Coupling of STAMP and CFPM Models and Their Application in Dynamic Risk Evolution of Emergency Systems

To address the challenges in risk assessment of complex emergency systems, such as difficulties in closed-loop structure modeling, insufficient quantification of dynamic evolution, and poor adaptability to multiple scenarios, this study proposes a dynamic risk assessment method that integrates the System-Theoretic Accident Model and Processes (STAMP) and the Cascading Failure Propagation Model (CFPM). The novelty of this coupling lies in a bidirectional “qualitative diagnosis → quantitative prediction” logic: STAMP’s identification of Unsafe Control Actions (UCAs) provides a theory-grounded blueprint for configuring the CFPM network topology and propagation parameters, while CFPM’s dynamic simulation translates these qualitative control flaws into computable risk evolution trajectories. The proposed framework adopts a two-layer structure of “qualitative modeling–quantitative analysis”. STAMP is used to construct a hierarchical control structure, identify Unsafe Control Actions (UCAs), and analyze the nonlinear interaction mechanisms among “human–organization–technology” factors. For typical scenarios of “fault not processed” and “online fault processing”, CFPM is employed to abstract the system into a node network, quantify the time-step propagation process of node failure probability, calculate the system residual performance index, and generate real-time risk evolution curves. A case study of the Tianjin Port ‘8·12’ explosion accident demonstrates that this method effectively captures the closed-loop interaction characteristics and dynamic risk evolution patterns of emergency systems. Quantitative results reveal a distinct contrast between the two handling scenarios: in the absence of maintenance intervention, system residual performance deteriorates exponentially and rapidly approaches a critical threshold; in contrast, effective online maintenance significantly retards risk accumulation and facilitates gradual system recovery, thereby preventing further escalation of consequences. Compared to traditional methods like Bayesian Networks, it shows stronger applicability by explicitly modeling closed-loop feedback structures and enabling discrete time-step quantification of risk accumulation, and can accurately identify control flaws and quantify risk accumulation effects, thereby providing support for optimizing emergency strategies. Future research should focus on enhancing the method’s adaptability to data uncertainty and cybersecurity threats.