Journal of Construction Engineering and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Regardless of the different project characteristics in the construction industry, cost and schedule overruns are always regarded as being of paramount importance in the project controls area. Numerous research efforts have been directed to forecast the aforementioned two important project variables using different modeling techniques. However, no prior work is believed to have offered an integrated approach to estimating the performance of construction projects. This critical knowledge gap is compounded even more by the evolving complexities and uncertainties in today’s construction industry. This paper creates a holistic framework to evaluate project progress and to predict its performance by incorporating a broad spectrum of inputs. The objectives are to (1) quantify the impacts of the risks related to the performance of projects in terms of cost and schedule; (2) formulate a holistic assessment model; and (3) correlate the developed system to predict cost and time at project completion. To this end, a multistep research methodology was utilized. First, for data collection, a survey was distributed and filled by 63 construction experts to study the effects of 25 performance risks that have shown to be the most important based on a meta-analysis of the literature in a previous study. Second, mathematical and statistical analysis techniques were used to develop a model that maps the investigated project risks to both cost and schedule performance. Steps included fitting parametric and nonparametric distributions, calculating cost overruns, verifying the model, and providing guidelines for using the proposed model. Third, for application, a hypothetical dataset was used to demonstrate the use of the model, its ability to deduce real-world behavior patterns, and associated limitations. The developed framework contributes to the body of knowledge by providing a novel model that improves project performance in terms of prediction, control, management, analysis, and decision making based on an individualized assessment of different risk indicators. This study is valuable for the construction industry because it allows all stakeholders to evaluate the performance of construction projects based on a list of variables, ultimately ensuring more effective and efficient delivery and execution of projects.

The construction industry is facing an acute labor shortage around the globe, which has caused an escalation in construction costs and project delays. Work–life imbalance is identified as a major detrimental factor in attracting young people to join the industry. Previous research on work–life balance (WLB) has focused on shift workers, females, and managerial and professional individuals. There is still a paucity of literature on WLB for manual workers, in particular, manual workers in the construction industry. This paper aims to investigate how workers perceive their WLB and identify the factors that affect WLB in the construction industry. Based on interviews with the chief executives and senior officers from key trade unions, a questionnaire survey was conducted covering all types of construction manual workers including building, civil engineering, and electrical and mechanical (E&M) workers. A total of 970 valid answers were analyzed through structural equation modeling (SEM). The findings identified workplace support as the most critical area of concern. Additionally, work–life balance was found to have significant effects on the perceived health and safety of workers. The sample size of this study is so far the largest of its kind. This study helps us make informed suggestions for improving work–life balance in the construction industry, and hence its attractiveness to the younger generation to solve the labor aging and shortage problems.

With the rapid development of information and communication technologies, big data is expected to enable the creation of new paradigms for construction project management and improve the efficiency of design and construction activities. In practice, a few factors with complex impacts on each other can significantly affect the utilization of big data in construction projects. Practitioners should comprehensively examine these factors when shaping strategies for promoting the use of big data in their projects. This study aimed to identify factors that significantly impact the utilization of big data and investigate how these factors influence each other. First, a factor list was compiled based on a literature analysis and semistructured interviews with experts. Then, the nominal group technique was used to map interactions among the identified factors in an adjacency matrix, and interpretive structure modeling was used for further analysis. Finally, these factors were grouped into four categories with respect to their driving and dependence powers. Suggestions were made for promoting the utilization of big data in construction projects. The results indicate that incentive policies and the ethics and legal mechanisms of copyright, privacy, and data security are the most important factors that must be carefully considered by project managers and engineers when formulating strategies for utilizing big data in their projects.

While researchers have dispensed considerable effort in the past decades to reduce the risk of occupational injuries in the construction industry, the large amount of safety incidents occurring each year indicate that many of the safety interventions and technological advances have not fully achieved their safety goals. This fact suggests the possibility of a latent side effect of safety interventions, known as risk compensation. Since no study has empirically examined the risk-taking behaviors of workers as a function of the number and type of safety interventions in place for their protection, this research examined whether the concept of risk compensation could offset some safety benefits of protection equipment. An immersive mixed-reality environment (i.e., virtual reality and passive haptics) was developed to simulate a roofing activity. Then, combining real-time head- and ankle-tracking sensors with qualitative sources of data, the authors monitored the reactionary behavioral responses of participants while they completed roofing tasks under three, randomly ordered levels of safety protection in the mixed-reality roofing simulation. The results indicated that providing more safety interventions (i.e., higher levels of fall protection) produced a sense of invulnerability among participants. This false sense of security ultimately increased their risk-taking behavior by up to 55%: participants stepped closer to the roof edge, leaned over the edge, and spent more time exposing themselves to fall risk. Although this study used students as unskilled roofing workers, it provides an initial empirical understanding of how more safety protections might implicitly signal workers to take additional risks—an effect of risk compensation. These findings could significantly influence how the construction industry approaches the development and implementation of safety interventions to offset the influence of risk compensation.

Nowadays, horizontal directional drilling (HDD) technology is one of the most popular trenchless construction methods for installing pipelines under obstacles. Both risk analysis and risk management are key tools helping to meet today’s challenging trenchless construction project demands connected with quality, deadlines, costs, and legal issues. In this paper, a new model based on proactive and holistic approaches is presented, which is an extension from traditional risk analysis toward complex risk management in HDD projects. Fuzzy fault tree analysis, fuzzy weighted index, and a risk response matrix supported the risk management process. Overall, 147 risk remediation strategies for all the identified HDD risks were proposed. The following types of risk response strategies were applied: passive and active risk retention, risk transfer, risk elimination, and risk cause or effect reduction. The practical application of the proposed risk management model for HDD projects was shown in an example. The application of the proposed risk management model allows one to effectively reduce the risk of basic events occurrence and provides a remarkable reduction of the risk of the top event’s occurrence.

Observations and sample tests at five construction sites showed that the time required for generating a daily bill of materials (BOM) is within the range of 12–68 min. However, 20 foremen interviewed at the sites stated that, in reality, they cannot spend more than 2–3 min per day generating a daily BOM, although this information would be helpful for field control. To meet this time requirement, this study uses a building information model at level of development (LOD) 400, i.e., the fabrication level, and a concept referred to as the smallest workface boundary (SWFB), which is the boundary that encapsulates a set of LOD400 objects in the smallest workable unit to overcome their high granularity. This research also presents four types of rules to automate the generation of SWFBs to the extent possible from LOD400 objects. The SWFB concept and rules are incorporated into a protype to aid a foreman in quickly generating a daily BOM with LOD400 objects. A methodology that a foreman can use with the prototype is also provided. For validation, field experiments were conducted at sites in Abu Dhabi, Stockholm, and Lima. With the latest version of the prototype, the time requirement (i.e., not requiring more than 2–3 min per day) was met with an average of 59 s.

In almost all modern cities, various utility pipelines and cables often form a maze of underground utility network. Mismanagement of underground utility incidents could cause serious consequences including unavailability of services, property damages, and personnel injuries and fatalities. Since one major reason for mismanagement is unsuitable urban utilities planning, it is a challenging task to plan urban utilities in an efficient way. This paper proposes an underground utility occupation index (UUOI), which serves as an indicator of urban utilities development. With the statistical information of existing utilities in the predefined polygons, UUOI provides planning information, including underground space already occupied, and space available for future utilization by analyzing from multiple perspectives: by utility, by zone, by road category (footway and carriageway), and by city district. UUOI supports utility companies and governmental authorities in their decision-making at both the microlevel and macrolevel. At the microlevel, UUOI provides the necessary utility and space information for utility companies to share information in making long-term and short-term development plans to partially avoid conflicts between utilities belonging to different companies and adopt suitable construction methods to minimize damages to existing utilities. At the macrolevel, the UUOI can be used to extrapolate from a small scale to a large scale to facilitate government authorities in urban planning and development. The consolidated UUOI enables the differentiation and prioritization of areas of different levels of utility density and facilitates government authorities in formulating guidelines for future urban development, establishing strategic development areas and issuing permits for underground works. The improved practices of both government authorities and utility companies will also reduce disturbance to the general public. An illustrative example has demonstrated the applicability and usefulness of UUOI.

Building information modeling (BIM), as a technological artifact, has been acclaimed to have significant effects on construction projects by overcoming inherent problems such as poor communications. However, little has been empirically verified. This paper investigates the effects of BIM on construction project organizations using social network analysis (SNA) with empirical data from two comparable projects, one using BIM and the other without. Longitudinal SNAs of both projects showed that BIM is closely related to (1) changing the organizational structure by making it looser and flatter and involving more roles of building service and fewer design-related professionals; (2) showing the potential of increasing communication efficiency; and (3) reorganizing the collaborative processes by shifting most communication among different professions to earlier stages and engaging more roles in early-stage communication. This research not only validates the effects of BIM on projects from an organizational standpoint with SNA method, but also articulates the pathway through which such effects are made.

Heavy civil and mining construction industries rely greatly on the usage of heavy equipment. Managing a heavy-equipment fleet in a cost-efficient manner is key for long-term profitability. To ensure the cost-efficiency of equipment management, practitioners are required to accurately quantify the equipment life-cycle cost, instead of merely depending on the empirical method. This study proposes a data-driven, simulation-based analytics to quantify the life-cycle cost of heavy equipment, incorporating both maintenance and ownership costs. In the proposed methodology, the $K$-means clustering and expectation-maximization (EM) algorithms are applied for input modeling to distinguish the maintenance stages, and to further generate corresponding distributions of these points. These distributions then are used to quantify the uncertainties embedded in the equipment costs through simulations. A historical data set of ownership and maintenance costs for a mining truck model was used to demonstrate the feasibility and validity of the proposed approach. This approach was proven to be effective in predicting the cumulative total cost of equipment, which provides analytical decision support for equipment-management practitioners.

3D-printed construction allows elaborating building elements with diverse shapes that are digitally controlled. This paper exposes the modeling of 3D-printed curved walls through parametric programming in building information modeling (BIM) in order to support a cost-efficient building design. The advantage of using curved walls is based on the possibility of reducing their thickness with respect to straight walls of similar length given their higher resistance to overturning forces. The programming developed here can propose a considerable set of solutions using curved walls for a rectangular enclosure of dimensions given by the user. A case study for a vehicle sale pavilion is shown, for which a set of 1,600 solutions with curved walls of different curvature angles and lengths is generated and subsequently analyzed. From this analysis, those models with lower material consumption and execution time are selected to be more thoroughly studied in the design process. Thus, a novel strategy is provided to researchers and practitioners for developing more efficient and expressive building designs based on 3D-printed construction. The most efficient solution identified in the example reduces material consumption by 61%, with an estimated cost saving of 53%.

Construction robots have emerged as a revolutionary technology for buildings. However, real-world adoption is still limited. Little is known how the decision of adopting construction robots or not is made in organizations, which inhibits the benefits of robots from being fully realized. This paper aims to investigate the determinants affecting construction robot adoption for building works at the organizational level. Drawing on the technology-organization-environment framework, a research model with 11 hypotheses on determinants was developed and empirically evaluated via a survey of 94 typical building contractors in Hong Kong using a partial least-squares structural equation modeling technique. The results indicate that relative advantage, top management support, organizational readiness, market competitive pressure, and high costs are significantly related to the adoption, among which top management support is most influential. A two-stage conceptual framework is derived that provides theoretical insights on the determinants and reveals the complexities and influence dynamics of determinants on the adoption during technology development. The findings offer useful suggestions for the effective development of robots for building works and lay a foundation for construction innovation adoption in a wider context.

The use of agent-based modeling (ABM) in the analysis of construction processes and practices has increased significantly over the last decade. However, the developed models are not able to address both random and subjective uncertainties that exist in many construction processes and practices. Monte Carlo simulation is able to account for random uncertainty, and fuzzy logic is able to account for the subjective uncertainty that exists in model variables and relationships. In this paper, a methodology for the development of fuzzy Monte Carlo agent-based models in construction is provided, and its application is illustrated through the development of a model of construction crew performance. This paper makes three contributions: first, it expands ABM’s scope of applicability by showing how to model both random and subjective uncertainty in ABM; second, it provides a novel methodology for integrating fuzzy logic and Monte Carlo simulation in ABM, which allows for the development of fuzzy Monte Carlo agent-based models in construction; and third, it illustrates a fuzzy Monte Carlo agent-based simulation of construction crew performance, which improves the assessment of crew performance by considering both random and subjective uncertainties in model variables.

During the European Refugee Situation in 2015, Sweden received an influx of forcibly displaced persons seeking asylum, the highest number of asylum seekers per capita among European Union member states. A challenge existed in providing accommodations within the Swedish built environment, in contrast to other housing solutions such as geographically distinct refugee camps. Regulations are one method of maintaining consistent standards of buildings, but they are effective only if they are enforced. During uncertainty, compliance decisions can be based on cognitive understanding (cognitive legitimacy). Otherwise, deterrence-based regulation is rooted in direct consequences (pragmatic legitimacy), and responsive regulation relies on normative motivation (moral legitimacy) for compliance. This study explored the normative aspect of regulatory enforcement through inspections. In 2017, 34 semistructured interviews were conducted with individuals involved with providing temporary accommodations in Sweden during 2015–2016. From these, six interviews with employees of regulatory enforcement agencies were qualitatively analyzed to discover how regulators legitimized. Findings show that regulators justified their inspections predominantly based on what is considered as a socially acceptable approach (procedural legitimacy) and what is understandable based on their personal experience (comprehensibility legitimacy). This study provides practical insight to regulators regarding the benefits for using responsive regulation during institutional responses to mass population displacement in urban communities. Findings will assist in ensuring that the quality of buildings is consistent for the general population and reduces variation in the safety provided by buildings. Additionally, this analysis contributes to the literature addressing mass population displacement and provides new knowledge of organizational legitimacy in technical applications.

A significant number of construction projects involve repetitive processes. Planning them occurs to be a complex task. Network methods traditionally applied to construction scheduling prove inefficient in the case of repetitive construction projects (RCPs) due to the increased number of relationships among processes, locations (units), and resources. Frequently, the aim of scheduling RCPs is making the full use of the resources’ capacity, thereby ensuring continuity of the crews’ work. To achieve this, the planner typically allows discontinuity of some secondary processes to shift resources to the key processes, allowing optional modes of process delivery and introducing soft precedence relations between processes. This paper presents a new way of increasing the continuity of work of a general contractor’s crews in RCPs by employing subcontractors’ crews. A deterministic mathematical model of the mixed-integer linear problem of a repetitive (nonlinear) project was formulated to find a schedule that minimizes the downtime of the general contractor’s crews under constraints of a maximum acceptable total project duration and a maximum acceptable cost of the works commissioned to subcontractors; the model accounts for the limited availability of crews. Application of the model is illustrated by an example.

Selecting an appropriate delivery method for highway projects is a complex decision process. Delivery decisions typically involve evaluating both quantitative (e.g., cost, schedule) and qualitative (e.g., project complexity, delivery risk) criteria. Although many studies have proposed processes and guidance to select the most suitable delivery method, there is a lack of understanding on how to rigorously address qualitative criteria. This study aimed at applying fuzzy cluster analysis to investigate cost performance associated with qualitative criteria, including project complexity and delivery risk. The proposed approach includes three main steps: (1) assess data clustering tendency; (2) determine number of clusters; and (3) validate clustering result. An empirical data set of 254 completed highway projects was used to develop and illustrate the proposed approach. The result shows seven clusters for comparing cost performance between design-bid-build (D-B-B) and design-build (D-B) projects. D-B-B produces low-cost growth for Project Cluster 1, but medium- to high-cost growth for Project Cluster 3. D-B produces low-cost growth for Project Clusters 2, 4, 5, and 7. For Cluster 6, there is no difference in cost growth between D-B-B and D-B. The findings also indicate that D-B outperformed D-B-B in new, complex, and highly risky projects, whereas D-B-B was a better choice in certain reconstruction projects. This study contributes to the body of knowledge by identifying seven groups of highway projects that have many commonalities with respect to project attributes and cost performance associated with the use of D-B-B and D-B. The identified groups may help highway agencies better understand and select the most suitable delivery method for their projects.

Intelligent safety management based on machine vision has become indispensable in reducing collision safety accidents during construction. To prevent collisions between workers and machines in excavation site construction, a real-time intelligent evaluation system to reflect worker–machine safety status was developed. The system included: (1) determination of the key factors affecting the safety of the interactive operation between workers and machines; (2) extraction of precursor semantic information related to the safety assessment for each object in the construction site based on machine vision; and (3) assessment of the safety state of a monitored object using a fuzzy neural network. A case study of excavation site construction is presented to illustrate and verify the entire process of safety assessment using the developed framework. The results show that the proposed model achieves high detection rates: 96% and 94% for tracking accuracy and 91.67% for prediction accuracy.

This paper presents a generic copula-based method for accurate prediction of probabilistic time performance of projects. The proposed stepwise method first collates all uncertainties of the project activities and propagates them using a Monte Carlo simulation (MCS) in cumulative progress S-curves and commercial project risk analysis software. By fitting the beta distribution function to every normalized simulated progress curve, the corresponding parameters of the so-called Beta-S model can be calculated and the best-fit marginal distribution functions of these parameters, including project completion time, and the correlation matrix can be established. In an innovative approach, a multivariate copula function then is employed to bind the marginal distribution function of these random variables together and produce their prior joint probability distribution as a single closed-form function. The merit of this copula-based function is that it alleviates the incorrect assumption of the independence of random variables in the Beta-S model. The actual progress data of the project are used for efficient Bayesian updating of the model by means of the Metropolis-Hastings (M-H) algorithm. The applicability of the proposed methodology is demonstrated on a project, and it is shown to outperform the existing probabilistic model with independent variables and the earned schedule method as a deterministic method.

Building construction sites are highly complex and uncertain, and frequent incidents of dangerous events can easily lead to casualties. Thus, both risk assessment and evacuation analysis are fundamental to safety management. This paper proposed a methodology for risk-based evacuation analysis and illustrated its application with a case study. First, a joint probability model was proposed for the risk assessment of hazardous areas on construction sites, in which hazards are classified and identified by risk factor analysis. Second, a modified two-dimensional cellular automaton (CA) model was established to simulate the evacuation process by coupling the risk probability of hazardous areas into the evolution rule. A case study was presented to show that the risk-based evacuation model can describe the real-life situation as pedestrian movement is disturbed due to the occurrence of random events, especially in hazardous areas with high joint probabilities of risk. This study contributes to the body of knowledge by (1) assessing the risk of hazardous areas on construction sites with a probabilistic method; and (2) simulating the emergency evacuation process with a computational method considering the influence of hazardous areas. Therefore, a systematic approach to risk-based evacuation analysis in which the coupling between risk assessment and evacuation simulation is achieved.

Best-value procurement (BVP) is new in Norway, but there is a plan to use it in several mega infrastructure projects. However, there is no independent research that illustrates what measures ought to be implemented for the success of future projects using BVP. The purposes of this study are (1) to describe the BVP method thoroughly, (2) explore the experiences with the method from the Netherlands and Norway, and (3) develop suggestions for what measures ought to be implemented for the success of future projects using BVP. In addition to a literature review and document study, 11 Dutch cases and 4 Norwegian cases were studied through semistructured interviews with 28 key informants. This study identified several practical challenges practitioners are facing while using BVP. To avoid the problems documented in the case projects, both the method and its implementation should be improved continuously based on the current experiences. Recommendations for improvement are provided in this paper. Testing their effectiveness is a topic for future research. This paper contributes to construction engineering and management practice to increase the understanding of BVP by suggesting what kind of measures ought to be taken for the success of future projects using the method.

Structural steel prefabrication is a generally accepted practice in the construction industry conducive to better control of project duration and cost, higher productivity and efficiency, and enhanced quality and safety performance. This study initiated with an investigation of the current practice of major structural steel fabricators in Alberta, where skilled trades rather than fully automated equipment remain the critical resources in production planning and scheduling. How to synchronize the schedules of concurrent projects at different levels to optimize the allocation of fabrication resources (particularly skilled laborers) is identified as a matter of pressing concern in the industry. In collaboration with an industry partner in Edmonton, Alberta, Canada, we (1) developed a unified work breakdown structure (WBS) based on project management functions such as bidding, production planning, estimating, and scheduling; (2) synchronized schedules for various management functions by implementing a dual-level multiproject optimization scheduling framework; and (3) developed a multiproject scheduling tool to implement the research in practice. A case study of the fabrication of steel girders for three bridges investigates the benefits of the proposed scheduling approach to the fabrication process, which include improved alignment between shop operation progress, individual project objectives, and overall program goals.

The study reported in this paper investigated health and safety management systems (HSMS) found within medium to large construction contractor organizations in South Africa. The aim of the study was to identify the nuances associated with the dominant health and safety management systems within these organizations and to categorize them into types. A qualitative descriptive research methodology was adopted, and data was collected through semistructured interviews involving safety managers, health and safety (H&S) advisors at employers’ associations, and consulting firms. An analysis of the data revealed that the broad spectrum of health and safety management systems within contractor organizations in South Africa can be categorized into three distinct types. Defining features of the three HSMS types across three thematic areas were distilled. The position of the health and safety professional, legislative requirements, supply chain demands, and subcontracting practices of the organization are identified as factors that shape each HSMS type to varying degrees. The categorization and characterization outcomes of this study enables a subsequent objective evaluation of effectiveness and provide explanations for successes and failures of construction HSMS.

Cost overrun in rail transit projects in the United States is a pervasive and constant phenomenon, yet there has been a lack of rigorous statistical analysis on project costs. This has been partly due to the difficulty of obtaining reliable cost data and the small size of the sample. In addition, the Federal Transit Administration (FTA) has required formal risk assessment on transit projects since 2003. Whether this strategy has improved the accuracy of cost estimates is not known. To fill these gaps, this study has collected the cost data of 81 US rail transit projects built during the last 40 years, the largest sample of its kind. It is found that the accuracy of cost estimates at the decision-making phase has improved over time. Also, major projects have higher percentage cost overruns than smaller projects. Further, the accuracy of cost estimates decrease as project implementation durations increase. The utilized statistical methods yielded no evidence to suggest that cost overruns differ between projects designed before and after 2003, when the FTA risk assessment program started. However, since project size and implementation duration have increased over time, the positive impact of the FTA risk assessment program can be inferred and acknowledged to some extent. This research can serve as a reference point for studies on the accuracy of conceptual cost estimates and help in budgeting future projects. It can also help in understanding the causes of cost overruns in capital projects.