한국건설관리학회논문집 (Korean Journal of Construction Engineering and Management)

  • 제22권5호
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  • Pages.73-85
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  • 2021
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  • 2005-6095(pISSN)
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  • 2465-9703(eISSN)
한국건설관리학회 (Korea Institute of Construction Engineering and Management)
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딥러닝 기반 분류 모델의 성능 분석을 통한 건설 재해사례 텍스트 데이터의 효율적 관리방향 제안

A Suggestion of the Direction of Construction Disaster Document Management through Text Data Classification Model based on Deep Learning

  • 김하영 (이화여자대학교 건축도시시스템공학과) ;
  • 장예은 (이화여자대학교 건축도시시스템공학과) ;
  • 강현빈 (이화여자대학교 건축도시시스템공학과) ;
  • 손정욱 (이화여자대학교 건축도시시스템공학과) ;
  • 이준성 (이화여자대학교 건축도시시스템공학과)
  • Kim, Hayoung (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Jang, YeEun (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Kang, HyunBin (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Son, JeongWook (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Yi, June-Seong (Department of Architectural and Urban Systems Engineering, Ewha Womans University)
  • 투고 : 2021.04.09
  • 심사 : 2021.09.02
  • 발행 : 2021.09.30
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초록

본 연구는 딥러닝 기반의 텍스트 데이터 분류 모델의 성능 고찰을 통해 한국어 건설 재해사례의 효율적 관리방향을 제안한다. 이를 위해 비정형 텍스트 문서인 건설 재해 보고서를 활용해 건설 사고의 대표적 유형인 추락, 감전, 낙하, 붕괴, 협착의 5개 범주로 분류하는 딥러닝 모델을 구현하였다. 초기 모델 테스트 결과, 추락 재해의 분류 정확도가 상대적으로 높게 도출되며 타 유형을 추락 재해로 분류하는 경우가 많이 발생한다는 특징이 나타났다. 원인 분석 결과, 1) 구체적인 사고 유발 행동, 2) 유사한 문장 구조, 3) 여러 유형에 해당되는 복합사고가 위의 특징에 영향을 미치는 것으로 분석되었으며, 이 중 추가 실험을 통해 검증이 가능한 복합사고에 대한 두 가지 정확도 개선 실험을 진행하였다: 1) 재분류, 2) 제외. 실험 결과, 복합사고 제외 시 분류 성능이 185.7% 향상되었으며, 이를 통해 여러 사고 유형에 대한 내용을 동시에 포함하는 복합사고의 다중공선성(multicollinearity)이 해소되었음을 알 수 있다. 결론적으로 본 연구에서는 향후 사고에 대한 상황을 상세히 서술하는 체계를 마련함과 동시에 복합사고를 독립적으로 관리할 필요성을 시사한다.

This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail.

키워드

과제정보

이 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 21CTAP-C152263-03).

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