LAUSR

Abstract Although industrial accident rates are gradually decreasing in Korea, the construction industry's accident rate is still higher compared with other industries. Human errors, mentally unstable workers, insufficient safety training, and safety policy affect the occurrence of construction accidents. Owing to the characteristics of this industry, occupational accident types, such as fall from height, collision with objects, rollover, and those due to falling objects, can be related to the weather data. Therefore, to reduce and prevent occupational injury, it is necessary to classify and predict occupational accident types in detail. In this study, we built a model to classify and predict occupational accident types using a random forest (RF). We extracted important factors that affect the occupational accident types at construction sites using feature importance, and we analyzed the relationship between these factors and occupational accident types. The accuracy score of the RF model was obtained as 71.3%, and we presented key construction safety factors considering the feature importance. For future research, we will collect data and develop models to predict occupational accident types in real-time. Real-time construction accident prediction research will reduce accident at construction sites.