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An extended recursive decomposition algorithm for dynamic seismic reliability evaluation of lifeline networks with dependent component failures

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  • He, Jun

Abstract

An extended recursive decomposition algorithm (e-RDA) is proposed to evaluate the dynamic seismic reliabilities of the complex and/or large-sized lifeline network systems with dependent component failures. To effectively analyze statistical dependence of failures of system components, multivariate extreme value response distributions of the components and joint occurrence probabilities of the (disjoint) shortest paths and cuts of systems, a multivariate Gumbel Copula is developed and introduced into the original recursive decomposition algorithm (RDA). Based on the established joint occurrence probability formula of the paths and cuts, two techniques that may accelerate convergence of the upper and lower reliability bounds of complex and/or large-sized systems are developed and embedded into the RDA. Illustrative examples are presented to demonstrate the accuracy, effectiveness and use of the extended RDA for the node weight (only node-type components may fail), edge weight (only line-type components may fail) and general weight (both node- and line-type components may fail) lifeline network systems.

Suggested Citation

  • He, Jun, 2021. "An extended recursive decomposition algorithm for dynamic seismic reliability evaluation of lifeline networks with dependent component failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004452
    DOI: 10.1016/j.ress.2021.107929
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    References listed on IDEAS

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    1. Byun, Ji-Eun & Song, Junho, 2020. "Efficient probabilistic multi-objective optimization of complex systems using matrix-based Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Kang, Won-Hee & Kliese, Alyce, 2014. "A rapid reliability estimation method for directed acyclic lifeline networks with statistically dependent components," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 81-91.
    3. Hofert, Marius, 2008. "Sampling Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5163-5174, August.
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    1. Nazarizadeh, Farzaneh & Alemtabriz, Akbar & Zandieh, Mostafa & Raad, Abbas, 2022. "An analytical model for reliability assessment of the rail system considering dependent failures (case study of Iranian railway)," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Azhdari, Armaghan & Ardakan, Mostafa Abouei & Najafi, Mojtaba, 2023. "An approach for reliability optimization of a multi-state centralized network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Li, Shunlong & Wang, Jie & He, Shaoyang, 2023. "Connectivity probability evaluation of a large-scale highway bridge network using network decomposition," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Chan, Jianpeng & Papaioannou, Iason & Straub, Daniel, 2022. "An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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