An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks

The interactions of external disruptions and technical-human-organizational factors in emergency operations are usually observed. Resilience assessment of emergency systems can improve emergency response capability and system functional recovery. The increasing complexity and coupling of factors in emergency response systems need to be investigated from a system resilience perspective. In this paper, we propose to integrate a multi-stage System-Theoretic Accident Model and Processes (STAMP) with a dynamic Bayesian network (DBN) for the resilience assessment of emergency response systems. In the proposed methodology, emergency response systems are viewed as multi-step emergency operations for STAMP to analyze the hierarchical control and feedback structures. The output of multi-stage STAMP in controllers, actuators, sensors, and controlled processes is applied to develop a DBN for resilience assessment. For known external shocks (e.g., natural disasters), the effects of external shocks on the system are decomposed into subsystems or components. System degradation and recovery models are established. Regarding unknown external disruption (e.g., unforeseen failure modes), degeneration and recovery are temporally integrated into the analysis of system functionality. System performance is evaluated through the combination of socio-technical factors and external disasters. Eventually, the resilience of emergency response systems is obtained from the performance curves. The results demonstrate that the proposed model can evaluate system resilience after the system suffers from external disasters.