Relationship of Resilience Investment Rules and Routing Diversity to Recovery Speed in Coastal Infrastructure Networks

Authors

  • Alice Ng Department of Electrical and Electronic Engineering, Faculty of Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China Author

Keywords:

Coastal Infrastructure, Recovery Speed, Resilience Investment, Routing Diversity, Network Topology

Abstract

Coastal infrastructure networks represent a critical backbone of modern urban economies, yet they are increasingly vulnerable to severe weather events and climate-driven hazards. As the frequency of disruptive incidents escalates, evaluating network robustness alone is no longer sufficient; understanding the determinants of post-disaster recovery speed has become paramount. This study provides a comprehensive investigation into how resilience investment rules and routing diversity jointly explain the variance in recovery speed across complex coastal infrastructure systems. By conceptualizing infrastructure as interconnected spatial graphs, this research evaluates diverse resource allocation strategies alongside topological variations characterized by multi-path routing redundancy. Through extensive simulation of hazard scenarios such as storm surges and hurricanes, the analysis demonstrates that targeted resilience investments based on node vulnerability significantly accelerate functional restoration compared to uniform or purely centrality-based hardening. Furthermore, high routing diversity is shown to act as a crucial buffer during the initial phases of disaster impact, allowing networks to maintain baseline operations while physical repairs are executed. The findings indicate a synergistic relationship between strategic financial investment and topological design, suggesting that coastal urban planners must integrate both dimensions to optimize recovery trajectories. This paper provides actionable insights for civil engineers and policymakers tasked with safeguarding coastal communities against an increasingly volatile climate future.

References

1. Zhao, R.; Cai, J.; Luo, J.; Ran, Y.; Gao, J.; Xu, Y. Joint Beam Hopping and Resource Allocation for Load Balancing and Interference Avoidance in Multi-LEO Satellite Networks. In Proceedings of the ICC 2025—IEEE International Conference on Communications, Montreal, QC, Canada, 8–12 June 2025; pp. 958–963.

2. Fastiggi, M.; Meerow, S.; Miller, T.R. Governing urban resilience: Organisational structures and coordination strategies in 20 North American city governments. Urban Stud. 2021, 58, 1262–1285.

3. Viana, J.; Farkhari, H.; Gil Jiménez, V.P. Securing 5G and Beyond-Enabled UAV Links: Resilience Through Multiagent Learning and Transformers Detection. IEEE Access 2025, 13, 153993–154007.

4. Ayyappan, V.; Bruno, M.A. Applying Machine Learning to Optimize Resource Allocation & Maritime Wireless Mobile Network. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2025, 16, 406–416.

5. Kim, S.; Andrew, S.A.; Ramirez de la Cruz, E.; Kim, W.-J.; Feiock, R.C. Impacts of Local Government Perceptions of Disaster Risks on Land Resilience Planning Implementation. Land 2024, 13, 1085.

6. Kunreuther, H., & Pauly, M. V. (2006). Rules rather than discretion: Lessons from Hurricane Katrina. Journal of Risk and Uncertainty, 33, 101–116.

7. Ministry of Land, Infrastructure, Transport and Tourism. National Land Numerical Information Download Site. Available online: https://nlftp.mlit.go.jp/ksj/index.html (accessed on 29 August 2024).

8. Lin, G.; Xiao, Y.; Yu, S.; Yu, K.; Liu, J. Constrained Probabilistic Routing with RouterRL: A General Packet-Level Network Simulation Framework. IEEE Trans. Netw. Sci. Eng. 2026, 13, 2604–2622.

9. Li, S.; Wu, Q.; Wang, R. Efficient Packet Routing in Ultra-Dense LEO Satellite Networks via Cooperative-MARL with Queuing Theory Model. In Proceedings of the 2025 IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy, 24–27 March 2025; pp. 1–6.

10. Nguyen, T.T.H.; Kim, T. Proximal Policy Optimization for Up/Downlink Time Slots Allocation in 5/6G Dynamic TDD Networks. KSII Trans. Internet Inf. Syst. 2025, 19, 259–278.

11. He, H.; Yang, X.; Mi, X.; Shen, H.; Liao, X. Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing Network to Minimize Average Task Delay with Deadline Constraints. Sensors 2024, 24, 5141.

12. Cui, Z.; Qamar, F.; Kazmi, S.H.A.; Zainol Ariffin, K.A.; Safdar, G.A.; Ur Rehman, M.H. A Review of Multi-Agent Deep Reinforcement Learning for Resource Allocation in beyond 5G Network Slicing: Solutions, Challenges and Future Research Directions. PeerJ Comput. Sci. 2026, 12, e3728.

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Published

2026-05-26

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Articles