Journal of Construction Engineering and Management

This paper presents a review of 153 case studies of equipment interventions to improve safety and health of construction businesses in Ohio in 2003–2016. These represent $6.46 million (2016 USD) in purchases incentivized through the Ohio Bureau of Workers’ Compensation (OHBWC) Safety Intervention Grant (SIG) program. The source data in the review were extracted from employer grant applications and final reports of the case studies. Results were aggregated by type of construction equipment and included the reduction in safety and ergonomic hazards (risk factors for work-related musculoskeletal disorders), and an assessment of the quality of the case studies as determined through criteria established by the authors. Equipment associated with greatest reduction in risk factors and with case studies of higher quality were electrical cable feeding/pulling systems, concrete sawing equipment, skid steer attachments for concrete breaking, and manlifts (boom lifts). This review illustrates challenges in demonstrating efficacy of equipment interventions to improve construction safety/health—even from case studies within a structured health/safety program. The authors are aware of no other systematic review of case studies reporting on experiences with health/safety intervention equipment specific to the construction industry.

Identifying potential hazards of construction project is a data-intensive process that involves various types of information such as site data, specifications, and engineering documents. How to effectively convert the information into a machine processable format for safety management is a challenging task. To address this problem, in this paper, combining the HowNet and specific taxonomies from the relevant construction specifications, a semantic modeling approach is developed for the proactive construction hazard identification from images. A semantic scoring system is then introduced for quantifying the similarities between images, via comparing their annotations with the construction hazard specification. Furthermore, an image processing framework is developed to semantically annotate site images and further automatically classify the images into the categories. In this way, the potential hazards implied in the images can be identified automatically. Examples are developed to demonstrate the feasibility of the approach. The outcomes of this study have offered an alternative method to enhance site safety management on site.

The construction industry is embracing the digital transformation to streamline the process from design to build. However, compared with design companies, precast concrete (PC) companies seem to be slow in the adoption of building information modeling (BIM). The paper thus intends to (1) understand the current BIM maturity levels in these companies via a survey among the PC companies; (2) develop a model for identifying critical success factors (CSFs) influencing BIM adoption from PC companies’ view; and (3) give recommendations to the PC companies as well as policymakers. From the survey responses of 53 respondents from the PC industry in various countries including Association of Southeast Asian Nations (ASEAN) countries, China, Korea, Taiwan, the Middle East, and Finland, the study successfully identified a total of eight CSFs, which are regarded as the main contributions this research makes to the body of knowledge. Based on these CSFs, recommendations are provided to assist PC companies to become industry innovators. Some additional contributions are (1) a new BIM adoption model, which is a mix of a BIM maturity model and a BIM adopters’ model and acted on by driving forces; (2) a new framework (called “PCBIM Hexagon” by the authors) consisting of driving forces from six areas: the government, ecosystem, company, team, process, and BIM software tool; and (3) a new approach for ranking the importance of the factors.

Timely identification and removal of constraints is critical for improving workflow reliability. Construction project managers need to spend a significant amount of working time in various types of planning meetings to discuss, analyze, and remove constraints. However, the amount of uncertainty associated with each constraint, mutual information shared among constraints, and any information gained by addressing those constraints are not necessarily known with any great accuracy or precision. This research used a bridge construction project as a case study to collect empirical data on detailed weekly meeting minutes over 18 weeks and the planned and actual starting and finish times of 475 activities. An information-theoretic approach was used to develop a framework to measure the amount of uncertainty and information sharing among constraint discussions. A Chow–Liu tree was then developed to help project managers optimize the sequence of topics discussed so that meeting time may be spent more productively. Results from this particular study indicate that weekly planning meetings need to strategically address issues of equipment availability, design and working method clarification, and prerequisite work readiness since removing those constraints could improve workflow reliability by 5.6%, 4.9%, and 3.6%, respectively. The contribution to the body of knowledge of this research was the development of a framework to utilize an information theoretic–based approach to measure information exchange, uncertainty, and sharing in construction planning meetings. The method can be applied to other construction projects, and the findings can help project managers plan and run meetings more effectively.

Architectural, engineering, and construction (AEC) project teams are multiteam systems that temporarily gather individuals with diverse expertise to develop a common project. Individuals in these project teams often fail to use properly knowledge that is transferred to them, especially across disciplinary and organizational boundaries. Consequently, project outcomes are less than optimal in terms of time, cost, and quality. Using a multiple regression model, this study examines the key knowledge-related factors that allow individuals to apply the knowledge transferred to them in AEC project teams. Researchers validated the model using longitudinal data from an AEC project team consisting of more than 160 individuals following the Integrated Project Delivery (IPD) method. Peer-evaluated survey data treated with predictive mean matching to estimate the missing values led to measurement of the study variables for 129 team members from 25 disciplines and 12 organizations. Results show that individuals’ absorptive and articulating capacities determine how effectively the individuals can apply received knowledge, although they might lack a wide common knowledge with peers from other disciplines. The study contributes to the body of knowledge by expanding understanding of how AEC project teams and multiteam systems can capitalize on the knowledge-transfer interactions among individuals to optimize project outcomes.

The work environment in construction is physically and mentally demanding. This demanding environment can place adverse risk on the construction workforce including emotional, physical, and financial challenges. To minimize such challenges, continued development and cultivation of the construction workforce is required. Continued development and cultivation can sustain the workforce and lead to both personal and business growth. The process of developing and cultivating the workforce enhances construction workforce sustainability, a measure of the social sustainability of the construction workforce. The aim of the present study is to develop a practical tool for assessing workforce sustainability in construction. A mixed-methods research approach that relied on a review of literature, semistructured interviews, and a multiround expert survey was utilized to achieve the aim of the study. The developed workforce sustainability tool includes three levels of components (attributes, indicators, and metrics) organized in a hierarchy to characterize a workforce. The use of the assessment tool yields a final aggregated score that reveals the level of sustainability of a workforce. The present study contributes to the body of knowledge by providing a means to assess and ultimately improve workforce sustainability in construction. Widespread use of the tool is expected to help the construction industry develop and nurture its workers to produce a healthy, productive, and resilient workforce.

Conceptual cost models do not provide details of materials, equipment, or personnel that make up construction cost. Therefore, obtaining project details for early resource planning and cost control is difficult. A feasible solution to providing valuable details for early resource planning and cost control is to model a constituent part of construction cost, i.e., quantities. Hence, the development of models for predicting conceptual quantities of reinforced concrete structural elements is the aim of the current study. Using design parameters such as live load and soil bearing pressure, predictors were defined and used for constructing conceptual quantity models. A framework for range estimation of conceptual structural quantities using support vector regression was also presented. A total of 12 models were developed using a combination of nonparametric support vector regression and bootstrap resampling techniques. The of out-of-sample prediction intervals showed that bootstrapped support vector regression models can provide useful conceptual quantity estimates. The predicted quantities intervals can assist construction planners in early resource planning and enable performance measurement of early cost predictions throughout the construction process. This study presents two additional contributions to the existing body of knowledge apart from the proposed framework. First, an overlooked predictor variable in the literature for conceptual structural quantities, the gross soil reaction, was shown to be a good predictor of foundation quantities. Second, it shows that conceptual low-level bills of quantities estimates can be provided without sketch drawings—a necessity for measuring scope creep throughout the development of a building project.

The design of social housing is a multifaceted endeavor, as are many social projects, such as megaprojects whose expected benefits reach beyond a handful of stakeholders. Understanding the requirements of such projects can be a painstaking endeavor, and without a structured process to guide decision making, such projects often fail to deliver on their intended benefits. This paper examined how the use of utility theory (UT) and quality function deployment (QFD) can serve as a useful basis to support the delivery of such benefits. The research examined a social housing project in Brazil to focus on contextual influences on understanding, structuring, and delivery of benefits. The approach adopted QFD for requirements management, whereas utility theory assessed utility decision making in ranking the housing models on the basis established user requirements and derived design requirements (DRs). A medium-low-income model is preferred; the next preferred models are very low, medium low, and medium high, in that order. The analysis indicated no transitivity in preference from an end-user perspective. The novelty of the paper is in representing quantitatively a process of requirements management that supports the delivery of project benefits on a utilitarian basis in social housing design. The method is able to account for interdependencies between design and user attributes in supporting social housing design decision making in an integrated process. This understanding can be key to the delivery of the correct social projects that reflect context-specific end-user needs.

It is critical to the successful implementation of construction projects that the behavior of both the owner and the contractor meet common expectations. In the transaction, the economic results and the impact of justice on relational behavior should not be ignored. Our study on the perspective of contractors explores whether and how different dimensions of interorganizational justice affect relational behavior in construction projects and the moderating role of owner’s asset specificity. On the basis of survey data from the construction industry, the empirical results reveal that if contractors perceive higher procedural justice or interactional justice, the contractor’s relational behavior conforms to relational norms more. Furthermore, the owner’s asset specificity dilutes the positive impact of interactional justice on the contractor’s relational behavior. The findings contribute to strengthening the understanding of project management and interorganizational justice while guiding owners to pay more attention to the process of contract execution in practice and the appropriate investment of specific assets in projects to improve the contractor’s relational behavior.

Matching site concrete-placing resources with the appropriate number of concrete-delivery truckmixers depends upon good site and concrete plant coordination if good concreting productivity is to be achieved. In general practice, however, the placing crew usually is idle for some of the pour time waiting for deliveries, and at other times, truckmixers are idle on site waiting to be emptied. In the case of concrete supplied by a circulating fleet of truckmixers, which is a balance point process, two new diagram models were developed relating fleet size to the parameters of placing-crew idle time, truckmixer idle time, truckmixer unloading time, round-trip time, and concrete-placing production rate. The new models augment classical balance point theory. To illustrate practical application: (1) the diagrams were developed and used to reveal system behavior insights for the case of three circulating truckmixers, and (2) the relevance of the new model to a real pour of 46 deliveries was examined in relation to the balancing of site and plant resources for better coordination and system productivity.

Disparities in worker risk tolerance may create barriers to implementing safety management systems and improving safety performance. At present, it is unclear if and to what extent construction safety risk tolerance vary across broad geographic regions. To better understand patterns in these sociocultural constructs, a survey of building trade contractors and subcontractors was administered. Using principal component analysis and K-means clustering, the determinants of risk tolerance were analyzed for 11,997 construction workers from 17 countries via controlled sampling for equal representation. The analysis showed that risk tolerance is influenced and linked by individual and sociocultural determinants, i.e., affective associations, control beliefs, safety culture, and risk-taking attitudes. Differences and distinct groupings were observed when the derived global risk tolerance scores were compared to country-specific risk-tolerance scores. This study contributes to the literature by empirically identifying determinants of risk tolerance and quantifying cross-cultural disparities in risk tolerance. It was found that the natural grouping of countries, based on their risk-tolerance determinants, coincides with their ancestral heritage and socioeconomic systems. The results can be used to inform policymakers, stakeholders, safety professionals, and industry leaders to improve safety decisions in the workplace, promote strong situational awareness, design structural policies, and implement safety programs.

The construction industry has the highest number of casualties per accident, and workers at construction sites are vulnerable to accidents. Current research on unsafe behavior mainly focuses on behavior-based safety at the individual level and safety culture at the organization level; these two safety management rubrics enjoy respective advantages and are essentially complementary. This study takes a psychological approach and examines how personality traits affect unsafe behavior of construction workers. Based on the Big Five personality model and the theory of planned behavior, it is postulated that risk propensity mediates the relationships between personality traits and unsafe behavioral intention of construction workers. Structural equation modeling and bootstrapping techniques were used to analyze data collected from 533 construction workers in China. Results show that (1) personality traits, risk propensity, and unsafe behavioral intention of construction workers are related to each other; and (2) risk propensity plays a partial mediating role on the relationships between personality traits (extraversion, openness to experience, and conscientiousness) and unsafe behavioral intention. The findings provide a deeper understanding of how the perceptions of construction workers affect their behaviors.

This research investigated the influence of the social context on work practices and safety at the principal contractor–subcontractor interface. Previous research has acknowledged that social interactions among supervisors and workers shape safety implementation on work sites. However, a detailed examination of the nature and impact of these social interactions has been missing. An ethnographic method was deployed in which a researcher spent approximately 100 h attending four construction sites over a 6-month period. This deep embedded research method enabled the nature and impact of social interactions to be explored. Organized safety walks and informal walk-arounds were attended, during which interactions were observed and conversations were held with project participants, including foremen, professional health and safety advisors, supervisors, and workers. Field notes and comments were subjected to thematic content analysis. Different understandings of safety were apparent, with key differences emerging in relation to the role attributed to the principal contractors’ site safety rules. The social ecosystem at the four sites was also embedded within broader construction industry practices, in particular competitive tendering and price-based selection. The results reveal limitations inherent in traditional technical approaches to understanding safety in the construction site environment because these approaches tend to ignore the social and power relations at play. The results reveal that safety at the principal contractor–subcontractor interface is better understood as an emergent property of a complex ecosystem of social relationships and interactions.

A large body of empirical and theoretical work has identified associations between emotional intelligence and positive outcomes in large-scale projects. However, scholars have yet to fully understand the mechanisms underlying these links. In this study, we sought to investigate the mediating influences of commitment, team satisfaction, and turnover intention on these associations. An analysis of data collected from 341 project team members in large-scale construction projects showed that construction project workers with higher emotional intelligence exhibit higher satisfaction, greater commitment towards their job, and lower turnover intentions. Additionally, role ambiguity acts to weaken the positive association between team satisfaction and performance. This research contributes to project management literature and has clear implications for building and training project teams in large-scale construction projects.

Over the years, the study of resource-constrained scheduling heuristics has focused on testing different sets of priority rules without paying attention to the conditions under which each heuristic produces better results. Although some authors have recommended the use of specific heuristics over any other rule, these recommendations are general and do not encompass all possible project characteristics in terms of resources and network topology. Without a guidance system, schedulers must try several combinations of rules until they find one that compares favorably (shortest duration) with the results of the other priority rules. This study proposes a new tiebreaker (priority number) that enhances the performance of an existing heuristic and classifies the heuristics’ performance based on specific project characteristics. The results show that, as a tiebreaker of the late finish rule, the priority number leads to obtain schedules with lower deviations from the critical path method (CPM) duration and a higher number of shortest schedules than with traditional tiebreakers. The proposed classification system indicates the two heuristics with the best performance for specific resource network characteristics. This classification will help practitioners to decide which heuristic applies when mitigating the resource supply–demand problem given the project characteristics.

Researchers and practitioners are focusing greater attention on safety citizenship behavior (SCB), an important factor in preventing injuries and improving workplace safety conditions; however, very little research has been done to study the cognitive mechanisms of employees engaged in this proactive safety behavior. This study aims to explain critical antecedents and cognitive mechanisms of construction workers’ SCBs based on an integrated theoretical framework of the theory of planned behavior (TPB) and norm activation model (NAM). This study was conducted by distributing a questionnaire survey to 719 construction workers in China. The results of structural equation modeling (SEM) indicated that personal norms (PNs), attitudes toward SCB (ATT), subjective norms (SNs), and perceived behavioral control (PBC) have significant positive impacts on the intention of SCB, and the size of direct effects decreases in turn. The study also confirmed the significant indirect effects of SN on behavioral intention by way of ATT, PBC, and PN; ATT, PN, and PBC predicted SCB through the mediating processes of SCB intention. The study demonstrates the applicability and effectiveness of the integrative model in predicting workers’ intentions to adopt SCBs and provides targeted suggestions and strategies for the sustainable development of safety management. These findings have meaningful implications for academic research on SCBs and the industrial practice of safety management.

The need to enhance worker safety in construction has often studied recently due to the high rates of fatalities and injuries in the construction industry. To abate this scourge, various control methods including the use of technology have been adopted to improve workplace safety performance in construction. However, not all of the technologies adopted by industry personnel for safety management have achieved the desired outcome or have proved to be effective. More research is needed regarding the adoption of safety technologies in the construction industry. Importantly, industry stakeholders need to evaluate their readiness to adopt safety technologies. The present study develops a decision-making tool that can aid in the adoption of safety technology in the construction industry. The safety technology decision-making adoption tool is developed using the fuzzy synthetic evaluation technique and consists of three categories of safety technology predictors: external, organization, and technology-related. Technology-based predictors have emerged as the most impactful predictor. Subsequently, a technology adoption assessment protocol was developed and applied to a case study to assess an organization’s readiness to adopt wearable sensing devices. The present work contributes to the body of knowledge by identifying, quantifying, and categorizing predictors of technology adoption and then integrating the predictors into an evaluation protocol to assess the adoption of safety technologies in the construction industry. This contribution is translated into practice by developing a user-friendly tool called the Construction Safety Technology Adoption Index (C-STAI). The developed tool is expected to help construction professionals and practitioners make informed decisions regarding the adoption of safety technology. Improved technology adoption in construction is expected to lead to enhanced safety and reduce injuries on construction projects.

Payment mechanisms lie at the heart of public-private partnership (PPP) contracts. A good design of the payment mechanism should consider the owner’s goals in the project, allocate risks appropriately to stakeholders, and assure satisfactory performance by providing reasonable compensation to the private developer. This paper proposes a Stackelberg game theory–based model to assist public agencies in designing payment mechanisms for PPP transportation projects. The interests of both public and private sectors are considered and reflected by a bilevel objective function. The model aims to search for solutions that maximize a project’s overall performance for the sake of social welfare while simultaneously maximizing return for the sake of private investment. A variable elimination method and genetic algorithm are used to solve the optimization model. A case study based on a real PPP project is discussed to validate the effectiveness of the proposed model. The solutions provided by the model reveal that the optimal payment mechanism structure could be established such that it would satisfy owners’ requirements for overall project performance while optimizing project total payments to contractors.