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Scalable criticality analysis in interdependent infrastructure systems using functionality graphs
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AI
DOI：10.1016/j.ijdrr.2026.106360.png)
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Abstract
En 中文
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Introduces a scalable human-centric framework for interdependent infrastructure analysis using functionality-level modeling across multiple infrastructure networks.
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Integrates Hetero-Functional Graphs (HFGs) and Graph Neural Networks (GNNs) with domain expert input for criticality assessment.
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Models fine-grained functionality dependencies and cascading impacts, supported by Hetero-function graph generation, and robustness tests on expert seed labels.
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Proposes an Access Degradation Factor (ADF) to quantify community-level service disruption using geospatially referenced accessibility and travel-time metrics.
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Demonstrates scalability, generalization to geographically disjoint regions, and computational gains over traditional simulation-based methods using real multi-layer infrastructure data from Western Kansas, USA.
Keywords:
Interdependent critical infrastructure systems
Resilience
Complex system
Interdependencies
Smart cities
Critical asset
Robustness
Journal
IF:
4.5
Papers:
6.0K
Citations:
2.1W
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