Research on Dynamic Simulation of High Performance Concrete Supply Chain Security Based on Hybrid Fuzzy Bayesian Intelligent Optimization Design

In response to the problem of difficulty in accurately quantifying multi-source fuzzy risk information and unclear dynamic mechanisms of safety evolution in the supply chain of high-performance concrete (HPC), this paper constructs a hybrid fuzzy Bayesian intelligent optimization model coupled with a system dynamics simulation framework. Relying on fuzzy theory to quantify supply chain risk uncertainty information, introducing adaptive particle swarm optimization algorithm to optimize model parameters. Implementing dynamic inference of risk probability through fuzzy Bayesian analysis; Divide the four subsystems of raw material supply, production and processing, logistics distribution, and engineering construction, build a stock flow dynamics simulation model, and conduct numerical experiments in multiple risk disturbance scenarios. The results indicate that the supply chain system has good single risk self-healing ability, but multiple risk coupling disturbances can produce significant amplification effects, greatly reducing the security resilience of the supply chain. Sensitivity analysis confirms that the stability of raw material supply and logistics transportation efficiency are the core sensitive factors affecting supply chain security and the key focus of engineering control. The research can provide intelligent simulation theory and decision support for risk prediction and dynamic safety control of high-performance concrete supply chain.