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http://dx.doi.org/10.5345/JKIBC.2021.21.2.165

Analysis of the Feature Importance of Occupational Accidents Occurring at Construction Sites on the Severity of Lost Workdays  

Kang, Kyung-Su (Construction Engineering and Management Institute, Sahmyook University)
Choi, Jae-Hyun (School of Architecturral Engineering, Korea University of Technology and Education (Koreatech))
Ryu, Han-Guk (Department of Architectural, Sahmyook University)
Publication Information
Journal of the Korea Institute of Building Construction / v.21, no.2, 2021 , pp. 165-174 More about this Journal
Abstract
The construction industry causes the most accidents and fatalities among all industries. Although many efforts have been made to reduce safety accidents in construction, the study on the lost workdays that return to work place is insufficient. Therefore, this study proposes a model that classifies the lost workdays lost into moderate and severity, and derives the importance of variable and analyzes important factors through the trained random forest model. We analyze the learning process of the random forest which is a black box model, and extracted important variables that impact on the severity of the lost workdays through the extracted feature importance. The factors existing inside were analyzed through the extracted variables. The purpose of this study is to analyze the accident case data at the construction site through a random forest model and to review variables that have a high impact on the lost workdays. In the future, this sutdy can apply to improve construction safety management and reduce the accident of industrial accidents.
Keywords
construction safety; machine learning; random forest; feature importance;
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