A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Mining construction accident reports via unsupervised NLP and Accimap for systemic risk analysis
Abstract 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.
Highlights We compute sentence vector similarity from multi-level clustering results. We cluster 3539 factor sentences from 159 incident reports. We identify the themes of the accident factor clusters. We analyze accident factors with Accimap and provide several managerial insights.
Mining construction accident reports via unsupervised NLP and Accimap for systemic risk analysis
Abstract 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.
Highlights We compute sentence vector similarity from multi-level clustering results. We cluster 3539 factor sentences from 159 incident reports. We identify the themes of the accident factor clusters. We analyze accident factors with Accimap and provide several managerial insights.
Mining construction accident reports via unsupervised NLP and Accimap for systemic risk analysis
Ma, Zheng (author) / Chen, Zhen-Song (author)
2024-02-23
Article (Journal)
Electronic Resource
English
An improved text mining approach to extract safety risk factors from construction accident reports
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