Journal of Construction Engineering and Management

Physical fatigue is a major health and safety–related problem among construction workers. Many previous studies relied on interviews and/or questionnaire to assess physical fatigue in construction workers. However, these traditional methods are not only time-consuming but also limited by recall bias. To overcome these limitations, many researchers have used physiological metrics (e.g., heart rate, heart-rate variability, skin temperature, electromyographic activity, and jerk metrics) to measure real-time physical fatigue. Although physiological metrics have shown promising results for real-time assessments of physical fatigue, no state-of-the-art review has been conducted to summarize various physiological metrics in measuring physical fatigue among construction workers. Therefore, the current state-of-the art review aims to summarize existing evidence regarding the use of physiological metrics to measure physical fatigue of construction workers in real-time. This review used systematic searches to identify relevant studies and critically appraised the application of physiological metrics in measuring physical fatigue of construction workers. First, it summarizes the application of various physiological metrics for real-time measurement of physical fatigue in construction workers. Second, various wearable sensing technologies for measuring physiological metrics are identified. Third, this review discusses the potential challenges for applying physiological metrics to measure physical fatigue. Finally, future research directions to advance the development and adoption of various physiological metrics to monitor and mitigate physical fatigue in construction workers are discussed.

Three-dimensional (3D) printing in construction technology has rapidly developed in recent years and is being implemented on a prototype basis for relatively small building and bridge construction projects. A number of 3D printing-based solutions are still at the laboratory experiment stage. Thus, understanding how to adapt 3D printing successfully is still one of the challenging issues facing the construction industry. Although the innovative literature provides clues about potentially relevant factors for construction 3D printing technology adaptation, research has still not specifically addressed this topic. This knowledge gap hampers further advancements in 3D printing for construction, launching new products, and the development of related services for the industry. Hence, as a response to the aforementioned shortcomings, this paper reveals nine potential factors and thirty-two of their measurements that determine the decision to adapt 3D printing technology for construction projects. To quantify the influence of the relevant factors and measurements, the structural equation modeling technique was employed. The results indicate that the top significant factors in ensuring the success of 3D printing technology in construction are “technology compatibility,” “supply-side benefits,” and “complexity.” Based on these findings, current and future 3D printing project managers may utilize the results of this study to guide their efforts in adapting this technology to produce high-value construction components and related products and to generate new business opportunities.

Building and urban railway construction are two typical high-risk types of construction in which unsafe behaviors have a considerable impact on the occurrence of accidents. To explore the patterns of unsafe behaviors and promote accident prevention, a comparative analysis is conducted to analyze the behavioral risk chains of accidents for these two construction types in the Chinese construction industry using complex network (CN) theory. First, accident cases are collected from government websites, and unsafe behaviors are extracted and classified according to a summary list of several safety standards and operating procedures. Second, behavioral risk chain networks of accidents (BRCNAs) for the two construction types are established as directed networks. Pajek, a program for visualizing and analyzing large networks, is then used to construct network models. Finally, the topological parameters of the BRCNAs are calculated and compared. The results show that BRCNAs are both scale-free and small-world networks. The characteristics of the building construction BRCNA are related to individual workers, and the characteristics of the urban railway construction BRCNA are related to the working environment and supervisors. However, there are certain unsafe behaviors that play an important role in accidents in both construction types. These findings reveal the variations in interactions among unsafe behaviors that cause accidents in the two construction types to further understand the patterns of unsafe behaviors. This study is of theoretical and practical significance for improving accident causation models and supporting on-site safety management in the construction industry.

A concrete mix design aims to obtain the optimal proportions of concrete ingredients, including cement, water, sand, coarse aggregates, and admixtures. Neural networks (NNs) have been widely applied in research to predict concrete strength, and the concrete ingredients and concrete strength metrics have been used as the input and output parameters, respectively. The objective of this study is to use the 3-day concrete strength as the prior information in the NN training to reduce overfitting and improve the 28-day concrete strength prediction capability. This study is unique because the 3-day concrete strength was not used as another input parameter in the NN training; instead, it was used as data for the initial weights and biases of the connection node in the hidden layer during the NN training. Accordingly, a prior information-based NN model (PI-NNM) was developed to obtain a 28-day concrete strength prediction model. According to the tests with data subsets, the PI-NNM showed a better prediction capability than the conventional NN model, which uses only input parameters for the concrete prediction. Moreover, an adjusted PI-NNM was applied to the actual concrete production; the results showed a high prediction capability for the 28-day concrete strength.

The shortage of skills together with the knowledge loss due to employee retirements is compelling trade contractors to capture the know-how from retirees to prepare the future workforce. Compared to explicit knowledge, the tacit dimension is relatively unexplored in the construction industry. To capture tacit knowledge, it should be first identified in practice. This research posits an instrument to gauge the reliance of field supervisors on tacit knowledge and identifies barriers to knowledge sharing through case studies involving electrical contractors. Findings reveal that there is a significant relationship between the individual’s experience and reliance on tacit knowledge, especially field supervisors with 20 years of work experience or more. It is also identified that field staff and management have contrasting opinions regarding the ranking of barriers to knowledge sharing. Management prioritized lack of formal processes as the key barrier to knowledge sharing at the site level, whereas for the field team it is the lack of socialization and encouragement. The proposed study will aid trade contractors to strengthen the existing knowledge-harnessing strategies by categorizing the supervisory knowledge into tacit and explicit, simultaneously identifying barriers to its sharing.

Addressing ergonomic risk factors in modular construction is critical to enhancing the productivity of production lines and reducing the societal cost burden for worker recovery. Ergonomics-driven workplace design is thus essential to not only proactively reducing risks through design changes but also accommodating medical restrictions for workers returning to work after an injury or illness. However, efforts to realize this two-fold approach are confounded by the lack of a sound approach to identifying the root causes of ergonomic posture risks among various workplace design parameters or an optimal workplace design for multiple tasks. To address this limitation, this paper proposes a parameterized workplace design optimization framework that consists of four core components: workplace design initiation, interactive worker–workplace simulation, development of predictive surrogate models, and workplace optimization. The framework is demonstrated through a case study designing a drywall preparation workplace in a real modular construction plant. The results show that the framework enables the design of the optimal workplace (i.e., the one with minimal ergonomic risk) and provides a guide for devising practical action plans for safety improvement.

Investment estimation is a key component of early decision-making for a construction project, which is crucial to the project cost control. Currently, most investment estimation researches render the point value results, which could lead to considerable uncertainty in the estimation results and increase the risk of decision-making. Therefore, it is essential to explore a type of systematic, accurate, and effective estimation method. This study proposed an innovative estimation method of probability interval prediction based on the distribution of prediction errors. First, the dimension reduction of the construction indexes was conducted by using exploratory factor analysis (EFA). Then, a model was developed based on the fusion of the support vector regression (SVR) and grey wolf optimization (GWO) algorithm. Finally, cost intervals with different confidence levels were obtained on the basis of kernel density estimation (KDE). The case results indicated that when the confidence was 95%, the comprehensive evaluation index coverage width-based criterion (CWC) and the interval coverage rate PICC of the cost estimation were 2.17 and 93.33%, respectively. Hence, the proposed interval prediction model was fairly reliable, which could provide practical guidance for the investment decisions in the early stage of construction projects and give the decision makers more abundant forecasting information.

High risk in actual traffic demand often causes a serious imbalance in interests between the public and private sectors in transportation public–private partnership (PPP) projects. The party who suffers has the incentive to breach the contract and hopes to make up for their loss by renegotiating the PPP concessions. The high frequency of such concession renegotiations has raised questions about the viability of the PPP approach. To study how concession renegotiation behaviors evolve when actual traffic volume is lower or higher than expected, this study introduces traffic changes as a quantitative financial indicator. Moreover, it develops two evolutionary game models in which the private and public sectors initiate the renegotiation. The thresholds for renegotiation by the private and public sectors can be defined based on the models and provide decision support for the governance of concession renegotiation behaviors in transportation PPP projects to reduce the incidence and incentives to renegotiate PPP contracts.

Public-private partnerships (PPPs) have motivated diverse bibliometric analyses of PPP research. Nevertheless, the evolution and connections between the main topics in the PPP domain have not been explored. To improve the understanding of the PPP research agenda, this study uses Content Analysis (CA) and Semantic Network Analysis (SNA) to expose relationships and trends in PPP research topics over the last two decades. These methodologies helped researchers to move beyond analyzing thematic trends in isolation in order to gain insight into the interrelationships among PPP topics by using qualitative and quantitative tools, which is difficult to achieve with other methodological approaches. The SNA metrics showed that the PPP research agenda evolved into a high-clustered network driven by five macrokeywords: financing and economic aspects, road infrastructure, public sector management, risk management, and contract management. These macrokeywords may enable the growth of the entire PPP research agenda, but they could also jeopardize the emergence of new research trends. Results show that the participation of some traditional PPP infrastructure types, such as power generation, rail, and tunnel, has been reduced, in contrast to more recent types such as healthcare and, especially, airport infrastructure. Similarly, research focus has shifted from examining traditional PPP issues in the US and European Union to analyzing PPP development in Africa, Latin America, and Asia. Overall, this study presents a more holistic understanding of the PPP body of knowledge, its evolution, and the interaction of its main topics.

Any occupation requires a certain set of competencies at a given employment level. Although in the construction industry the competency requirements for mid- and upper-level employees are very well defined, such clear definitions at the workers’ level are missing. Literature suggests that along with practical skills, workers require additional socioemotional, cognitive, and technical competencies to perform their work efficiently. Therefore, through a nationwide survey of 598 industrial professionals in India, 6, 9, and 13 competencies were identified for unskilled, semiskilled, and skilled categories of workers, respectively. These competencies included attitude, motivation, and physical strength for unskilled workers; self-confidence and technical knowledge for semiskilled workers; and writing, reading, mathematical, and problem-solving skills for skilled workers. These results will have threefold implications. First, they will assist training providers designing an effective worker training program. Second, they will help the development of a career progression path for the workers. Third, if the identified competencies are considered in workers’ categorization process, then the latter will become more robust than the existing one, in which only practical skills are considered.

Although the Occupational Safety and Health Administration (OSHA) has played a key role in decreasing safety accidents on construction sites, such reduction is not well perceived among the different types of incidents. For instance, while the injuries resulting in days away from work were substantially decreased, construction fatalities rose in the previous years. While previous safety fatality research work concentrated on studying how different variables could influence the number of fatalities in the construction industry, there is a lack of research efforts that studied the associations, interdependencies, and critical combinations between the different fatality causes. As such, to solve the critical fatality problem that the construction industry is facing, more advanced management methods should be applied. That said, this paper aims to address this important knowledge gap through a methodology based on data-driven computational algorithms. The data analyzed in this paper is comprised of 100 OSHA national fatal accident case files that are prepared by OSHA compliance officers. First, a reference matrix of 100 case files was constructed based on 60 causes that have shown to be the most pertinent to construction fatalities based on a previous study. Second, the spectral clustering computational algorithm was used to cluster the fatality causes based on the strength of their interconnectivities. Third, the frequent pattern data mining and Apriori algorithms were used to determine the critical combinations and associations of causes that mostly contribute to fatal accidents on construction sites. The findings indicate that the 60 fatality causes could be categorized into five clusters. Thus, the most critical combination of fatality causes was determined within each one of the identified clusters. Also, the findings reflected that while safety accidents could happen due to factors or causes that are individually critical, fatalities on construction sites could also result due to a combination of factors that might not be perceived to be critical on the individual level but rather become critical when combined with other factors. This study adds to the body of knowledge by proposing a data-driven accident causation approach to analyze construction fatality accidents, which adds a different dimension to the traditional analysis of safety accidents by factoring possible associations and combinations between different causes. That said, this paper equips safety professionals with a proactive approach that allows them to take the needed preventive actions to avoid fatalities on construction sites by identifying, in hindsight, the critical combinations and associations of fatality causes. Ultimately, the outcomes of this paper would enhance the safety performance in the construction industry and prevent construction fatalities.

Improper planning and management of heavy lifts is a major cause of cost overruns, delays, and, more importantly, safety incidents in industrial megaprojects. Automated lift planning is widely acknowledged as an effective solution. This research presents an automated lift path planning system for mobile cranes leveraging space discretization and an obstacle-avoidance technique from robotics. The proposed method treats the lifted object as a three-degree-of-freedom convex traveling through the surrounding environment [a given two-dimensional (2D) elevation] with discretized rotational and translational motions in polar coordinates. It efficiently finds the best feasible pick location and optimized collision-free lift path in the polar coordinate system to the set location. This system is a state-of-the art advancement in crane path planning because it mimics the crane’s intrinsic behavior and generates paths considering cost functions for safety, practicality, and economic objectives to enable its implementation in real practice settings. Illustrative examples are presented to verify the proposed approach and demonstrate its superiority over past similar path planning systems in terms of optimality and operational ease.

The construction industry has been plagued with a labor shortage, poor productivity, high material wastage, and unsafe working conditions predominantly due to monotonous and labor-intensive activities. Unlike construction, other industries have successfully adopted prefabrication, automation, and robotics to enhance their productivity, safety, and sustainability throughout their processes. However, the construction industry is unable to fully integrate such groundbreaking technologies due to various challenges. To date, there is scarce research that identifies and documents the existing barriers to the usage of robotics, particularly within the United States construction industry, thus impeding the mainstream adoption of robotics due either to a lack of identifying such challenges or poor understanding of them. The goal of this study is to identify the documented barriers to adopting robotics in the US construction industry, which in turn can support a profound understanding of such barriers as well as informing the construction industry and academic researchers about how to look for solutions for these barriers. This effort of identifying the challenges to using robotics is the foundation to support and advance the adoption of such technology by, for example, fulfilling the need to provide robotic training for the future US workforce. To achieve this goal, this research targeted high-quality journals from ASCE as well as the ScienceDirect library and, as a result, identified 14 unique barriers through a critical literature review of 291 research articles published between the years 1980 and 2020. The study specifies unique findings that contribute to construction management, including planning, productivity, practices, workforce education and development, and construction automation, bodies of knowledge by identifying the challenges behind the uncertain adoption of robotics in the US construction industry. The findings of this research are also critical to the profession in terms of understanding the myths associated with the uncertainty impacts of using robotics in relation to our future workforce. The US construction industry needs to initially realize the benefits, target the barriers based on difficulty of resolution, and then exploit opportunities through addressing the barriers that impede the integration of robotics, which could ultimately bring about a sustainable paradigm shift in construction practices.