Interdependent infrastructure systems serve as the foundation of modern society, ensuring the delivery of essential services, economic stability, and public well-being. The increasing integration of these systems, driven by advancements in cyber-physical systems (CPS) and the Internet of Things (IoT), has improved operational efficiency and adaptability. However, this interconnectivity and interdependency have also introduced vulnerabilities, making infrastructure networks more susceptible to cascading failures, cyber attacks, and disruptions from natural disasters and human-induced events. Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges, offering data-driven solutions for reliability and resilience analysis across interconnected infrastructure networks. This paper provides a comprehensive review of GNN applications in interdependent infrastructure systems, including transportation networks, power distribution networks, water distribution networks, and communication networks. By leveraging spatial-temporal modeling, multi-modal data integration, and physics-informed learning, GNN-based approaches enhance predictive accuracy, system resilience, and decision-making efficiency. This review underscores the potential of GNNs in infrastructure management optimization and risk mitigation, and enabling the development of more sustainable, adaptive, and resilient infrastructure systems in the face of evolving challenges.

Natural hazards can substantially damage bridge networks. Therefore to protect these lifelines, a bridge maintenance and retrofit strategy is imperative. To prepare for earthquakes in particular, conventional methods for seismic risk analysis needs to calculate network response which is a computationally intensive process. This prevents the optimal bridge maintenance for seismic events from being widely done for large networks. Also, it is critical to integrate measures of equity in these optimal asset management solutions to ensure equitable post-disaster access to mobility across all income levels. In this study, we propose a graph neural network (GNN) surrogate model for rapid estimation of transportation performance and a bridge maintenance optimization framework that considers network connectivity and equity measures. The proposed multi-objective optimization, via genetic algorithm with graph neural network surrogates, identifies the optimal bridge retrofit strategies constrained by a retrofit budget. The efficacy and accuracy of the proposed algorithm are demonstrated using a road network in the California Bay Area as a case study. Also, it will be shown that integrating income disparities in the region into the optimization framework can lead to improved transportation equity measures.