LAUSR

Critical infrastructure systems (CISs) have a fundamental role in delivering commodities that are essential to various functions in urban systems. The resilience of CISs concerns the robustness of system performance against extreme events, the ineffectiveness of disturbance propagation, and the efficiency of post-disaster system performance restoration. The resilience of CISs is significantly impacted by the interconnectivity among CISs and the interactions among different systems. Although this impact has been recognized by numerous studies, it has rarely been comparatively assessed using different metrics that reflect the different perspectives of various stakeholders. Moreover, the existing literature on the impact of interdependencies in the context of CIS disaster risk reduction has primarily focused on the resistance stage rather than the entire life cycle of disaster events. To address these gaps, this study assesses this impact at different stages of the life cycle of disturbance events, analyzes the effect of interdependencies on determining the total resilience of CISs, and discusses the implications of the results in the context of resilience enhancement of CISs in practice. To achieve this objective, this study models interconnected CISs using four different network-based approaches, simulates the disturbance propagation process and system restoration process of CISs in three different scenarios, and measures the resilience of disturbed CISs with three different resilience metrics. A case study of three CISs in a middle-sized city in Eastern China was conducted. The CISs included an electric power system, a telecommunication system, and a water supply system. The results revealed that the vulnerability of CISs to extreme events would be significantly underestimated if interdependencies of the CISs were not considered, which would cause a misleading estimation of the total resilience of the CISs. The findings also suggested the importance of considering the interdependencies of CISs in the sequencing of restoration tasks to optimize the efficiency of post-disaster restoration tasks.