Journal of Construction Engineering and Management

Research in multiskilling experiences an incremental trend from academic, industrial, and governmental point of views. Different methodologies have been used in previous research to identify best practices in multiskilling. A comprehensive and critical review of the literature creates in-depth insight into the dynamics of multiskilling in different settings. Accordingly, the aim of this literature review was twofold. First, it set out to present appropriate classifications to illustrate the ramifications of crosstraining in order to help create a better understanding of this concept. Second, it aimed to identify research gaps that can be the focus of future research. A two-stage paper screening methodology was used to collect relevant papers. This study classified papers from the early introduction of multiskilling in the construction industry until the present and categorized crosstraining based on configuration, strategy, collateral effects, context, and mainstream research. The main contribution of this study is to identify theoretical gaps in crosstraining research and to discuss the interaction and dependency between different studies. Furthermore, it paves the way for comprehensive studies to produce more realistic knowledge that considers both technical and managerial details.

Automated worker action recognition helps to understand the state of workers’ actions, enabling effective management of work performance in terms of productivity, safety, and health issues. A wristband equipped with an accelerometer (e.g., activity tracker) allows to collect the data related to workers’ hand activities without interfering with their ongoing work. Considering that many construction activities involve unique hand movements, the use of acceleration data from a wristband has great potential for action recognition of construction activities. In this context, the authors examine the feasibility of the wrist-worn accelerometer-embedded activity tracker for automated action recognition. Specifically, masonry work was conducted to collect acceleration data in a laboratory. The classification accuracy of four classifiers—the k-nearest neighbor, multilayer perceptron, decision tree, and multiclass support vector machine—was analyzed with different window sizes to investigate classification performance. It was found that the multiclass support vector machine with a 4-s window size showed the best accuracy (88.1%) to classify four different subtasks of masonry work. The present study makes noteworthy contributions to the current body of knowledge. First, the study allows for automatic construction action recognition using a single wrist-worn sensor without interfering with workers’ ongoing work, which can be widely deployed to construction sites. The use of a single sensor also greatly reduces the burden to carry multiple sensors while also reducing computational cost and memory. Second, influences associated with the variability of movement between subject and experience group were examined; thus, a consideration of data acquisition that reflects the characteristics of workers’ actions is suggested.

Poor hazard recognition is a widespread issue in the construction industry. When construction hazards remain unrecognized, workers are more likely to indulge in unsafe behavior, experience unanticipated hazard exposure, and suffer catastrophic injuries. To improve our understanding of why construction hazards remain unrecognized, the current study examined hazard recognition as an everyday visual search task—similar to an individual searching for a product in a supermarket, a radiologist examining a radiograph for tissue abnormalities, or a security personnel screening baggage at an airport terminal. More specifically, the research used eye-tracking technology to examine the relationship between visual search patterns adopted by workers while participating in a hazard recognition activity and the resulting performance levels (i.e., hazard recognition performance). The research also focused on testing the effects of introducing a recently developed personalized training intervention on visual search patterns adopted by workers—and the subsequent hazard recognition performance. Visual search patterns examined in the study included search duration, fixation count, fixation spatial density, and others identified from past research. The results reveal that several quantifiable visual search patterns are predictive of superior hazard recognition performance. For example, workers who spent more time examining the workplace for safety hazards (i.e., search duration) recognized a larger proportion of hazards. Likewise, workers that devoted higher levels of attention through a larger number of fixations (i.e., fixation count) and longer fixation durations (i.e., fixation time), and those that distributed their visual attention more broadly across the work area (i.e., fixation spatial density), demonstrated superior performance. The findings also suggest that the adoption of the personalized intervention can lead to improvements in visual search patterns and hazard recognition performance among workers. The present research will be useful to diagnose and remedy search weaknesses demonstrated by workers (i.e., human factors) that are associated with poor hazard recognition levels.

Modular construction offers several advantages, such as safe working conditions, fast construction, and high-quality end products. The project scheduling of modular construction should consider the time involved in the factory production, transportation, and erection processes of the units given limited resources (e.g., the number of modular units, available transportation, and workers) and project restrictions (e.g., the sequences of manufacturing production and on-site installation). However, current modular construction project scheduling techniques and previous academic approaches cannot deal with the overall project processes and production/on-site installation sequences simultaneously. This is one reason few mass-production multiple projects exist in the manufacturing industry. Moreover, it can lead to inappropriate project management guidelines and fail to minimize the overall project duration. This paper suggests a model for modular construction projects scheduling using a genetic algorithm. The results show that implementing both overall project processes and project requirements is an efficient way to develop a modular construction project schedule. The proposed optimization tool will help project managers allocate resources efficiently and reduce the completion time of multiple projects.

Despite the improvement of digital technologies (e.g., building information modeling) in enhancing construction safety management, human factor–related issues such as individual perceptions, attitudes, and behavior in safety cannot be downplayed. Existing studies have adopted safety management approaches that address human-factor issues by defining safety climate. From safety climate research, it is evident that certain demographics or subgroup factors can significantly affect safety management. This study aimed to investigate how individual perceptions of safety hazards would be affected by the given hazard’s own feature (e.g., probability of occurrence). In addition, the study explored the impacts of subgroup demographic factors (e.g., job position and experience level) on safety perceptions. Eight commonly encountered site hazard/accident scenes were predefined according to their occurrence, severity, and visibility. A site survey approach was adopted to investigate how construction employees from different demographic subgroups rated the degree of danger of the eight predefined scenes. The follow-up statistical analysis revealed that (1) a hazard/accident scene with higher occurrence and lower severity caused a higher variation among employees’ opinions in perceiving its degree of danger; (2) entry-level employees tended to evaluate hazards with a higher degree of danger; and (3) compared to early career employees and senior peers, the mid-career professionals tended to perceive a lower degree of danger of a given hazard/accident scene. This study contributed to the body of knowledge in construction safety by investigating the effects of the given hazard/accident’s feature (e.g., occurrence) in employees’ perceptions, as well as integrating different scenes of safety hazards in the subgroup analysis based on employees’ job duties or work trades and their experience levels. Future research is also recommended to address individuals’ safety perceptions and demographic factors in safety management.

Monitoring systems based on cameras are crucial for safety management of the high-risk activities common in the complex environment of metro station construction sites, and hence there is a need for a reliable camera network system. However, the number and locations of cameras are currently estimated according to the experience of managers/engineers, and the occlusion effect is rarely considered, resulting in weak coverage of 3D spaces in practice. To address this gap, a novel approach is presented to optimize camera placements for 100% site coverage while considering the occlusion dynamics caused by sidewalls and supports during excavation in metro station projects. A case study of a metro station project is presented to demonstrate and validate the approach, with results showing that the optimized plans for camera placement have larger coverage than the original plan and better performance in safety monitoring and controlling hazards. Optimized in this way, the plans for camera placement can help managers with effective planning and enhanced safety management of metro station construction sites.

Within the construction industry, electrical transmission and distribution workers (TD workers) account for one of the highest fatality rates. Because of the hazardous nature of the work, there is little margin for human error. Previous research shows that fatigue exacerbates human error, thus representing a critical safety factor for TD work. Although researchers have studied the causes and consequences of fatigue in laboratory settings and in other industries, there is no research specific to TD worker fatigue. To address this knowledge gap and explore the principal fatigue causes and consequences as recognized by the workers; 143 TD power company workers were interviewed using a standardized questionnaire. Additionally, fatigue identification and mitigation techniques relevant to TD work and the impact of fatigue in accident causation were discovered. The results revealed that TD workers perceive extreme temperatures and long shifts to be the principal causes of their fatigue, resulting in reduced work pace and the loss of attention as the primary consequences. The results suggest that fatigue laboratory research may not directly apply to field conditions.

In certain circumstances, near-miss falls can evolve into fall accidents in construction sites. Insight into near-miss falls offers an efficient way to better understand fall accidents. In this context, this paper explores potential applications of the smartphone as a data-acquisition tool to detect and identify near-miss falls on the basis of an artificial neural network (ANN). In training and evaluation experiments, a loss-of-balance (LOB) environment was artificially established by means of a balance board to simulate the scenarios in near-miss falls. Through a transition model between static and dynamic near-miss falls, the similarity between simulated and actual scenes of near-miss falls was improved. Furthermore, the feasibility of adopting ANN to correctly identify near-miss falls was verified. The results showed that the average precision, recall, and F1 score were 90.02%, 90.93%, and 90.42%, respectively, with an average error-detection rate of 16.26%. In test cases, the thresholds $H_{20\%}$ (0.07692) and $H_{10\%}$ (0.06061) were acquired and illustrated from the perspective of probability. This approach, which demonstrates the feasibility of integrating smartphones and ANN to measure near-miss falls, will help detect near-miss fall events and identify hazardous elements and vulnerable workers. In addition, it provides a new perspective for measuring the relationship between near-miss falls and fall accidents quantitatively, laying a solid foundation for better understanding the occurrence mechanisms of both events.

Although previous studies have suggested that the level of owner-provided design can influence the performance of design-build (DB) projects, few have explicitly and empirically investigated the effect of the level of owner-provided design on the contractor’s design quality. This paper fills this gap by empirically investigating the effect of the level of owner-provided design on the design quality of contractors in DB projects. Questionnaires were collected from 243 Chinese project professionals and hierarchical regression analysis was conducted. Empirical results reveal that the level of owner-provided design negatively affects design innovation and positively affects design change. The contractor’s competence strengthens the effect of owner-provided design level on design innovation. A minimum proportion of owner-provided design can improve design quality. It is also necessary to ensure the clear articulation of requirements in the request for proposal (RFP). The findings contribute to the understanding of the relationship between the level of owner-provided design and the contractor’s design quality in DB projects. These findings also give owners insight into the preparation of the RFP with regard to the optimal level of design.

The purpose of this paper is to present a structure of governance for activities and processes applicable to the architecture and engineering sector for the execution of projects and public works. It is intended that this structure will orchestrate the various necessary actions, ranging from the identification of the complete organization’s delivery scope, through to delivery disorders, activity structure, and the way in which these activities are to be managed. The current literature does not include a complete structure applicable to public service that can be widely used and with short learning time, such as the presented structure. Based on a business process management (BPM) cycle, the stages of planning, analysis, modeling, and optimization of processes were developed. In the planning phase, the typical activities of a sector responsible for the architecture and engineering projects and works of a public institution were identified and categorized, and possible dysfunctions were verified. Subsequently, analysis and modeling of the prioritized processes was performed, and finally, optimization and validation of the modeled processes were completed. The results obtained demonstrated that the proposal can be applied to seek improvements in public organizational environments regarding issues related to architecture and engineering projects and works through the management of their processes. Moreover, the lessons learned have indicated some common problems of the administrative units responsible for this type of activity that need to be addressed.

A long investment cycle and complex environmental changes bring lots of uncertainty to urban rail-transit (URT) project investment, and lead to many investment risks. However, investors’ aggressive flexible management strategies may effectively avoid the risks and create greater investment benefits. In order to scientifically evaluate the potential benefits brought from flexible management, this paper systematically builds an analysis framework for investment benefits evaluation based on real options theory. Previous studies have still been confined to the single option benefits measurement and the evaluation methods assume that expected investment cost and income were constant. To fill the gap, this study takes account of the interaction effects of several real options and uses fuzzy theory to relax some parameter hypotheses, then establishes a multiple-fold compound real-option benefits evaluation model based on trapezoidal fuzzy numbers. Another contribution of this paper is that it provides a clear modeling approach for estimating the parameters of investment benefits volatility and discount rate by using historical open data in the financial market. A case study of the Changsha Metro Line 2 in China is discussed, and the empirical analysis shows that option benefits account for a large proportion of the entire investment returns. Moreover, the compound option benefits are about five times of the single option, so the implied option benefits, especially the compound option benefits, should not be ignored in the measurement of the URT project, and investors should be encouraged to actively implement flexible management strategies. Finally, this paper makes a sensitivity analysis of compound option benefits, and provides a basis for effectively reducing the government’s subsidies and achieving profitability of URT project investment.

A construction company may invest capital and participate in a special purpose company (SPC) for financing a plant project if high profitability is expected from product production after completion. Thus, the construction company should decide whether to invest on the basis of production costs as well as construction costs. The impact of production costs on profitability is especially large, because products are produced over a long period. This study proposes a method for predicting raw material prices with the aim of contributing to more accurate predictions of profitability. The prediction method is a multivariate time series analysis and the prediction target in this study is the price of iron ore, which is the largest contributor to the price of raw materials for steel products. Following established practices and previous studies, the accuracy of the prediction results was compared with past average values over a specified period. The proposed method was found to be more than 2.3 times more accurate than past average values. The proposed method was applied to predicting the price of iron ore in this study, but for the improvement of prediction accuracy the method may apply to other raw material prices that do not use a statistical method for prediction.