A substantial proportion of construction accidents is associated with unsafe worker behavior. Identifying their underlying mechanism is vital for designing effective interventions. As prior studies could not capture complex nonlinear interactions among organizational and individual factors, this study leverages machine learning (ML) techniques, which can capture complex relationships by handling large datasets, and can identify patterns in worker behavior. The study proposes an explainable ML model to interpret key determinants of safe behavior. The data were collected from 425 construction workers in Saudi Arabia. Multiple ensemble and benchmark ML algorithms—including random forest (RF), categorical boosting, decision jungle, light gradient boosting machine, support vector machine, and adaptive boosting—were implemented and compared. The results indicate that the RF model achieved the best predictive performance, outperforming several competing models. To enhance the model’s interpretability, explainable artificial intelligence (XAI) techniques were applied to reveal the interaction of key predictors influencing workers’ behaviors. The results demonstrate that safety communication, risk perception, and supportive work environment are the most influential determinants shaping safety behavior. As a key novelty, this study introduces an ML-based approach for predicting construction workers’ safety behavior and applies XAI techniques to systematically interpret the key determinants of safety behavior. The results also provide valuable insights for safety managers and offer data-driven guidance to enhance the effectiveness of safety interventions.
Wahan et al. (Fri,) studied this question.