How to Prevent Construction Safety Accidents? Exploring Critical Factors with Systems Thinking and Bayesian Networks

Construction safety remains a critical concern, with frequent accidents leading to fatalities, severe injuries, and significant economic losses. To address these challenges and enhance accident prevention, this study adopts a systems thinking approach to investigate the causal factors of construction safety accidents. First, drawing on Rasmussen’s risk management framework, this study developed a Construction Accident Causation System (CACS) model that comprises six hierarchical levels and 23 influencing factors. Through the analysis of 331 investigation reports of construction accidents in China, causal factor correlations were refined, and the topological structure and network parameters of the model were determined. This study integrates diagnostic reasoning, sensitivity analysis, and fuzzy mathematics within a Bayesian Network (BN) framework. Through this approach, it identifies the most probable accident pathways and highlights seven critical and three sensitive factors that jointly exacerbate construction safety risks. A real-world case of a formwork collapse in Baotou City is further analyzed to verify the model’s reliability and practical relevance. The results confirm that the integrated CACS and BN framework effectively captures the multi-level interactions among managerial, behavioral, and technical factors, providing a scientific basis for proactive safety management and accident prevention in the construction industry.