Graph Neural Networks for Evaluating the Reliability and Resilience of Infrastructure Systems: A Systematic Review of Models, Applications and Future Directions

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...

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Bibliographic Details
Published in:IEEE access Vol. 13; p. 1
Main Authors: Liu, Tong, Liu, Fangyu
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communication networks
Critical infrastructure systems
Cyber-physical systems
Cybersecurity
Data integration
Graph neural network
Graph neural networks
Human influences
Infrastructure
Interdependency
Internet of Things
Measurement
Modal data
Natural disasters
Neural networks
Power system faults
Power system protection
Power system stability
Probabilistic logic
Reliability
Reliability analysis
Resilience
Resilience analysis
Transportation
Transportation networks
Water distribution
Water engineering
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3611333