| description abstract | The significance of accident prevention in the construction industry has been consistently emphasized. Despite the research into various preventive methodologies, their application in actual construction sites still needs to be improved. Therefore, it is necessary to develop accident prevention methodologies and make improvements so that practitioners can utilize them in actual construction sites. This study sets out to propose methods for accident prevention using construction accident data and to develop a system that construction practitioners can easily utilize. Firstly, a methodology was developed to assess the risk of construction sites by considering site-specific characteristics through actual construction accident data. This led to the identification of five principal risk factors (i.e., construction type, facility type, ordering organization, construction cost, and safety management plan), and the results of risk assessment for different site classifications were derived by combining these factors. Then, machine-learning models were developed to predict accident-causing objects, accident types, and injury-death using four algorithms: random forest, light gradient boosting model, eXtreme gradient boosting (XGBoost), and categorical boosting. As a result, XGBoost demonstrated the most outstanding predictive performance, with averaged F1 scores of 0.839, 0.749, and 0.977, respectively. A web-based prototype was also developed to deploy the proposed methods, confirming the practical utility of the accident prevention strategies outlined in this research. These findings have the potential to improve the efficacy of site management by enabling proactive identification of high-risk areas, thereby promoting more effective accident prevention initiatives. | |
