A population-based worst-case localized disruption scenario identification method of interdependent infrastructure systems

Infrastructure systems are indispensable to modern societies, underpinning vital services that sustain everyday life. Nevertheless, these systems are intricately interdependent and frequently vulnerable to various hazards, often manifesting as localized disruptions. This study eschews hazard-specific modeling in favor of a worst-case scenario approach to identify the potential for the most severe damage scenario that could precipitate the greatest loss of performance across these interconnected systems. Traditional identification methods primarily assess post-disaster performance based on the operational states of individual components. However, many authoritative reports emphasize the affected population as a significant consequence of such hazards. Addressing this discrepancy, this study proposes a population-based method to identify critical areas whose component disruptions could impact the largest number of people. This model also facilitates the exploration of new types of logical interdependencies, captured through a population-based objective function, thus providing a complementary tool for the disaster management of urban areas. Applied to Tianjin Eco City, the results demonstrate the dynamic nature of disaster consequences, varying with the intensity of disruptions. The study further elucidates the implications of considering interdependencies within road transport systems and offers a comparative analysis of different modeling approaches for attackers and operators.