Mining construction accident reports via unsupervised NLP and Accimap for systemic risk analysis

The mortality rate in the construction industry in China is comparatively greater than that of other industries. However, the existing research on accident texts in this field is constrained to manual analysis and natural language processing (NLP) approaches. While the former approach necessitates labor-intensive efforts, the latter is restricted by a narrow viewpoint, posing challenges to comprehensively evaluating the interrelationships of factors. This study uses a Chinese sentence model to capture factors from 159 accident reports, organize text with clustering, and use manual encoding to identify themes. The accident risk was analyzed based on Accimap. The study results show the potential of combining NLP with accident causation modeling to provide a technical solution for data-driven systemic accident analysis (SAA). The findings offer insights for controlling risks on construction sites and improving safety in the industry.