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http://dx.doi.org/10.14346/JKOSOS.2022.37.2.18

Text mining-based Data Preprocessing and Accident Type Analysis for Construction Accident Analysis  

Yoon, Young Geun (Department of Safety Engineering, Incheon National University)
Lee, Jae Yun (Department of Safety Engineering, Incheon National University)
Oh, Tae Keun (Department of Safety Engineering, Incheon National University)
Publication Information
Journal of the Korean Society of Safety / v.37, no.2, 2022 , pp. 18-27 More about this Journal
Abstract
Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.
Keywords
construction accident; latent class cluster analysis; predictor importance; data preprocessing; alluvial flow diagram;
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Times Cited By KSCI : 4  (Citation Analysis)
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