Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
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Association Rules Analysis of Safe Accidents Caused by Falling Objects

낙하물에 기인한 안전사고의 연관규칙 분석

  • Son, Ki-Young (School of Architectural Engineering, University of Ulsan) ;
  • Ryu, Han-Guk (Department of Architecture, Sahm Yook University)
  • Received : 2019.05.09
  • Accepted : 2019.07.10
  • Published : 2019.08.20
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Abstract

Construction industry is one of the most dangerous industry. As the construction accidents occur due to the repeated factors found in each accidents, there is a limitation in analyzing all types of occupational accidents by the existing descriptive analysis and statistical test. In this study, we classified safety accidents caused by falling objects among the accident types occurring at construction sites into fatal and nonfatal accidents and deduced the factors. In addition, we deduced the association rules among the safety accidents factors caused by falling objects through the association rule analysis method among the machine learning techniques. Therefore, considering the association rules for fatal and nonfatal accidents proposed in this study, it would be possible to prevent accidents by searching for countermeasures against safety accidents caused by falling objects.

건설업은 전체 산업 중에서 가장 많은 재해자를 발생시키는 산업 분야이다. 각 재해에서 발견되는 반복되는 요인들로 인해 재해가 발생하기 때문에 기존의 기술통계 분석 및 통계적 검정으로 업무상 재해 유형을 분석하는 데 한계가 있다. 이에 본 연구는 건설현장에서 발생하는 재해 유형 중 낙하물에 기인한 안전사고에 대하여 사망과 부상 사고로 구분하여 사고 원인들을 도출한다. 또한, 기계학습 기법 중 연관 규칙 분석 방법을 통하여 낙하물에 기인한 안전사고의 규칙을 발견하고, 낙하물의 요인들을 군집하여 중점 재해요인을 도출한다. 본 연구에서 제안한 낙하물에 기인한 사망과 부상 사고에 대한 규칙을 감안하여 낙하물에 기인한 안전사고에 대한 대처방안을 모색하면 보다 정확한 사고예방이 가능할 것으로 판단된다.

Keywords

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Figure 1. Methodology

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Figure 2. Data representation for the model

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Figure 3. Frequent words

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Figure 4. Scatter plot for association rules

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Figure 5. A dendrogram example of nonfatal and fatal accidents grouping caused by fall objects

Table 1. Data outline

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Table 2. Result of association rules based on fatal

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Table 3. Result of association rules based on nonfatal

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Table 4. Summary of hierarchical clustering result based on the input variables

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