ScholarWorks@경상국립대학교

초록

Background and objective: The objective of this study was to develop an intuitive accident prediction model that can bereadily applied at construction sites to effectively reduce the incidence of accidents through the analysis of constructionaccident data. Methods: These accidents significantly contribute to the construction industry's overall accident rate. Accident types werecategorized into fatalities, injuries, and material damages to construct the accident prediction model. A total of 24 factorswere considered across eight major variables, which were identified during the first and second phases of datapreprocessing to analyze construction accident big data. Machine learning techniques were employed, specifically supervised learning and ensemble learning, to identify the optimal predictive model. Results: Among the models tested, XGBoost emerged as the most effective due to its highly balanced accuracy, even in the presence of class imbalance. Conclusion: The implementation of the XGBoost accident prediction model, along with the feature importance codes developed in this study, enables the prediction of accident types for specific tasks performed at construction sites. This predictive capability is expected to inform the implementation of targeted accident prevention measures, such as enhancing safety protocols or adjusting work procedures based on the prediction outcomes.

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