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.@en

Traditional risk assessment approaches mainly focus on the pre-failure scenarios with certain information. For complex systems, the scope of risk assessment needs to be extended to include the post-failure phase; because the emerging hazards of these systems cannot be wholly identified and are usually highly uncertain. Thus, resilience assessment needs to be investigated. Most of the existing literature quantify resilience based on a system's performance loss caused by disruptions. These studies fail to assess the probability of a system to sustain or restore to a normal operational state after disruptions occur, how this probability changes with time, and how fast the system can be restored. The dynamic and probabilistic characteristics of resilience must be considered in systemic resilience assessment, in which the engineered system, human and organizational factors, and external disruptions are considered. This paper aims to develop a dynamic Bayesian network (DBN)-based approach to the probabilistic assessment of the system resilience by incorporating temporal processes of adaption and recovery into the analysis of system functionality. The proposed method also provides a new way to define resilience in terms of the probability of system functionality change during and after a disruption. A case study on the Chevron refinery accident is used to demonstrate the applicability of the proposed methodology.@en