국제학술발표논문집 (International conference on construction engineering and project management)

한국건설관리학회 (Korea Institute of Construction Engineering and Management)
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Data mining approach for identifying factors impacting construction accident costs: from indirect expenses perspectives

  • Ayesha Munira CHOWDHURY (ICT Integrated Safety Ocean Smart Cities Engineering Department, Dong-A University) ;
  • Eun-Ju HA (Department of Electronics, Dong-A University) ;
  • Jae-ho CHOI (ICT Integrated Safety Ocean Smart Cities Engineering Department, Dong-A University)
  • 발행 : 2024.07.29
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초록

Construction projects account for a significant proportion of workplace hazards globally. While construction cost reports typically emphasize direct accident costs such as treatment expenses, nursing care costs, or disability benefits, indirect factors like work interruption loss costs or consolation costs are frequently overlooked, because it is relatively difficult to estimate those factors in advance. Recognizing and accurately estimating the indirect costs factors associated with construction accidents would not only shed light on the monetary impact these incidents have on overall project costs but also would enable to estimate the total accident cost in advance. The current study seeks to identify factors influencing indirect costs, which ultimately govern the total accident cost, through a data mining approach. A survey was conducted in domestic construction companies, resulting in a dataset of 1038 accident records collected from construction sites. First, statistical analysis was performed to uncover characteristics and patterns of factors affecting construction accident costs from both direct and indirect perspectives. Later, this study proposes four distinct machine learning (ML) models, comparing their performances in predicting the total accident cost (including indirect costs) in advance. Additionally, this research sheds light on an important issue in construction data analysis, which is the scarcity of data in a particular class, by applying random oversampling and random undersampling techniques. The suggested framework can assist practitioners and management in estimating construction accident costs and identifying the relevant attributes that impact accidents at the construction site for future practices.

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과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (National Research for Smart Construction Technology: Grant 225MIP-A156381-03), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A6A1A03012812).

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