Journal of Construction Engineering and Management

This study reviews the common practices and procedures conducted to identify the cost drivers that the past literature has classified into two main categories: qualitative and quantitative procedures. In addition, the study reviews different computational intelligence (CI) techniques and ensemble methods conducted to develop practical cost prediction models. This study discusses the hybridization of these modeling techniques and the future trends for cost model development, limitations, and recommendations. The study focuses on reviewing the most common artificial intelligence (AI) techniques for cost modeling such as fuzzy logic (FL) models, artificial neural networks (ANNs), regression models, case-based reasoning (CBR), hybrid models, diction tree (DT), random forest (RF), supportive vector machine (SVM), AdaBoost, scalable boosting trees (XGBoost), and evolutionary computing (EC) such as genetic algorithm (GA). Moreover, this paper provides the comprehensive knowledge needed to develop a reliable parametric cost model at the conceptual stage of the project. Additionally, field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error (MAPE) and adjusted $R^2$, respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty are discussed for the 20 developed AI models. In addition, this study presents a publicly open data set for FCIPs to be used for future model validation and analysis.

Projects delays are among the most pressing challenges faced by the construction sector attributed to the sector’s complexity and its inherent delay risk sources’ interdependence. Machine learning offers an ideal set of techniques capable of tackling such complex systems; however, adopting such techniques within the construction sector remains at an early stage. The goal of this study was to identify and develop machine learning models in order to facilitate accurate project delay risk analysis and prediction using objective data sources. As such, relevant delay risk sources and factors were first identified, and a multivariate data set of previous projects’ time performance and delay-inducing risk sources was then compiled. Subsequently, the complexity and interdependence of the system was uncovered through an exploratory data analysis. Accordingly, two suitable machine learning models, utilizing decision tree and naïve Bayesian classification algorithms, were identified and trained using the data set for predicting project delay extents. Finally, the predictive performances of both models were evaluated through cross validation tests, and the models were further compared using machine-learning-relevant performance indices. The evaluation results indicated that the naïve Bayesian model provides a better predictive performance for the data set examined. Ultimately, the work presented herein harnesses the power of machine learning to facilitate evidence-based decision making, while inherent risk factors are active, interdependent, and dynamic, thus empowering proactive project risk management strategies.

The adoption of relational contracting (RC) has been increasingly used by parties in various developed countries as one of the innovative tools in traditional contracting to achieve benefits such as cost savings, schedule reduction, reduced litigation, improved teamwork, increased trust, stronger relationships, and overall satisfaction. However, the adoption of RC in public-sector construction organizations has been observed to encounter difficulties when compared with implementation in private-sector organizations because of typical barriers, including a lack of understanding of partnering principles and personality indifference, in addition to a low offer mentality, ineffective top management, and a lack of positive working-level commitment. Studies in this area conducted with the objective of ensuring the effective implementation of RC in public-sector organizations are also limited. In view of this, this study aims at exploring the factors that will have an important impact on developing RC practices and the relationship between the contractor and the public firm. Previous studies focused on the identification of the success factors of RC, but those studies were limited to RC implementation from a top management perspective only. Thus, this study investigates the critical factors in RC that are necessary to produce successful relational partnerships in public organizations exclusively from the perspective of front-line employees. The front-line employees include junior engineers and assistant engineers, who are responsible for the day-to-day management of construction activities. An extensive literature review offered 9 important relational contract success factors that were identified and measured by 29 indicators. The data were collected from 202 operational-level employees at four public-sector construction organizations in India. The structural equation modeling technique was used for data analysis. The critical factors identified from the study are training, organizational change, commitment of top management, partner selection, and the assessment process. Organizational change for partnering emerged from this study as one of the new critical factors. The findings from this study could be used to improve the relational partnerships of public organizations.

Many scholars from multiple professional and academic disciplines have investigated the various links between the construction industry and economic output. Nevertheless, there remains a noticeable dearth of studies that address the potential impact of the players within the construction industry on various economic indicators. The goal of this research is to study how the economic performance of the US—measured in GDP—is impacted by the performance of the construction industry and its key players and how the performance of the construction industry could help in forecasting future US GDP. This goal is achieved by studying the relationship between GDP, total construction spending (TTLCONS), the Standard and Poor’s 500 (S&P500) index (GSPC), and the stocks of major publicly traded construction companies. The authors applied an interdependent research methodology that included (1) data collection, (2) statistical testing on the data using correlation analysis and Granger causality testing, and (3) vector autoregression (VAR) for both fitting and prediction purposes. A positive correlation was found between GDP, the S&P500, TTLCONS, and the stocks of major publicly traded construction-related companies. Also, the Granger causality test showed that some major construction company stocks are useful in forecasting GDP. The developed VAR model was used to forecast GDP for 2 years with acceptable accuracy. In this connection, the model was validated by successfully forecasting in a retrospective manner the effect of the 2008 financial crisis. This shows that the stock prices of select publicly traded construction and equipment companies can be used to predict GDP. In fact, a similar model could have been used to predict the 2008 economic collapse and develop ex ante mitigation strategies. The findings of this study could open opportunities for abandoning the notion of studying the construction industry solely using the health of residential construction. As such, this research should help in moving toward the development of a construction–economy nexus.

Federal and state laws require governments to ensure accessibility for people with disabilities on their pedestrian facilities. State and local governments are required to develop transition plans to upgrade inaccessible pedestrian facilities to fully comply with all accessibility requirements. This paper presents the development of a novel model that is capable of optimizing the scheduling of pedestrian facilities upgrade projects to maximize compliance with accessibility laws and regulations. The model provides original contributions to support decision makers in (1) quantifying the impact of upgrading noncompliant pedestrian facilities on people with disabilities; (2) identifying near-optimal trade-offs among the three optimization objectives of minimizing total number of interrupted pedestrian trips, minimizing total upgrade duration, and minimizing annual upgrade budget; (3) generating detailed schedules for upgrading noncompliant pedestrian facilities to satisfy ADA transition plan requirements; and (4) creating graphic maps of the generated near-optimal transition plans. A case study of a real-life city that includes 4,178 noncompliant pedestrian facilities was analyzed to evaluate the performance of the model and illustrate its novel and unique capabilities.

Although building information modeling (BIM) holds the promise of significantly advancing the architecture, engineering, and construction (AEC) market worldwide, its widespread acceptance and adoption is still an unsolved issue. From a technology diffusion perspective, this paper proposes a game theory–based model including two firms who both are potential BIM adopters under support from the government (i.e., subsidy). Two influential factors affecting AEC firms’ BIM adoption decisions are identified, including BIM adoption efficiency and adoption incentives. Through analyzing the model with a backward-induction method, AEC firms’ best responses of joining time and the government’s optimal subsidizing strategies are discussed. The findings show that the government subsidy is effective in promoting BIM adoption because the subsidy can both bring forward the joining time and enhance BIM adoption efficiency by offsetting firms’ setup costs. Under the government’s subsidy policies, AEC firms that are originally negative toward BIM adoption can become positive, which demonstrates the occurrence of BIM technology diffusion. Theoretical and practice implications are also discussed. This study contributes to a new perspective for understanding BIM adoption behaviors among AEC firms. In addition, the study for the first time introduces game theory into explaining BIM adoption and diffusion, which may benefit BIM implementation and enhance the effectiveness of policies aiming at promoting BIM application practices.

The relationship between the perceived fairness of administering projects’ claims and contractors’ reduced potential to disputes is probably missing in published literature in the context of engineering ethics. In the current research, the moderating effect of engineering ethics is explored in relationship between perceived fairness and contractors’ reduced potential to dispute. The data are gathered from 90 contract administrators in the construction industry of Pakistan. The results depict that decision outcome fairness, procedural fairness, quality of treatment experienced, and control indicate a significantly positive effect on the contractors’ reduced potential to disputes. Additionally, engineering ethics positively moderated the relationship between the quality of treatment experienced and contractors’ reduced potential to disputes. These results can guide project management teams to diminish the contractors’ perceptions concerning construction claims and disputes.

Although methods for assessing and simulating the influence of safety-related measures on safety performance have been proposed, practical applications remain limited. Data required by these methods are dispersed across departments, necessitating the development or redesign of data warehouses. This research proposes a simulation-based analytics approach to enhance safety management system (SMS) decision making using distributed simulation to overcome limitations associated with previous approaches. This distributed simulation approach is used to (1) integrate historical data without modifying data-warehouse structures (i.e., data fusion component), (2) link data to an artificial neural network–based analysis component for determining the influence of safety-related measures on incident levels, (3) connect data and analysis components to existing simulation components, and (4) combine the outputs, resulting in a comprehensive safety performance evaluation system to examine incident levels. Results demonstrate that this approach successfully fuses and integrates data from several sources with analysis and simulation components in a cost-, labor-, and time-efficient manner. A distributed simulation–based analytics approach represents a considerable opportunity for industrial construction companies to more effectively use historical data, analysis tools, and simulation models.

Safety climate and safety performance in construction projects vary at an organization level and group level. However, the differences between supervisors and construction workers are rarely examined. This study compared the safety climate, safety behavior (safety compliance and safety participation), and safety outcomes (injuries, unsafe events, and stress) between these two social groups. The relationships among these variables were also contrasted with each other using the structural equation modeling (SEM) technique. The data were collected from 119 supervisors and 536 site workers at 22 construction projects in China. The results show that four dimensions of safety climate were distinctly different between supervisors and workers, which is consistent with the social identity theory. Two dimensions of the supervisors’ safety behavior were significantly better than those of the workers’ behavior, while supervisors suffered higher stress. The relationships between safety climate and safety behavior were positively associated for both groups. However, the links from safety climate to safety outcomes and from safety behavior to safety outcomes significantly differed between supervisors and construction workers, which revealed a complementation phenomenon. This study sheds light on group-level safety performance research. It also suggests that safety professionals should take group-targeted safety intervention measures and pay more attention to supervisors’ psychological well-being.

The unique nature of construction projects can mean that construction activities often suffer from duration and cost variability. Because this variability is unplanned, it can present a problem when attempting to complete a project on time and on budget. Various factors causing this variability have been identified in the literature, but they predominantly refer to the nature and/or context of the whole project rather than specific activities. In this paper, the order of magnitude of and correlation between activity duration and cost variability is analyzed in 101 construction projects with over 5,000 activities. To do this, the first four moments (mean, standard deviation, skewness, and kurtosis) of actual versus planned duration and cost (log) ratios are analyzed by project, phase of execution, and activity type. Results suggest that, contrary to common wisdom, construction activities do not end late on average. Instead, the large variability in the activity duration is the major factor causing significant project delays and cost overruns. The values of average activity duration and cost variability gathered in this study will also serve as a reference for construction managers to improve future construction planning and project simulation studies with more realistic data.

Issues regarding relationships between supervisor safety behavior (SSB) and worker safety behavior (WSB) are well recognized in construction management research. Nevertheless, how safety plans are articulated through the actions of supervisors to the workforce remains unclear. By conceptualizing safety from the window of the “psychological contract” (PC), this research proposes that if the “psychological contract of safety” (PCS)—if reciprocal commitments between a supervisor and a worker are fulfilled—then WSB will be positively influenced. Using a PCS scale based on employer obligations, multilevel surveys were performed at five construction sites in Sydney, Australia, and data from 352 workers associated with a mega-construction project were collected. A hypothetical model proposing the PCS as an intervention is presented. The model is validated using structural equation modeling (SEM), the data reliability and validity of the survey findings are ensured, and the goodness of fit of the proposed model was examined. The results indicate that PCS successfully mediates the relationship between two important safety agents—the supervisor and the worker. When addressing the research gap, this article explains how the concept of PCS can be further utilized to investigate safety issues that are regularly raised. In addition, new avenues for researchers and practitioners are suggested to consider the social exchange relationships at construction sites. Using the validated PCS model and understanding the level of fulfillment, construction supervisors may influence workers’ behavior with greater confidence. Furthermore, when finding a low level of fulfillment of PCS, senior managers can deliver training to supervisors to improve their safety behavior to the benefit of WSB.

For project managers, accurate, timely, and intuitive information is the key to successful decision-making during progress control at a construction site. This paper presents a method that combines computer vision with building information modeling (BIM) for automated progress monitoring of tiles. This method can automatically and accurately measure the in-built progress information of a construction site and transmit real-time progress information to the cloud in a visualized form. With the in-built and real-time progress information, project managers can know the progress of construction site in time and make decisions easily. The proposed method includes several modules. First, an image database is built with thousands of tile images, and a variety of local binary patterns (LBPs) feature extraction methods, and support vector machines (SVMs) are used to train a tile classifier with satisfactory performance, with an accuracy of 91.17%. The purpose of the first module is to construct a mathematical feature of an image and to train a classification algorithm according to this feature, so that tiles in images can be identified. The improved edge detection algorithm detects the boundaries of completed tiles in given images. Afterward, the boundary line coordinates are converted from image pixel coordinates to a real-world coordinate system through camera calibration. Next, using the information from the camera location and room profile information extracted from the BIM model, the actual tile area can be calculated automatically. Finally, the in-built progress is highlighted in the room floor plan, and the result is delivered to the BIM cloud simultaneously. The proposed method was tested at a real indoor construction site. The experimental results indicate that the method can effectively carry out real-time automatic quantity calculations.

Procurement practices are often characterized by competitive tendering. The overarching purpose of this is to ingrain transparency, probity, and value for money into the processes of acquiring goods and services. When tenderers collude and clients are unable to detect them, bids will become uncompetitive. Yet, there have been a limited number of effective practical tools and methods developed that can be used by procurement authorities, controllers, and public officials to detect collusive tendering. Using data obtained from the Brazilian Federal Police and their ongoing criminal investigation titled Operation Car Wash, a robust and practical probabilistic method is developed. The main findings were that the method was able to accurately identify (81%–96%) the occurrence of collusion during a sealed tendering process. Conclusions are drawn from the lessons learned from the forensic investigations, indicating that the approach presented for detecting collusive behavior during tendering is grounded in reality. This paper presents a new way to utilize statistics and probability to identify the presence of and control collusion in public- and private-sector tendering.

Healthcare facilities management (HFM) plays an important role in delivering healthcare services. However, compared with core activities in hospitals (i.e., quality and safety of medical treatment and clinical care), the role of HFM has received much less attention. To this end, a comprehensive conceptual benchmarking framework considering five dimensions—cost-effectiveness, customer satisfaction, energy and resource efficiency, management efficacy, and operation and maintenance efficiency—is proposed to facilitate the benchmarking of performance measurement in hospitals. A total of 23 Shanghai municipal hospitals, governed by the Shanghai Hospital Development Center, were used as a case study to demonstrate how the conceptual model can be implemented. The Delphi method and analytic hierarchy process were employed to derive the prioritized performance indicators of HFM. The findings of the pilot study showed that cost-effectiveness and customer satisfaction are the top two factors that drive HFM in Shanghai, followed by operation and maintenance efficiency, energy and resource efficiency, and management efficacy. The implementation of this proposed framework will enable academics and practitioners to conduct dynamic and data-driven benchmarking for healthcare facilities and, as a result, find solutions for continuous improvement during operation phases.

Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a big data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a predictive analytics and modeling system (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75-million-point dataset of power transmission projects has been used as a case study. The proposed big data architecture fits this purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the body of knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a big data architecture that can manage and process large varied datasets and seamless scalability.