The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

1. 서 론
2. 연구 방법
    2.1 데이터 수집 및 전처리
    2.2 데이터 분석
3. 연구 결과
    3.1 건설사고 현황 분석
    3.2 통계 기반 건설업 사망사고 요인분석
    3.3 기계학습 기반 건설업 사망사고 요인분석
4. 결론
    4.1 요약
    4.2 연구의 한계 및 향후 연구과제

• 최지윤(울산대학교 산업경영공학부) | Jiyoon Choi (Department of Industrial Engineering, University of Ulsan)
• 김시현(울산대학교 산업경영공학부) | Sihyeon Kim (Department of Industrial Engineering, University of Ulsan)
• 이송이(울산대학교 산업경영공학부) | Songe Lee (Department of Industrial Engineering, University of Ulsan)
• 김경훈(한국산업안전보건공단) | Kyunghun Kim (Korea Occupational Safety and Health Agency)
• 이수동(울산대학교 산업경영공학부) | Sudong Lee (Department of Industrial Engineering, University of Ulsan) Corresponding Author