Peilun Yu

Confined space operations are characterized by environmental complexity and latent hazards, where failures in human risk perception represent a primary precursor to industrial accidents, posing a significant challenge to sustainable occupational health and safety (OHS) management. This study investigates the mechanism by which individual traits (risk propensity) influence risk perception performance through cognitive processes (inattentional blindness). Utilizing psychological scales and eye-tracking technology, we quantitatively analyzed the visual search behaviors of participants with varying risk propensities across typical confined space hazard scenarios. The results indicate that individuals with high risk propensity tend to adopt a “random-exploratory superficial scanning strategy,” characterized by significantly delayed Time to First Fixation (TFF) and lower Fixation Count (FC) within critical hazard areas compared to the low-risk propensity group. Statistical analysis reveals that inattentional blindness exerts a full mediating effect between risk propensity and risk perception performance, accounting for 72.56% of perception failures. This research confirms that an imbalance in attentional resource allocation leads to higher cognitive omission of salient hazards among high-risk propensity individuals. These findings provide a theoretical foundation for cognitive reliability assessment and the design of sustainable safety training programs in high-risk industries, ultimately contributing to the social sustainability and well-being of industrial workforces.

The initial firefighting capacity of complex commercial areas depends on the service level and the location of micro firefighting facilities. In response to the issue of coverage by micro firefighting facilities in complex commercial areas, a graded progressive coverage model is established. This model includes distance-progressive coverage and firefighting service level attenuation coverage. The former integrates fairness and efficiency in fire planning, while the latter considers the adaptability between the demand points and the fire service levels. The objectives include maximizing the coverage matching degree, effective coverage rate, medium- and high-risk coverage rate, and overall coverage rate of the fire service point and the demand points. A genetic algorithm is designed to solve the model, where the construction level and the number of micro fire stations are varied to analyze changes in various indicators. Central Street, characterized by complex buildings and high pedestrian traffic, is selected as a case for the experiment. The results show that simply adjusting the construction level of micro fire stations increases the effective coverage rate by 2.5%. The graded progressive coverage model shows a turning point in the effective coverage rate and the overall coverage rate when the number of new micro fire stations is 14 or 10, with coverage rates of 76.1% and 93.2%, respectively. The maximum progressive coverage model shows a turning point in the overall coverage rate when 9 new stations are added, which is 10.5% lower than that of the graded coverage model; when 10 stations are added, the overall coverage rate is 11.2% lower. These findings demonstrate the effectiveness of the graded progressive coverage model.