Optimizing urban infrastructure resilience: Analyzing cascading failures and critical node dependencies through multilayer network models

Abstract

As urbanization and industrialization progress, urban infrastructure systems grow increasingly complex, heightening their vulnerability to cascading failures from natural disasters and human-induced disruptions. Strengthening the resilience of these systems is critical for sustainable urban development and sustaining residents’ quality of life. This study introduces a novel framework to analyze cascading failure propagation within infrastructure networks. Utilizing the implicit interdependency model, we construct a multilayer network that delineates interconnections and dependencies across infrastructure sectors. The PageRank algorithm is used to identify critical nodes by evaluating their network centrality, thereby highlighting key components within the system. Through simulations of random, PageRank-based, and betweenness-based attack scenarios, we explore failure dynamics and their propagation patterns. Additionally, we evaluate mitigation strategies, with the community periphery augmentation strategy proving most effective, enhancing resilience by linking peripheral nodes between communities. This research systematically connects the significance of key nodes to cascading effects, uncovering vulnerabilities and providing actionable insights for disaster response and recovery planning.