Identifying influential assets in higher order interdependent infrastructure networks through population impact

A significant increase in catastrophic events worldwide has had a disastrous impact on the built environment, as these disruptive events devastate critical infrastructure lifelines, resulting in a substantial reduction of services across a community. Management of critical infrastructure assets is essential, and this can be achieved by optimal resource allocation to key assets within interconnected systems. The majority of current literature focuses on capturing the behavior of infrastructure systems individually, which presents a computational problem when dealing with complex, interdependent infrastructure systems. In this study, a comprehensive framework to evaluate a type of influence metric for ranking node and edge assets within an infrastructure network is presented. The influence metrics are shown to identify the importance of individual assets relative to one another, considering their dependencies with other critical networks. The city of Lima is utilized as a testbed to demonstrate the effectiveness of the proposed influence metric. We found that the failure of individual assets in critical infrastructure systems, such as water treatment plants, major and minor power stations, water reservoirs, and hospitals, affected a maximum percentage of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of the Lima population, respectively. The proposed influence metric is observed to outperform degree centrality in identifying critical assets within individual infrastructure lifelines, considering complex dependencies on other systems. This approach highlights a direction to understand dependent networks in general and can open up new frontiers in understanding complex system behavior.