Construction workers’ emotional states (e.g., pleasure, displeasure, excitement, and relaxation) are known as a critical factor that affect their performance (e.g., safety, health, and productivity). To prevent adverse impacts on work performance, measuring emotional states should take precedence to better understand how workers’ emotions vary while they are working. Among many methods available to measure emotional states, electroencephalogram (EEG) has a great potential for quantitative measurement by overcoming a possible bias from the survey-based subjective assessment of emotions. Although EEG-based emotion measurement has been tested and applied only in a laboratory environment, recent advancements in wearable EEG sensors, which are portable, wireless, and affordable, open a new door toward nonintrusive field emotion measurement. This study thus investigates the feasibility of measuring workers’ emotions in the field using a wearable EEG sensor. To do this, a bipolar dimensional emotion model, which consists of valence (from displeasure to pleasure) and arousal (from relaxation to excitement) dimensions, was applied to quantify workers’ emotional states. Then, workers’ valence and arousal levels were measured using a wearable EEG sensor during their ongoing tasks. The validity of the EEG-based emotion measurement was examined through a comparison with cortisol levels obtained from workers’ saliva samples, which has been accepted as a reliable physical measure of emotions. The results demonstrate the applicability of a wearable EEG sensor for measuring workers’ emotions, particularly valence levels, which remain crucial to understanding workers’ emotional states. This study contributes to the body of knowledge on in-depth studies for understanding workers’ emotions in the field by providing a means to continuously and nonintrusively measure workers’ emotions while they are working.

Workers’ unsafe behaviors have a substantial impact on construction safety. To regulate workers’ unsafe behavior, construction practitioners have mainly used formal controls (e.g., penalties). However, the formal approaches may not be effective at eliciting desired behavioral changes in improving safety behavior. Therefore, recently, researchers have paid more attention to how unsafe behaviors are produced. In this regard, cognitive models of safety behavior and empirical evidence on social influence have been suggested. However, there is a noticeable paucity of research investigating the mechanism behind the link between cognitive process, social influence, site risk, and safety behavior. In this paper, an empirically based agent-based model that incorporates theoretical and empirical findings of the sociocognitive process of workers’ safety behaviors is developed. The model is used to conduct experiments examining how the sociocognitive process interacts with safety management interventions (i.e., strictness and frequency of management feedback, and project identification) and influences workers’ safety behaviors across different site risk conditions (i.e., low-, modest-, and high-risk conditions). The results demonstrated that all three interventions contribute to decreasing the incident rate. Also, the interaction effects of the interventions in different site risk conditions were found using the parameter sweeping. The results indicated that (1) promoting workers’ project identification would be an effective strategy in the modest-risk site condition; (2) other interventions should be combined after achieving the medium strictness of management feedback in the high-risk site condition; and (3) other interventions would not be effective without very strict management feedback in the low-risk site condition. This paper contributes to the body of knowledge on construction safety by extending the authors’ understanding of the role of sociocognitive process and its interaction with the environment in shaping workers’ safety behaviors. Additionally, the experiment results are expected to lay a strong foundation for developing effective safety management interventions in the construction projects.

Each participant in a building construction project requires a dedicated work space in which to execute their activities. In this environment, inappropriate work-space planning in a construction site causes work-space problems, which results in a loss of productivity, safety hazards, and issues of poor quality. Therefore, the work space should be considered one of the most important resources and constraints to manage at a construction site. However, current construction planning techniques have proven to be insufficient for work-space planning because they do not account for the spatial feature of each activity. To establish a formalized work-space planning process, therefore, this paper categorizes work space by its function and movability and suggests a framework for a work-space planning process that contains five phases, including 4D building information model (BIM) generation, work-space requirement identification, work-space occupation representation, work-space problem identification, and work-space problem resolution. The proposed framework in this paper can improve the accuracy of work-space status representation and work-space problem identification by introducing the work-space occupation concept and the integrated work-space planning process that considers characteristics of activity, work space, and construction plan. In addition, this paper aims to ameliorate the work-space planning process through path analysis and a formalized work-space problem resolution process. To validate the proposed approach, a case project was tested. The result shows the efficiency and effectiveness of the proposed framework on improving the work-space planning process. Based on the result of this study, a project manager will be able to prevent possible work-space problems and their negative effects on project performance by devising a pertinent work-space plan during the preconstruction phase.