Quantifying Reliability and Resilience in Interdependent Infrastructure Networks
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Abstract
Infrastructure networks such as power, water, and communication systems are interdependent, and disruptions in one network can propagate across others, causing performance loss and delayed recovery. Although prior studies have modeled recovery and evaluated resilience in such systems, few have examined their operational limits through reliability analysis. This study develops a simulation-based framework to quantify reliability and resilience in two partially interdependent networks while considering node failure and recovery. The critical percolation threshold is identified from the cascading failure process and used in a voting system model to compute system reliability. Results show that higher coupling strength shifts the percolation threshold to a larger value and reduces system reliability, whereas lower coupling strength allows the system to sustain connectivity under a greater degree of node failure. Resilience is evaluated through a cascading recovery process following complete disruption of one network. Recovery of a node enables restoration of its dependent counterpart in the other network, propagating sequentially across both networks. Multiple simulation scenarios are employed to represent uncertainty in the node recovery process. Results indicate that higher coupling strength leads to greater initial disruption in the dependent network but facilitates faster and more coordinated recovery in both networks. Thus, reliability and resilience exhibit opposing trends with coupling strength. An interdependency index is also introduced to support management decisions for interdependent infrastructure networks. This framework provides a structured basis for understanding how inter-network connectivity affects system reliability and recovery dynamics.
How to Cite
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Reliability, Resilience, Cascading failures, Interdependent networks, Percolation theory
Applegate, C. J., & Tien, I. (2019). Framework for probabilistic vulnerability analysis of interdependent infrastructure systems. Journal of Computing in Civil Engineering, 33(1), 04018058.
Aros-Vera, F., & Thekdi, S. (2025). Modeling and managing resilience and risk for interdependent networks. Socio-Economic Planning Sciences, 97, 102105. doi: https://doi.org/10.1016/j.seps.2024.102105
Barton, D. C., Eidson, E. D., Schoenwald, D. A., Stamber, K. L., & Reinert, R. (2000). Aspen-EE: An agent-based model of infrastructure interdependency. Sandia National Laboratories, Albuquerque, NM, USA.
Becker, R., Casteigts, A., Crescenzi, P., Kodric, B., Raskin, M., Renken, M., & Zamaraev, V. (2026). Giant components in random temporal graphs. SIAM Journal on Discrete Mathematics, 40(2), 449–485.
Bellè, A. (2022). Resilience and coupling of interdependent critical infrastructures: Models, optimization, and operations [Doctoral dissertation, Université Paris-Saclay].
Bešinović, N. (2020). Resilience in railway transport systems: A literature review and research agenda. Transport Reviews, 40(4), 457–478. doi: https://doi.org/10.1080/01441647.2020.1728419
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 1025–1028. doi: https://doi.org/10.1038/nature08932
Bunde, A., & Havlin, S. (2012). Fractals and disordered systems. Springer.
Chowdhury, R. R., Yadav, O. P., & Limon, S. M. (2024). Reliability analysis of interdependent stochastic-flow networks. In 2024 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1–6).
Cohen, R., & Havlin, S. (2010). Complex networks: Structure, robustness and function. Cambridge University Press.
Elsayed, E. A. (2012). Reliability engineering (2nd ed., Vol. 88). John Wiley & Sons.
Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17.
Gaur, V., Yadav, O. P., Soni, G., & Rathore, A. P. S. (2021). A literature review on network reliability analysis and its engineering applications. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(2), 167–181.
Ghorbani-Renani, N., González, A. D., Barker, K., & Morshedlou, N. (2020). Protection-interdiction-restoration: Tri-level optimization for enhancing interdependent network resilience. Reliability Engineering & System Safety, 199, 106907. doi: https://doi.org/10.1016/j.ress.2020.106907
González, A. D., Dueñas‐Osorio, L., Sánchez‐Silva, M., & Medaglia, A. L. (2016). The interdependent network design problem for optimal infrastructure system restoration. Computer-Aided Civil and Infrastructure Engineering, 31(5), 334–350. doi: https://doi.org/10.1111/mice.12171
Henry, D., & Emmanuel Ramirez-Marquez, J. (2012). Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 99, 114–122. doi: https://doi.org/10.1016/j.ress.2011.09.002
Holden, R., Val, D. V., Burkhard, R., & Nodwell, S. (2013). A network flow model for interdependent infrastructures at the local scale. Safety Science, 53, 51–60.
Holme, P., Kim, B. J., Yoon, C. N., & Han, S. K. (2002). Attack vulnerability of complex networks. Physical Review E, 65(5), 056109.
Iannacone, L., Sharma, N., Tabandeh, A., & Gardoni, P. (2022). Modeling time-varying reliability and resilience of deteriorating infrastructure. Reliability Engineering & System Safety, 217, 108074. doi: https://doi.org/10.1016/j.ress.2021.108074
Kobayashi, M. (2014). Experience of infrastructure damage caused by the Great East Japan Earthquake and countermeasures against future disasters. IEEE Communications Magazine, 52(3), 23–29. doi: https://doi.org/10.1109/MCOM.2014.6766080
Li, D., Zhang, Q., Zio, E., Havlin, S., & Kang, R. (2015). Network reliability analysis based on percolation theory. Reliability Engineering & System Safety, 142, 556–562.
Li, Y., Lin, J., Li, G., Wang, C., Zhang, C., & Gao, L. (2020). Vulnerability assessment of community-interdependent infrastructure network based on PSDA. Journal of Infrastructure Systems, 26(2), 04020006.
Lu, Q.-C., Li, J., Xu, P.-C., Zhang, L., & Cui, X. (2024). Modeling cascading failures of urban rail transit network based on passenger spatiotemporal heterogeneity. Reliability Engineering & System Safety, 242, 109726. doi: https://doi.org/10.1016/j.ress.2023.109726
Miao, H., Liu, Y., Zhong, Z., Han, J., Hou, B., & Du, X. (2025). Seismic reliability evaluation of interdependent water and power supply networks. Earthquake Engineering and Resilience, 4(1), 41–60.
Parshani, R., Buldyrev, S. V., & Havlin, S. (2010). Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition. Physical Review Letters, 105(4), 048701.
Paulik, R., Gusman, A., Williams, J. H., Pratama, G. M., Lin, S., Prawirabhakti, A., Sulendra, K., Zachari, M. Y., Fortuna, Z. E. D., Layuk, N. B. P., & Suwarni, N. W. I. (2019). Tsunami hazard and built environment damage observations from Palu City after the September 28, 2018 Sulawesi earthquake and tsunami. Pure and Applied Geophysics, 176(8), 3305–3321. doi: https://doi.org/10.1007/s00024-019-02254-9
Satumtira, G., & Dueñas-Osorio, L. (2010). Synthesis of modeling and simulation methods on critical infrastructure interdependencies research. In Sustainable and resilient critical infrastructure systems: Simulation, modeling, and intelligent engineering (pp. 1–51). Springer.
Setola, R. (2010). How to measure the degree of interdependencies among critical infrastructures. International Journal of System of Systems Engineering, 2(1), 38. doi: https://doi.org/10.1504/IJSSE.2010.035380
Sharma, N., Tabandeh, A., & Gardoni, P. (2020). Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure. Computer-Aided Civil and Infrastructure Engineering, 35(12), 1315–1330. doi: https://doi.org/10.1111/mice.12606
Shier, D. R. (1991). Network reliability and algebraic structures. Oxford University Press.
Shu, P., Wang, W., Tang, M., & Do, Y. (2014). Simulated identification of epidemic threshold on finite-size networks. arXiv preprint arXiv:1410.0459.
Stauffer, D., & Aharony, A. (2018). Introduction to percolation theory. Taylor & Francis.
Wilkov, R. (2003). Analysis and design of reliable computer networks. IEEE Transactions on Communications, 20(3), 660–678.
Wilson, J. M. (2002). An improved minimizing algorithm for sum of disjoint products in reliability theory. IEEE Transactions on Reliability, 39(1), 42–45.
Xiao, Y., Zhao, X., Wu, Y., Chen, Z., Gong, H., Zhu, L., & Liu, Y. (2022). Seismic resilience assessment of urban interdependent lifeline networks. Reliability Engineering & System Safety, 218, 108164. doi: https://doi.org/10.1016/j.ress.2021.108164
Zhang, C., Han, C., Meng, L., & Wang, Y. (2012). Interdependent dynamic model and repairing strategy of electric power and water supply systems. Journal of Information Systems Engineering & Management, 21, 564–570.
Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering & System Safety, 94(2), 125–141.
Zio, E., Podofillini, L., & Zille, V. (2006). A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems. Reliability Engineering & System Safety, 91(2), 181–190.
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