Journal of Construction Engineering and Management

Fuzzy set theory (FST), especially its concepts of linguistic variables and fuzzy membership functions (FMFs), has been frequently used in construction risk assessment due to a lack of historical data and reliance on expert opinions. However, FMFs have to be properly defined to leverage the ability of FST in dealing with vagueness, imprecision, and uncertainty in expert opinions. This study aims to identify risk variables and their associated FMFs used in relevant literature and develop a process of measuring FMFs. The research methodology involves conducting a systematic review, developing a process for defining FMFs, and validating the proposed process through an illustrative case study with hurricane storms as a risk event. Results showed that only 11 risk factors represented 77% variables used in 131 articles that were found relevant for further analysis. Most studies (88%) arbitrarily assigned FMFs to their risk variables despite the fact that defining FMFs is one of the critical steps for the successful use of FST-based methods. Membership functions have to be measured with relevant data, including those provided by experts. To fill this knowledge gap, a step-by-step process is developed based on the modified horizontal approach and applied in the case study to appropriately determine the FMFs of the risk variables. Separate empirical hurricane-related public data were used to verify the determined FMFs. This study contributes to the body of knowledge by providing standardizing FST-based methods for risk assessment and measuring the FMFs of risk variables. The study also facilitates the application of FST-based methods in actual construction projects and operations.

The short-line match method (SLMM) is an effective and popular technology for accelerated bridge construction because it is characterized by a small construction area, minimal labor, and fast construction speed. However, a millimeter-sized error in the precast phase may be amplified to a meter-sized error at the end; therefore, SLMM has an extremely high requirement for geometric control. Construction project abandonment and rework accidents caused by geometric control failure frequently occur. Existing geometric control methods should be improved due to their incomplete consideration of geometric errors. This study presents a novel high-precision geometric control method (HPGCM) for SLMM. The proposed method includes the decoupling of deviation angles consisting of coupled components from different planes and the identification of deviation angles with different spatial positions. The effectiveness of the proposed method is verified through simulated and practical examples. The developed method exhibits high precision and reliable error-correcting ability. It can provide a reference for the construction of SLMM-based bridges in the future.

Industrial construction employs various trades in large-scale prefabrication operations to produce modules and structural components at an offsite facility that will be shipped to the field for rapid installation. Developing an analytical methodology for characterizing the effect of variability in productivity on labor cost budgeting is vital to this particular construction type. Integrating current practices of estimating, scheduling, and budgeting in industrial construction, this paper describes an error propagation model for calculating the standard deviation of the cumulative labor hours at particular time points of the project duration and establishing a confidence interval around the average value. Analogous to plotting an S-curve, the lower bound and upper bound of the interval for cumulative labor hours budgeted at control points along the project duration can be articulated to form the S-stripe, which visually portrays the risk of labor cost budget due to risks inherent in labor productivity. The application and verification of the proposed analytical methodology are illustrated with a steel fabrication project case. Monte Carlo simulation is applied to the same project data in the case study, resulting in a near-perfect correlation between the two sets of results. In the simulation experiment design, determining the minimum number of simulation runs that are deemed sufficient to obtain reliable sampling results entails trial and error, and the obtained result is case-specific. In contrast, the proposed analytical method circumvents this barrier by analytically deriving the project labor cost budget in the form of an S-stripe.

Voluntary turnover of key employees with scarce skills and tacit knowledge has a potential influence on the competitive advantage of construction organizations. Although much research has been conducted to find out causes of turnover intention, there is limited research in the construction context, particularly considering the influence of the psychological perspective. In this study, using data from 351 construction professionals in the Turkish construction industry and utilizing a three-way moderated moderation model, we examined the interaction effects of psychological factors, operationalized in terms of organizational identification and perceived external prestige, on turnover intention within the boundary of gender differences. Analysis of the responses demonstrates that considering organizational identification, perceived organizational prestige, and gender independently may lead to the underprediction of the turnover intention of employees in construction organizations. The results indicate that organizational identification and perceived organizational prestige have an impact on construction professionals’ turnover intention in a condition boundary for gender differences. On this basis, this study may provide important insights for human resource practitioners in identifying potential employees that are likely to have lower turnover intention. It is also of relevance to managers in retaining valued professionals in construction firms.

A key underlying challenge in today’s model-driven approach to engineering is how progress and level of effort are being measured and reported. Recent definitions such as level of development enable project teams to track completeness of information in building information modeling (BIM)-driven processes; yet, there are no established processes or metrics to reliably measure progress or track productivity of a model-driven engineering process as a function of the maturity of the model and/or the data that supports the engineering process. To address this gap in knowledge, this paper presents a discrete set of model maturity index (MMI) definitions to measure progress in engineering work, per modeling discipline. Using these MMI definitions, a model maturity risk index (Model MRI) toolkit was created to track maturity of an engineering process at the granularity of work breakdown structure locations. The MMI toolkit also reports risk in achieving certain MMI levels per modeling discipline. To benchmark maturity of the modeling work per project milestone or model review session and to establish workflows for tracking progress, an addendum to the modeling execution plan (ModelXP) is presented. New productivity and project controls metrics for tracking modeling work are also introduced. The developed definitions, toolkit, and addendum to ModelXP were validated through exhaustive surveys and charrette tests with project stakeholders involved in capital projects. The role of the MMI definitions and Model MRI toolkit in providing project controls insight and proactive management opportunities in real-world projects is discussed in detail.

There is a beneficial effect when integrating Building Information Modeling (BIM) with lean practices to identify and reduce waste in the construction industry. According to experts, it is possible to improve the design process through waste reduction by implementing lean practices and BIM. An unexplored perspective on these synergies concerns the relationship between the specific uses of BIM and lean practices. Therefore, this study analyzed the relationships between Lean Design Management (LDM) practices and BIM uses in the planning and design phases of the infrastructure lifecycle. To achieve this objective, the research was organized into three stages: (1) the explanation of LDM practices and BIM uses; (2) the characterization of sample projects and data collection strategies; and (3) data exploration, including reliability analysis, descriptive statistics, association analysis, and a causal analysis of LDM practices and BIM uses. The analysis of the relationship between LDM practices and BIM uses generated empirical evidence of the implementation of BIM uses and lean management practices at the design phase. LDM practices from the categories planning and control and problem-solving and decision-making were more related to BIM uses than LDM practices from the category stakeholder management. Additionally, it was concluded that if a project applies a higher proportion of BIM uses, it will tend to apply a higher proportion of LDM practices; however, this relationship is not as clear in the other way around.

In the last two decades, value management (VM) has become an established construction practice based on fundamental approaches. Implementation of VM activities can contribute to enhancement of the initial objectives in terms of cost, quality, and time. However, the VM activities adopt informal approaches in the Egyptian construction industry. This study aims to develop a VM implementation model by investigating the relationship between VM implementation and the overall project success (OPS) in Egyptian building projects. Fifteen experts were interviewed to assess the VM activities obtained from previous studies. Exploratory factor analysis (EFA) was used to validate the results from the interviews. Structured questionnaires completed by building professionals were further employed to investigate the importance of these VM activities. Partial least squared structural equation modeling (PLS-SEM) was used to determine and confirm the factors structure and examine the relationships between the VM implementation and OPS. The results showed a moderate association, with the implementation of the VM contributing 55.5% to the success of the project. The findings can be used as a benchmark for decision-makers to minimize costs and boost efficiency by implementing VM in the Egyptian construction industry. This study advances the current construction engineering management by improving the understanding of VM methods and activities.

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction.

An effective green bidding mechanism is pressingly needed to drive sustainability awareness in construction projects. Nevertheless, several challenges have prevented the success of green bidding implementation in current practice, including difficulty in establishing a powerful bidding preference that has the capability to effectively encourage every construction-oriented party toward sustainability. This paper developed a novel, sustainable bidding model to identify a potential environmental weight for evaluating the environmental impacts using the amount of ${\mathrm{CO}}_2$ emission generated from construction equipment fleet operations. A highway construction project case study in Thailand was used to evaluate the performance and capabilities of the developed model in specifying possible environmental weights toward sustainable bidding. The model facilitated the quantification of different construction equipment mixes and their impact on the environment, based on the cost per kilogram of ${\mathrm{CO}}_2$ equivalent for inclusion in the bidding award preference. The model was validated to investigate the green bidding preference awarded to a contractor. The validation results confirmed the benefit of including the environmental weight in the bidder evaluation to provide better opportunities for contractors pursuing sustainability in bid winning under an impartial and more practicable environment. The developed model contributes to (1) promoting greater practicality in the existing confined green bidding mechanism by incorporating an effective sustainability weight for green performance evaluation of construction bidders and (2) reducing the substantial environmental impacts of construction equipment by offering incentives in the primitive bidding approach to create sustainable initiatives for public works contractors.

Systemic innovations require multiple interdependent actors to change their practices simultaneously in order to realize the benefits of the innovation. Building information modeling (BIM) has been classified as a systemic innovation that is adopted by building projects, firms, and users. However, the slow diffusion of BIM in the architecture, engineering, and construction (AEC) sector has maintained the gap between BIM visions and actual BIM practice. Some governments are planning to enact policies to promote BIM adoption in construction. However, in some countries, BIM adoption has already started with large construction organizations (i.e., the middle-out diffusion approach). To learn from previous experience and before enacting top-down interventions, policymakers require a model to stimulate systemic BIM adoption for entire supply chains with fragmented project and organizational structures. The current paper investigates the systemic adoption of digital innovations in construction and is aimed at formulating a model of systemic BIM adoption (MSBA). Three primary datasets consisting of 133 BIM users, 30 chief executive officers, and 20 project managers were collected in Peru and collectively analyzed using cross-classified multilevel modeling (CCMM). It was found that MSBA has five user-, three firm-, and two project-level factors, explaining 28%, 75%, and 50% of the variance in users’ BIM adoption, respectively. The proposed model would provide useful guidance for corporate decision makers and government policymakers to develop BIM-diffusion policies to accelerate adoption. It would also provide a useful practical implementation framework as the industry progresses toward a digital mode of working and could underpin further digitalization of the sector worldwide.

Public-private partnerships (PPPs) have seen continuous growth in water infrastructure and services over the last few decades due to budgetary and technological constraints of the public sector and the requirements for efficiency improvements. However, many water PPPs have failed in both developed and developing countries. In some cases, the government has to pay huge compensations for contract terminations, and in others, the private party withdrew or even became bankrupt because of extreme financial loss. Despite the disastrous consequences, research on water PPP failures is scarce. This paper attempts to unveil the factors associated with the private sector that have led to the failure of water PPPs and their causal relationships. Through an exploratory sequential mixed-method research approach that integrates literature review, multiple case studies, questionnaire survey, and statistical analysis, this paper has identified the common failure drivers initiated by the private sector, evaluated the relative severity of the failure drivers, and established the failure mechanism model for water PPPs in general. Outputs of this study will facilitate public and private sectors in better planning and managing water PPP projects and developing more responsive strategies to address the failure drivers that can occur in the different stages of the PPP life cycle toward successful outcomes.

Despite the development and implementation of intensive health and safety training programs, the construction industry still remains one of the most dangerous industries. To remain safe, workers must be able to effectively identify, value, and respond to dangers in their work environment. The sense of comfort or discomfort associated with a hazard (i.e., risk tolerance) can play a significant role in the propensity to engage in an unsafe behavior. However, it remains unclear if, and to what extent, risk tolerance in personal life is associated with risk tolerance at work or with the propensity to break safety rules at work. To address this knowledge gap, this research tested the hypotheses that personal risk tolerance influences both work-related risk tolerance and the propensity to break safety rules. These hypotheses were tested using linear mixed-effects models with data from a survey administered to 11,811 workers from 19 countries. Results indicated that personal risk-tolerance is positively associated with both work-related risk tolerance and risk-taking behavior. However, when controlling for demographic dimensions, the relationship between personal risk tolerance and risk-taking behavior was no longer significant. Practically, these results suggest that holistic training programs for reducing or calibrating safety risk tolerance should focus on personal life in addition to work, and the demographics and social interactions of the workers also should be considered when studying safety behavior.

Procuring the correct materials at the right time and for the lowest cost is essential in construction. This fact underlines the importance of an optimized material procurement schedule integrated with the construction schedule/program. However, few studies examine the development of a material procurement schedule by integrating the construction schedule and optimizing material costs as well as any material shortage impact. In addition, budget constraints and maximum storage capacity are rarely captured in existing models for material procurement optimization. To address these shortcomings, an optimization model is developed using the nondominated sorting genetic algorithm II (NSGA-II) and executed in MATLAB R2017a. This model was implemented in a construction project that developed a material delivery schedule based on the budget constraints and maximum storage capacity for five majorly used building materials, resulting in a costs saving of 31.17% and a reduction in shortage impact of 83.47% compared with the actual delivery schedule. The developed model also incorporates the minimum order quantity and standard shipping size as well as avoiding surplus materials, leading to sustainable procurement of materials. Construction practitioners can use this model for procuring materials when facing budget constraints in a construction project with the lowest cost and least shortage and without excess or insufficient purchasing. The model will aid in completing the project within the stipulated time and budgeted cost.

This study is based on the conservation of resources (COR) theory and the job demands-resources (JD-R) model and aimed to investigate the potential within-person relationships between self-efficacy, work engagement, and workplace deviance behavior on a daily level and the potential cross-level effect of job control among construction workers. Data were collected from 128 construction professionals and workers over five consecutive working days using the experience-sampling method. The hierarchical linear modeling results indicated that in construction professionals and workers, the daily experience of self-efficacy was positively associated with daily work engagement, which in turn negatively influenced daily deviance behavior at work. The results also revealed the cross-level moderating effect; job control strengthened the within-person suppressing impact of work engagement on deviance behavior. This study provides episodic evidence for the impact of self-efficacy on work engagement and deviance behavior in the workplace. These findings contribute to the positive psychology knowledge in construction projects by unveiling the psychosocial mechanism of construction professionals and workers through the daily diary survey method to capture the daily fluctuation of beliefs, engagement, and deviance behavior and by revealing the resource accumulation corollary of the COR theory and the motivation process of the JD-R model to highlight the facilitating function of personal and job resources.

This study investigated whether the relationships of personality with safety compliance and safety participation remains significant when safety-specific transformational leadership is supplied by supervisors. It also tested the moderating role of this leadership in the personality–safety behavior relationship. One hundred twenty workers completed the Big Five personality test and graded their supervisors’ leadership, and the workers’ safety behavior was assessed by the supervisors. The results showed that conscientiousness was related positively with safety compliance. Contrary to our theorizing, agreeableness was related negatively with safety compliance. Leadership always positively related to safety compliance and participation, and it also attenuated the relationships of conscientiousness and agreeableness with safety compliance. This enhances the importance of considering safety leadership as an antecedent and intervention of employee safety behavior. However, conscientiousness and agreeableness remained related with safety compliance when safety leadership was considered simultaneously. These findings expand the limited research on the personality–safety behavior relationship in the construction field. Evaluating conscientiousness and agreeableness prior to recruitment can enable employers proactively to curtail unacceptable safety transgressions. Enhancing supervisors’ safety leadership helps to prevent employees from performing safety-related actions that are subject to individual dispositions.

For transparency and accountability, public sector clients typically select a contractor using lowest-price open tendering. This paper focuses on improving the selection of a design-build contractor by modeling the inherent uncertainties associated with open tendering. Although there are numerous methods for selecting a contractor, few consider, and even fewer have accounted for, random uncertainties associated with open tendering. In the absence of a structured approach that includes cognitive uncertainty modeling, the risk of choosing a subpar contractor and consequent project failure increases. Cloud theory uses a normal distribution membership function to infuse fuzzy set theory with probability theory. The application of cloud theory in a case study to order the preferred contractors improves decision-making quality and, consequently, confidence in the derived outcome where uncertainty exists. By accounting for both fuzzy and random uncertainties expressed by decision makers, this formulation for contractor selection increases the potential to achieve client performance goals. The proposed new approach reduces the risk of project failure by offering a unique understanding of how to more efficiently choose a design-build contractor. It narrows the model prediction and practice gap by providing an alternative to selecting the lowest bidder.

Recycled concrete aggregate (RCA) is sustainable, recycled concrete waste that is used as a component of green concretes and that, in the long term, can be a substitute for natural aggregates, which, in turn, can result in the preservation of natural aggregates. The objective of this paper is to modify the cost-benefit analysis generation model of ready-mixed self-consolidating concretes made with RCAs. The model focuses on analyzing the financial effects that favor the production and delivery operations of RCAs rather than those of natural concretes. An inductive qualitative approach was adopted to conduct an inquiry into the cost-benefit assessment of ready-mixed self-consolidating concrete production prepared with recycled concrete aggregate. The study results indicate that the RCA price cannot fall below that of the natural concrete aggregate (NCA) regardless of the production plants used. The major finding of this paper is that production establishments of RCAs can be used for the large-scale production of recycled aggregate concretes at fair prices. Front-end loader and overhead bin plants for ready-mixed concrete may be used to reduce the increasing costs of RCA production. Nevertheless, recycled concrete pricing effects and demand and supply factors pose challenges that are barely accounted for.

The construction industry is overwhelmed by an increasing number and severity of disputes. The primary objective of this research is to predict the occurrence of disputes by utilizing machine learning (ML) techniques on empirical data. For this reason, variables affecting dispute occurrence were identified from the literature, and a conceptual model was developed to depict the common factors. Based on the conceptual model, a questionnaire was designed to collect empirical data from experts. Chi-square tests were conducted to reveal the associations between input variables and dispute occurrence. Alternative classification techniques were tested, and support vector machine (SVM) classifiers achieved the best average accuracy (90.46%). Ensemble classifiers combining the tested classification techniques were developed for enhanced prediction performance. Experimental results showed that the best ensemble classifier, obtained from the majority voting technique, can achieve 91.11% average accuracy. Based on Chi-square tests, the most influential factors on dispute occurrence were found as variations and unexpected events in projects. Other important predictors were all related to the skills of the parties involved. This study contributes to the construction dispute domain in three ways: (1) by proposing a conceptual model that combined the diverse efforts in the literature for identifying variables affecting dispute occurrence; (2) by highlighting the influential factors, such as response rate and communication skills, as indicators for potential disputes; and (3) by providing an empirical ML-based model with enhanced prediction capabilities that can function as an early-warning mechanism for decision-makers.

The fragmentation of the construction industry led to the development of innovative approaches including collaborative planning, relational contracting, integrated project delivery, advancements in technology, and lean construction. Existing research focused on certain aspects of collaborative planning without providing a comprehensive analysis of the factors affecting collaboration. To this end, the goal of this paper is to provide a holistic list of factors affecting collaborative planning in the construction industry and guiding future research needs. This study followed an interrelated research methodology. As such, the authors (1) conducted an extensive review and analysis of the literature on collaboration and collaborative planning in the construction industry within the last 30 years, (2) identified and defined the factors impacting the effectiveness of construction collaborative planning, (3) performed social network analysis (SNA) to quantify the importance and impact of each of the identified factors and verified the output using a simple analytic method, and (4) highlighted the gaps in the literature and created a roadmap for the factors that need further investigation. Consequently, this study provides substantial addition to the body of knowledge at multifacets. To this effect, the results include (1) presenting 50 factors that could collectively affect collaborative planning in construction projects; (2) indicating that future research endeavors need to investigate the understudied key factors for collaborative planning comprising vision, goal alignment and integration, early involvement of key project stakeholders, project parties relationship, and willingness for collaboration and cooperation; and (3) substantiating the need to develop models that simultaneously incorporate all 50 factors to have simulation environment that is as close as possible to reality. Ultimately, this research provides a foundation for a broader and more holistic framework to further advance the utilization of collaborative planning in the construction industry.

In the US, nuclear power plants offer a means of generating power with less carbon emissions and higher efficiency as compared to traditional fossil plants. Because of this, they offer a solution that meets the requirements of the current regulatory environment. While both hydroelectric and nuclear power are attractive from an emissions standpoint, these technologies also include greater risk of cost overruns than other types of power plants, e.g., coal fired, combined cycle, or solar. This paper leverages a robust literature search to collect data about nuclear power plant construction projects in the US, and based on a peer-reviewed risk register, highlights the trends in risk prevalence based on nuclear reactor type, year of construction, and Nuclear Regulatory Commission (NRC) region. This analysis revealed that there was no correlation to NRC region versus risk. Risks are, for the most part, present in all regions without an obvious correlation between region and risk presence or prevalence. The analysis also demonstrates that among the four reactor manufacturers present in the dataset, design risk was the most prevalent risk, and that Westinghouse reactors had the greatest occurrence of risk all together. This paper contributes to the power plant construction body of knowledge by (1) categorizing risks; cited in literature from 50 US nuclear power plant construction projects using an existing peer-reviewed risk register and (2) identifying trends in risks that lead to significant cost overruns over time.

In competitive bidding, the success and/or failure of the contractors strongly depends on their submitted bid price. Hence, the bidding price decision is a strategic subject for contractors of construction projects. This paper develops a stochastic programming model for determining the optimum bidding price in construction projects. Some model parameters, such as the number of competitors and the project’s cost, are estimated by analyzing historical data. Then, a mathematical model for the bidding price decision that maximizes the expected profit is proposed. To reduce the risk of suffering from a large loss, a maximum acceptable risk constraint is employed. To evaluate the model’s performance, some numerical problems are examined. Moreover, sensitivity analysis of the key parameters and a robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model’s effectiveness in real-world situations, a case study is analyzed using the proposed approach. The numerical results indicate that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model. This research contributes to the community of contractors of construction projects by providing a new approach for determining the optimum bidding price that is in greater accordance with real-world constraints.

Despite that the need to develop an integrated building information modeling–facility management (BIM-FM) system is often emphasized in the literature, real-life implementation case studies focusing on large-scale facilities are scarce. The goal of this paper is to describe the capacities of building information modeling (BIM) in the facility management (FM) phase by presenting the experience gained during the development and utilization of BIM-FM platform at a large-scale facility and to illustrate technical challenges and lessons learned from the perspective of practitioners. The selected case is an international airport facility, in which a BIM-FM platform was implemented and used in the FM phase for $4\mathrm{years}$. Two data collection methods were used in the case study: (1) interviews with the project team; and (2) investigation of documents, models, and BIM-FM platform. The main contribution of this paper is that it provides qualitative insights on how BIM-FM integration was performed in a large-scale project, including BIM-FM platform selection and development processes, and identifying the technical challenges and lessons learned that were observed through the utilization and maintenance of a BIM-FM platform. The findings of the case study can be used by the practitioners to effectively adopt BIM in the FM phase, and by academics to understand the challenges and to develop related approaches.

Rework can be a problem in construction projects. While research has contributed to the accumulation of knowledge about rework, there remains limited understanding about its causal mechanisms. The result is an inability to manage and control the risks and uncertainties that surround rework causation in construction projects. This paper suggests that possessing knowledge and having an understanding of rework causation and its context is pivotal for anticipating its likely occurrence and putting in place risk mitigation strategies. The paper concludes by suggesting there is a need to develop a knowledge engineering system to capture rework in projects so that artificial intelligence can be used to support uncertainty management.