An interpretable machine learning approach for evaluating the feature importance affecting lost workdays at construction sites

Abstract

This study investigates the intensity of accidents by considering the lost workdays based on real data and derived the feature importance quantitatively. Occupational injuries lead to lost workdays for construction workers. The term “lost workdays” refers to a period in which workers cannot perform regular jobs because of severe physical or mental damage from occupational injuries. Many studies focusing on type analysis, accident prediction, and risk management of construction accidents, combined with the intensity and frequency of accidents, have been conducted to prevent and minimize accidents. However, the existing method for measuring the intensity of occupational accidents/injuries is a qualitative method that reflects experts’ opinions, mainly using brainstorming, the Delphi method, and the analytic hierarchy process. Thus, we propose a framework that combines traditional analysis and interpretable machine learning approaches. The random forest model was trained with 11,223 injured worker samples, feature importance and frequency analysis with the chi-squared test, and local interpretable model-agnostic data. According to the results of this study, optimal safety management is possible if appropriate resources are allocated. If the risk of accidents is measured with objective and quantitative data, the prevention and analysis of accidents can be more reliable, and resources can be more efficiently allocated.