Normative Visual Patterns for Hazard Recognition: A Crisp-Set Qualitative Comparative Analysis Approach

Understanding the mental representations used for hazard recognition would help the development of inspection strategies for effective safety management. This study is part of ongoing research to identify hazard recognition patterns based on users’ mental representations. Hence, this study explored normative visual patterns for improving hazard recognition performance using a crisp-set qualitative comparative analysis (cs-QCA). Eye-tracking data and visual trajectories were collected using an eye-tracking device in a structural laboratory. A cs-QCA approach was adopted to analyze and summarize normative visual patterns that were used to successfully detect hazards by all participants, namely, potential electrical contact, a large machine with no guardrails, and steel bars dump. The results show that object identification should suffice as the basis for identifying electricity-related hazards, while struck-by hazards should focus on the objects and their pivot points or potential movement trajectories. The experimental design and analytical approach provide new insights into visual analytics in hazard recognition. The research extends and supplements recognition by component theory in the context of construction hazard recognition. The results also provide new and practical references for hazard inspection training, as well as for future development of automated hazard recognition systems.