한국안전학회지 (Journal of the Korean Society of Safety)

  • 제36권3호
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  • Pages.31-39
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  • 2021
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  • 1738-3803(pISSN)
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  • 2383-9953(eISSN)
한국안전학회 (The Korean Society of Safety)
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건설현장의 공사사전정보를 활용한 사망재해 예측 모델 개발

Development of Prediction Models for Fatal Accidents using Proactive Information in Construction Sites

  • 최승주 (울산대학교 안전보건전문학과) ;
  • 김진현 (한국산업안전보건공단 산업안전보건연구원) ;
  • 정기효 (울산대학교 산업경영공학부)
  • Choi, Seung Ju (Department of Safety Engineering, University of Ulsan) ;
  • Kim, Jin Hyun (Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency) ;
  • Jung, Kihyo (School of Industrial Engineering, University of Ulsan)
  • 투고 : 2021.01.15
  • 심사 : 2021.04.19
  • 발행 : 2021.06.30
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초록

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; NRF-2019R1A2C4070310).

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