Abstract
Construction site safety is a paramount concern, given the high rate of accidents and fatalities in the sector. This study introduces a novel approach to analyzing construction accident reports by employing advanced Large Language Models (LLMs), specifically GPT-3.5, GPT-4, Gemini Pro, and LLaMA 3. Our research focuses on the classification of key attributes in accident reports: root cause, injury cause, affected body part, severity, and accident time. The results reveal that GPT-4 achieves significantly higher accuracy across most attributes. Gemini Pro demonstrates superior performance in the "Injury Cause" classification, while LLaMA 3 excels in classifying "Severity" and "Root Cause." GPT-3.5, although lagging behind GPT-4, exhibits commendable accuracy. The insights gained from this study are vital for the construction industry, as they indicate the potential for developing more precise and effective safety measures. These findings could lead to a reduction in the frequency and severity of accidents, thereby enhancing worker safety.
