Interdependent effects of critical infrastructure systems under different types of disruptions

Modern critical infrastructure systems (CISs) are becoming increasingly dependent on each other for proper operation, which also exposes the systems to higher risks of cascading failures. Prior research has attempted to quantify the interdependent effect (IE) of CISs under random or intentional disruptions. However, those efforts are limited with respect to both the methodological approaches for simulating intentional disruptions and CISs failure propagation, and the granularity and quantification method of the simulated disruption impacts. To address these limitations, this study proposes a new framework for analyzing the IE of CISs. The framework first models interdependent CISs with a high level architecture (HLA)-based simulation approach that integrates existing knowledge, data, and models specific to each infrastructure domain. It then co-simulates the failure propagation through interdependent CISs under three different types of disruptions, namely random, topology-based and flow-based disruptions. Based on the simulation outputs, the framework quantifies the IE by comparing the disruption-induced system performance losses, which are measured with three different performance metrics, under both interdependent and independent conditions. A case study of two interdependent water and power supply systems was conducted to demonstrate the efficacy of the proposed framework. The case study results revealed a few new insights into the vulnerability of interdependent CISs under different disruption scenarios, which have important implications for the risk assessment and resilience management of interdependent CISs in practice.

Automated summary

Modern critical infrastructure systems are increasingly dependent on each other, which exposes them to higher risks of cascading failures. This study proposes a new framework for analyzing the interdependent effect of these systems. The framework models interdependent systems and simulates failure propagation under different types of disruptions. By comparing disruption-induced system performance losses, the framework quantifies the interdependent effect.