Identifying influential assets in higher order interdependent infrastructure networks through population impact.
Akshat Chulahwat1, Emad M Hassan2, Mersedeh Tariverdi3
1Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, United States.
Scientific Reports
|September 29, 2025
Summary
Catastrophic events threaten critical infrastructure. This study introduces an influence metric to rank essential assets in interdependent networks, outperforming traditional methods for better resource allocation and community resilience.
Area of Science:
- Engineering
- Network Science
- Urban Planning
Background:
- Increasing global catastrophic events impact critical infrastructure and community services.
- Current infrastructure management often analyzes systems in isolation, posing challenges for complex, interdependent networks.
- Optimal resource allocation to key assets is crucial for maintaining essential services.
Purpose of the Study:
- To present a framework for evaluating an influence metric to rank assets within infrastructure networks.
- To assess the importance of individual assets considering their interdependencies with other critical networks.
- To demonstrate the effectiveness of the proposed metric using the city of Lima as a case study.
Main Methods:
- Development of a comprehensive framework to calculate influence metrics for network nodes and edges.
- Application of the influence metric to analyze critical infrastructure in Lima, Peru.
- Comparison of the proposed influence metric with degree centrality for asset ranking.
Main Results:
- The proposed influence metric effectively identifies critical assets within interdependent infrastructure systems.
- Failure analysis showed significant population impact from individual asset failures (e.g., water treatment plants, power stations, hospitals).
- The influence metric outperformed degree centrality in identifying crucial assets within interconnected lifelines.
Conclusions:
- The developed influence metric provides a superior method for ranking critical infrastructure assets in complex, interdependent networks.
- This approach enhances understanding of dependent network behavior and informs strategies for infrastructure resilience.
- The findings support improved resource allocation for critical infrastructure management to mitigate the impact of disruptive events.
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