice of project procurement. The contributions of this paper are twofold: (1) a new way to contain (i.e., enhance the detection and minimize the adverse consequences) and reduce (i.e., limit the occurrence of) errors in construction is introduced—Quality II; and (2) it provides opportunities to initiate the cultural and organizational changes in practice needed to improve performance and productivity in construction.