Journal of Construction Engineering and Management

Epoxy asphalt concrete (EAC) is widely used in long-span steel bridge paving due to its good performance. It can be classified into two types of materials based on its construction temperature: warm mix epoxy asphalt concrete (WEAC) and hot mix epoxy asphalt concrete (HEAC). While many previous researchers have conducted studies in material designs and performance evaluations, few studies have demonstrated the construction technology of EAC in steel bridges. Additionally, few studies have been conducted to help managers select the appropriate EAC type. This study carried out a preliminary evaluation and comparison on the construction conditions and technologies of WEAC and HEAC. The viscosity properties of epoxy asphalt binder are characterized first to determine the construction temperature and allowable construction duration. The construction procedures including material storage, steel deck pretreatment, tack coats application, mixing and compaction of EAC, construction vehicles management, construction duration, and curing time are detailed. The situations under which WEAC or HEAC can be used are also discussed. Generally, WEAC is suitable for the use of new bridge paving in dry regions, while HEAC is more appropriate for the use in the maintenance and rehabilitation of bridge deck pavements with heavy traffic. Selection of the EAC type for a specific steel bridge paving project should take into consideration various factors such as construction requirements and environmental impact.

A terrestrial light detection and ranging (LiDAR) can be used to construct the building information modeling as well as to measure shape deformation that varies with time or load during or after construction. Thus, the measurement can be utilized for risk management and the completion inspection during and after construction, and for life cycling cost analysis to decide the reconstruction of existing structure. In this regard, this paper presents a practical feasibility study of a shape information model to monitor deformation or deflection of bridge structures with data loss minimization and computational efficiency. The 3D position information of the structure was obtained using terrestrial laser scanning, and the octree data structure was used for efficient processing of the acquired large-scale scan data. To accomplish this, we constructed a shape information model which means a 3D visualization process on the basis of the octree algorithm, which converts point cloud data via voxel to the improved octree structure by efficient management of empty nodes. First, laser scanning was carried out on a steel box-girder bridge (Bridge 1) and a steel I-girder bridge (Bridge 2), and the data for $35.4\times 29.2\times 6.89{\text{m}}^3$ and $9.3\times 5.4\times 1.8{\mathrm{m}}^3$ in each test-bed area were compressed from 158.72 to 13.05 MB with 91.7% reduction for the Bridge 1 and from 21.5 to 2.3 MB with 89.3% for the Bridge 2. As a next step, the shape information model was applied to the deflection estimation of another steel box girder bridge in service (Bridge 3) associated with the octree space division. The results showed the reasonable deflection values in comparison with the LVDT measurement. The proposed approach can provide useful information for effective condition evaluation and maintenance of bridge structures.

Modular construction uses building modules manufactured inside the controlled environment of factories to be transported and assembled on site. Modular construction can be considered as a hybrid of manufacturing and construction, and in many ways lends itself to lean production. Lean production, originating in the automotive industry, permits significant improvements in terms of productivity and quality. The latest challenge of lean production is its implementation in the modular building industry, where the full potential in terms of productivity is yet to be achieved. The paper discusses the implementation of lean techniques in the modular building industry, including all the production process stages (i.e., design, manufacturing, transportation, assembly on site, and supply chain) through a systematic review. Moreover, the paper provides a chronological and comprehensive review of lean methodologies and tools in manufacturing and traditional construction industries to further investigate the interface of lean and modular construction. This may assist practitioners with an overview of lean strategies and associated potential benefits and obstacles for future case studies.

In construction, the most relevant systems used for project management (PM) and project production management (PPM) in the planning and control phases are critical path method (CPM), last planner system (LPS), and location-based techniques (LB). Studies have addressed these systems, mostly in isolated fashions. This study aims to compare and contrast their use in terms of PM and PPM and clarify industry benefits in order to eliminate potential misunderstandings about their use. A survey was administered to construction professionals in Brazil, China, Finland, and the United States. No single system addresses all needs of PM and PPM. CPM is the dominant system when considering the following characteristics: primary industry types, type of organization, size of organization, professional position within the organization, and area of work. Contributions to knowledge include that CPM is a contract requirement with perceived benefits associated with critical path analysis; LB and LPS have perceived benefits regarding continuous flow and use of resources, treatment of interferences, and improving production control. All systems were found to have a similar level of benefits for management of contracts, delay and change, and evaluation of the root causes of delays. The industry can benefit from aligning project scheduling methods with project needs.

Damages to underground utilities are unfortunately a common occurrence on many construction projects. Damaging an underground utility during construction often leads to work delay, road closures, social costs, and safety accidents. Through a combination of damage data analysis and a construction practitioner survey, this paper examined the current deficiencies in damage prevention efforts as well as the possible root causes. Understanding the current root causes that contribute the most to underground utility damages presents opportunities to reduce future damages. The analyses presented in this paper are the result of a survey completed by 477 excavators who experienced the current damage prevention efforts. Data on 11,160 damages in the state of North Carolina were collected and analyzed. The key contributions of the study include, among others, recommendations to improve education material, tailoring construction firms’ plans to address locating delay and inaccuracy, and improving utility owners’ locating efforts. Accordingly, the findings can help the industry improve its efforts to reduce the damages to underground utilities and enhance the performance of construction projects.

One of the major hazards of the workplace, and in life in general, is occupational stress, which adversely affects workers’ well-being, safety, and productivity. The construction industry is one of the most stressful occupations. Current stress-assessment tools rely either on a subject’s perceived stress (e.g., stress questionnaires) or an individual’s chemical reaction to stressors (e.g., cortisol hormone). However, these methods can interrupt ongoing tasks and therefore may not be suitable for continuous measurement. To address this problem, the authors aim to develop and validate a framework for noninvasive and nonsubjective measurement of worker stress by examining changes in workers’ physiological signals collected from a wearable biosensor. The framework applies various filtering methods to reduce physiological signal noises and extracts the patterns of physiological signals as workers experience various stress levels. Then, the framework learns these patterns by applying a supervised-learning algorithm. To examine the performance of the proposed framework, the authors collected a physiological signal from 10 construction workers in the field. The proposed framework resulted in a stress-prediction accuracy of 84.48% in distinguishing between low and high stress levels and 73.28% in distinguishing among low, medium, and high stress levels. The results confirmed the potential of the proposed framework for assessing workers’ stress in the field. Automatic predictions of workers’ physical demand levels based on physiological signals is described in a companion paper. This study, along with the companion paper, contributes to the body of knowledge on the in-depth understanding of construction workers’ stress on construction sites by developing a noninvasive means for continuous monitoring and assessing workers’ stress. The proposed stress-recognition framework is expected to enhance workers’ health, safety, and productivity through early detection of occupational stressors on actual sites.

The construction industry is one of the world’s most labor-intensive industries. In it, workers are challenged almost every day by highly demanding physical tasks. Although current methods [e.g., the National Institute for Occupational Safety and Health (NIOSH)] to investigate the physical demands of various tasks provide valuable information with which to evaluate certain manual handling tasks, they may be limited to consideration of unique characteristics of each individual (e.g., physiological characteristics) and environmental conditions (e.g., ambient temperature and humidity). In other words, given the same task, different workers experience different levels of exertion. To address this problem, the objective of this research is to develop a procedure for automatic predictions of demand levels based on physiological signals collected from workers. To achieve the objective, workers’ physiological signals were captured using a wristband-type biosensor while they performed regular tasks in the field. Various physiological responses were extracted from the artifact-corrected physiological signals. The rate of energy expenditure, estimated using an energy-expenditure prediction program (EEPP), was used as a baseline to separate tasks into low-, moderate-, and high-intensity activities. Then, a supervised-machine-learning model was trained by applying a Gaussian kernel support vector machine. The results led to a prediction accuracy of 90% in recognizing low and high physical-intensity levels and 87% for low, moderate, and high physical-intensity levels. The main contribution to the body of knowledge is the development of an automatic and noninvasive method for assessing workers’ physical demands in the field. This study will contribute to improving construction workers’ productivity, safety, and general well-being through the early detection of highly physically demanding tasks in the field.

There has been insufficient research focusing on checking the reliability of construction employees’ hazard perceptions by comparing them with empirical safety data. There have also been limited studies focusing on how site employees’ perceptions could be affected by multiple external and internal factors such as workers’ experience levels. This study firstly developed a theoretical safety cognition framework addressing site employees’ perceptions toward hazards. Empirical data from construction safety reports in China were collected to quantify and define eight common safety hazard or accident scenarios. Following the empirical data analysis, these eight hazards were ranked in terms of their occurrence, severity, and controllability. Based on the results of questionnaires received in China’s construction industry, site employees’ perceptions toward these eight hazards were analyzed, ranked, and compared with the empirical ranking. Major findings included but were not limited to: (1) hazards with higher occurrences tended to be perceived with less deviation; (2) more experienced employees were more likely to apply prior scenarios and safety knowledge to perceiving given hazards and to have more reliable perceptions; and (3) prior scenarios might also create biased perceptions in the case of electrocution. The current study contributed to the knowledge in safety climate by proposing and testing the framework incorporating safety perceptions. Further research could be performed to explore more subgroup factors’ effects on workers’ perceptions, as well as how to design an effective safety training program to correct their biased perceptions.

The Belt and Road Initiative (BRI) is an ambitious economic project proposed by the Chinese government that focuses on establishing connectivity, strengthening cooperation, and creating closer economic ties among countries. The idea of BRI is based on the revival of the ancient Silk Road, a trade route that was once connecting China with Central Asia, India, Middle East, Western Asia, and Europe. More than 1,700 infrastructure projects are planned to be implemented under BRI. Such a huge infrastructure investment brings a lot of opportunities as well as risks for nations along the Silk Road, their governments, contractors, and investors. Compared to traditional international construction projects, BRI projects are exposed to additional risks since they are geographically distributed and complex in nature and include more stakeholders. Hence, risk assessment of BRI projects is a complex task that requires an efficient tool capable of providing detailed information about critical risks. To bridge this gap, a novel method is developed and applied to the risk assessment of complex, geographically distributed, and large-scale infrastructure projects, such as BRI projects. The proposed risk assessment method integrates fuzzy matrices, fuzzy logic, and probabilistic theory into a single approach that is applied to assess risks in BRI projects for different regions, as well as regional risk and total risk. In addition, sensitivity analysis is used to optimize the proposed fuzzy logic–based risk assessment method. The key findings are as follows: (1) the main risks in BRI projects are delay in supplying materials, increases in material prices, poor quality of materials, delays in equipment delivery, increases in the costs of equipment use, and economic risk; (2) the highest risk-ranked regions in BRI projects are Central Asia and Eastern Europe, while the lowest risk-ranked region is East Asia; and (3) a weighted-average method is the optimal defuzzification method for the proposed fuzzy logic–based method. The theoretical contribution of this research is a novel risk assessment method that integrates expert judgment, fuzzy sets, fuzzy logic, fuzzy matrices, and probabilistic theory into a single approach supported by sensitivity analysis. In practical terms, this study provides significant information about risks in various regions, regional risk, and overall risk of BRI projects to contractors, investors, and other stakeholders.

Political risk is one of the most prominent dangers for International Construction Companies (ICCs) to deal with in overseas countries. Despite considerable studies on political risk management in international business, few have been focused on quantitative political risk assessment methods in international construction projects. Thus, this study aims to establish a quantitative model for assessing political risk in such projects. Through the comprehensive literature review and the pilot interviews with 10 industry experts, 36 political risk source variables were first identified. Then, a questionnaire survey was conducted with 155 respondents, and a confirmatory factor analysis (CFA) was performed to group these variables into six exogenous driver components and six endogenous vulnerability components. Subsequently, a quantitative assessment model was developed, based on the vulnerability theory and the Cannikin Law, and validated by 294 cases obtained from 45 Chinese Construction Companies (CCCs). This study incorporated the vulnerability variables and the Cannikin Law in political risk assessment and provides ICCs with a more effective approach to assess political risk using the available data from international research institutes and project systems. The application of the model can also facilitate in political risk warning and management in the international construction business. As the model proposed in this study can be applied to ICCs in different countries, the findings and outcomes of this study can contribute to the global body of knowledge of political risk management.

Postphenomenology adds to the intentional relationship between subject and object (as in phenomenology) the fundamental mediated character through technologies. The focus in postphenomenology is on understanding the different roles that technologies play in the relationships between humans and the world. This study’s objective is to assess the applicability of Ihde’s postphenomenological model of human–technology relationships when exploring the impact of digital technologies on human–technology relationships in construction processes. To accommodate the different forms of human–technology relationship, a mature digital technology was chosen as the subject of a set of case studies: radio-frequency identification (RFID). Based on a literature review and interviews, the different roles that RFID applications play in human–technology relationships are explored in terms of the four human–technology relations proposed by Ihde: embodiment, hermeneutic, alterity, and background relations. It is in the context of these relations that the impact of digital technologies in construction in general is assessed.

Using the method of stochastic actor-oriented models (SAOM) and longitudinal data on building information modeling (BIM)-based construction projects undertaken in Hong Kong during 2008–2017, this study empirically modeled how the industry-level network of project-based collaborative relationships for BIM implementation evolves over time and how this evolution is influenced by a set of micromechanisms. The results of descriptive network analysis revealed that the collaborative network became increasingly dense over time but was persistently characterized by a relatively short average path length among network nodes, a high clustering coefficient, and a core–periphery structure. The results of SAOM analysis further provide evidence that the evolution of the macronetwork structure significantly relates to the structure-based preferential attachment effect and the experience-based similarity effect operating at the microlevel. The results also suggest that the individual covariate effects related to organizational ownership type and organizational BIM experience significantly influence the dynamics of the project-based collaborative network. This study not only provides a network view of how industry organizations interact with each other in BIM implementation practices across projects, but also contributes to a deepened understanding of how the networks of project-based collaborative relationships evolve as complex adaptive systems whose dynamics are driven by a set of structure- and attribute-based effects operating at the microlevel.

This article aims to identify design-construction interface problems and their causes in design-build projects to present solutions to resolve these problems. Accordingly, through a qualitative multiple–case study approach in four Iranian design-build projects, 26 problems were identified, some of the most important of which are too much employer interference in design and construction, poor planning and scheduling, and poor decision making by employers. Knowing the causes and their causation, which are related to all project phases, can help practitioners consider them and avoid their unfavorable consequences in future projects, improve the interface integration, and make better decisions that can potentially enhance project performance. In addition, the problems were prioritized to determine the top 10 problems. Eventually, the experts recommended 10 solutions according to the most important causes and two main root causes, including poor public culture and misunderstanding of the design-build term. In conclusion, it is worth bearing in mind that this study opens a new perspective in exploring the root causes of design-construction interface problems.