Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
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Proposal of a Prediction Framework Based on Deep Learning Algorithm to Predict Safety Accidents at Small-scale Construction Sites

소규모 건설현장의 안전사고 예측을 위한 딥러닝 알고리즘 기반의 예측프레임워크 제안

  • Kim, Ji-Myong (Department of Architectural Engineering, Mokpo National University)
  • Received : 2023.09.27
  • Accepted : 2023.11.09
  • Published : 2023.12.20
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Abstract

This study aims to develop a framework for an accident prediction model leveraging a deep neural network algorithm, specifically tailored for small-scale construction sites. Notably, the incidence of accidents in the construction sector is markedly higher compared to other industries, with a significant contribution from small-scale sites. The challenging nature of construction in urban settings, coupled with the increasing frequency of adverse weather conditions, is likely to escalate accident risks at these sites. Anticipating and mitigating accidents at small-scale construction sites is therefore crucial to decrease the overall industry accident rate. Consequently, this research introduces a Deep Neural Network-based model for forecasting accidents at small-scale construction sites. The framework and findings of this study are poised to serve as a guideline for the safety management of small-scale construction projects, ultimately aiding in the realization of safer, more sustainable construction practices at these sites.

건설산업의 재해율은 다른 산업에 비해 매우 높다. 그 이유로 다른 규모에 비해 상대적으로 더 사고에 취약한 소규모 건설현장의 높은 재해발생율을 꼽고 있다. 최근 난이도 높은 도심 건설공사의 증가, 악천후의 증가 등으로 앞으로 소규모 건설현장의 사고 발생 위험은 더 커질 것으로 예상된다. 따라서 소규모 건설현장의 사고를 사전에 예측하고 이를 통한 사고 예방 및 저감은 건설산업의 재해율을 낮추기 위해 반드시 필요하다. 따라서, 본 연구에서는 소규모 건설현장 사고를 예측하기 위한 Deep Neural Network Algorithm 기반의 사고 예측 모델 개발 프레임워크를 제안하였다. 본 연구의 프레임워크와 결과를 활용하여 소규모 건설현장 안전관리의 가이드 라인으로 활용이 가능하며, 궁극적으로 소규모 건설현장에서의 사고 위험을 줄임으로써 지속가능한 건설사업관리에 기여할 수 있을 것이다.

Keywords

Acknowledgement

This research was funded by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2022R1F1A106314112).

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