Purpose: This study aims to show what interventions in human factors can effectively reduce construction workers' unsafe behavior.Design/methodology/approach: A diagnostic intervention model targeted the construction workers' weakest internal factors. The workers' behavior and cognition data were collected via a questionnaire and a video camera system from two medium-sized construction sites. A safety supervisor accompanied each site supervisor to improve construction workers' internal factors by implementing the designed intervention measures.Findings: The statistical analysis results confirmed a persistent positive effect on construction workers' safe behavior by improving internal factors. Among the intervention programs applied, those aimed to improve the subjective norms, safety knowledge and attitudes had the most significant effect sizes.Practical implications: The findings of this case study advise project managers to design a specific behavioral intervention that aims at improving construction workers' significant internal factors, including subjective norms, safety attitudes, habits and knowledge together with demographic characteristics to reduce construction workers' unsafe behavior.Originality/value: While the declining rate of construction accidents approaches an asymptote which is still high, this study suggests that targeting the individual internal factors through diagnostic interventions is the key to further reduce the rate by improving construction workers' behavior.

Addressing safety risks in construction is an ongoing priority, and integrating safety considerations into construction scheduling is a crucial aspect of this effort. A notable challenge is the safety risk posed by concurrent tasks, which has received limited attention in prior research. This study aims to address this research gap by introducing a novel Building Information Modeling (BIM)-based model that assesses the increased hazardousness resulting from overlapping construction activities. Historically, research has predominantly focused on individual task safety, with less emphasis on the risks associated with overlapping activities. Our innovative approach introduces the concept of a ‘source–target’ match, which evaluates the degree of hazardousness escalation when activities overlap. Drawing on data from the Occupational Safety and Health Administration (OSHA) and the National Institute for Occupational Safety and Health (NIOSH) fatal accident reports, we extracted 11 hazardous and 9 susceptibility attributes to build a source–target match table. This table reveals the characteristics of activities that generate hazardous conflicts when overlapping. The key contribution of this research is the assessment, prioritization, and visualization of risk levels in a BIM environment. This framework empowers safety managers to proactively address safety risks resulting from overlapping construction activities, ultimately reducing accidents in the construction industry. By shedding light on this overlooked aspect of construction safety, our research highlights the importance of integrating safety considerations into construction scheduling and provides a practical tool for mitigating risks, enhancing workplace safety, and ultimately improving project outcomes.

The immediate cause of construction accidents is attributed to human error. However, few studies have investigated the relationship between individual internal factors and unsafe behaviors in construction sites. This study aimed to develop and test a model to understand the mechanism of unsafe behaviors based on individual characteristics and the Theory of Planned Behavior (TPB) constructs. To test the hypotheses, structural equation modeling, and hierarchical regression analysis were utilized. A total of 191 workers were evaluated in five construction sites to explore the effects of each internal factor. The findings showed that the TPB constructs fully mediated the relationship between individual background characteristics and unsafe behaviors. Furthermore, the analysis of the moderating effects of the variable of habits showed that the inverse relationship between safety attitude and unsafe behaviors was stronger when there was a high level of safety habits. This study also found that safety attitude and safety habits were the best predictors for the behaviors of the workers. The findings of this study provide deeper insights for both researchers and practitioners by identifying how internal factors influence the behavior to prevent the workers’ unsafe behaviors.