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http://dx.doi.org/10.14346/JKOSOS.2022.37.6.40

Analysis of Characteristic Factors for Non-fatal Accidents in Construction Projects using Association Rule Mining  

Gayeon, Lee (Daegu-Gyeongbuk Regional Headquarter, National Health Insurance Service)
Sung Woo, Shin (Department of Safety Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Safety / v.37, no.6, 2022 , pp. 40-49 More about this Journal
Abstract
Simple statistical frequency based analysis, such as Pareto analysis, are widely used in conventional accident analysis. However, due to the dynamic and complex nature of construction works, many factors can simultaneously affect or involve the occurrence of accidents in construction projects. Therefore, the identification of the complex relationship between such factors is important to establish relevant and effective safety management policies and/or programs. In this study, characteristic factors and their relationships' contribution to non-fatal accidents in construction projects are analyzed using the association rule mining (ARM) technique. To this end, a total of 59,202 construction accident data are collected from 2015 to 2019 and the ARM is performed to retrieve specific relationships -named as association rules-among classified factors in the data. Characteristics of the retrieved relationships are analyzed and compared with the results of conventional Pareto analysis. Based on the results, it is found that both fall and trip are notable accident forms having characteristic relations with other factors for non-fatal accidents in construction projects. It is also found that small-scale construction, age of 50s, less than 1 month of working period, and architectural construction are important factors for non-fatal accidents in construction projects.
Keywords
accident analysis; association rule mining; non-fatal accident; construction safety;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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