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Given that roofing contractors in the construction industry have the highest fatality rate among specialty contractors, understanding the root cause of incidents among roofers is critical for improving safety outcomes. This study applied frequency analysis and decision tree data-mining techniques to analyze roofers’ fatal and non-fatal accident reports. The frequency analysis yielded insights into the leading cause of accidents, with fall to a lower level (83%) being the highest, followed by incidence sources relating to structures and surfaces (56%). The most common injuries experienced by roofing contractors were fractures (49%) and concussions (15%), especially for events occurring in residential buildings, maintenance and repair works, small projects (i.e., $50,000 or less), and on Mondays. According to the decision tree analysis, the most important factor for determining the nature of the injury is the nonfragile injured body part, followed by injury caused by coating works. The decision tree also produced decision rules that provide an easy interpretation of the underlying association between the factors leading to incidents. The decision tree models developed in this study can be used to predict the nature of potential injuries for strategically selecting the most effective injury-prevention strategies. 

Given the dynamic and complex nature of the construction industry, maintaining situation awareness at job sites is critical. To react properly, workers must identify dynamic safety hazards within the scene. The majority of studies assessing construction workers’ situation awareness have utilized static images, virtual reality, and other types of simulation methods, but questions remain as to whether these formats are able to capture and monitor workers’ naturalistic behaviors and hazard identification abilities. To identify whether the format of hazardous stimuli (i.e., static, image-based vs. dynamic, and video-based formats) impact workers’ subjective and objective hazard identification and situation awareness metrics, this study developed 23 safety hazard scenarios utilizing state-of-the-art augmented 360° panoramas and then tracked differences in workers’ visual search patterns and hazard identification abilities using eye-tracking technology. The workers’ cognitive responses, evidenced by their eye movements, showed that workers had significantly varied cognitive processes and abilities depending on the format of stimuli: Workers with lower hazard identification skills were more likely to miss hazards in a dynamic environment. This result suggests that the experimental setting should be carefully designed to determine construction workers’ natural cognitive process.