Evolutionary Process of Worker Behavior Risk in Nuclear Power Plants Under Construction Based on Multi-Source Fusion Algorithm: A Case Study of BN–Game–SD

Nuclear power plants (NPPs) under construction are required to meet the stringent safety standards of operational facilities while also facing the heightened risk characteristics of construction projects. The combination of dense worker populations and generally low safety awareness presents serious challenges for ensuring construction safety. To address this, the present study proposes a BN–Game–SD multi-algorithm fusion model that systematically examines the evolution of behavioral risks from both group and individual perspectives. First, a behavioral indicator system was constructed using Bayesian Networks (BNs) to identify key risk factors. Then, a dynamic payoff matrix game model was introduced to analyze the incentive mechanisms between individuals and groups. Finally, a BN–Game–SD model was developed to capture the dynamic evolution of worker behaviors in NPP construction. Simulation results reveal that, under fixed probabilities of safety strategy selection, clear thresholds exist in group resistance to individual behavioral deviation and vice versa. Applied to a real NPP construction site, the model helped achieve a 10.39% reduction in safety violations. This study provides a theoretical foundation for promoting self-organized safety behavior evolution in nuclear enterprises and presents an innovative methodological framework for safety management in nuclear engineering.