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


Vol. 37,  No. 2, pp. 18-27, Apr.  2022
10.14346/JKOSOS.2022.37.2.3.18


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  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.

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  Cite this article

[IEEE Style]

윤영근, 이재윤, 오태근, "Text mining-based Data Preprocessing and Accident Type Analysis for Construction Accident Analysis," Journal of the Korean Society of Safety, vol. 37, no. 2, pp. 18-27, 2022. DOI: 10.14346/JKOSOS.2022.37.2.3.18.

[ACM Style]

윤영근, 이재윤, and 오태근. 2022. Text mining-based Data Preprocessing and Accident Type Analysis for Construction Accident Analysis. Journal of the Korean Society of Safety, 37, 2, (2022), 18-27. DOI: 10.14346/JKOSOS.2022.37.2.3.18.

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