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Network reliability analysis of complex systems using a non-simulation-based method

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  • Kim, Youngsuk
  • Kang, Won-Hee

Abstract

Civil infrastructures such as transportation, water supply, sewers, telecommunications, and electrical and gas networks often establish highly complex networks, due to their multiple source and distribution nodes, complex topology, and functional interdependence between network components. To understand the reliability of such complex network system under catastrophic events such as earthquakes and to provide proper emergency management actions under such situation, efficient and accurate reliability analysis methods are necessary. In this paper, a non-simulation-based network reliability analysis method is developed based on the Recursive Decomposition Algorithm (RDA) for risk assessment of generic networks whose operation is defined by the connections of multiple initial and terminal node pairs. The proposed method has two separate decomposition processes for two logical functions, intersection and union, and combinations of these processes are used for the decomposition of any general system event with multiple node pairs. The proposed method is illustrated through numerical network examples with a variety of system definitions, and is applied to a benchmark gas transmission pipe network in Memphis TN to estimate the seismic performance and functional degradation of the network under a set of earthquake scenarios.

Suggested Citation

  • Kim, Youngsuk & Kang, Won-Hee, 2013. "Network reliability analysis of complex systems using a non-simulation-based method," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 80-88.
    • Handle: RePEc:eee:reensy:v:110:y:2013:i:c:p:80-88
    DOI: 10.1016/j.ress.2012.09.012
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    References listed on IDEAS

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    1. Adachi, Takao & Ellingwood, Bruce R., 2008. "Serviceability of earthquake-damaged water systems: Effects of electrical power availability and power backup systems on system vulnerability," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 78-88.
    2. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
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    Cited by:

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    14. Muriel-Villegas, Juan E. & Alvarez-Uribe, Karla C. & Patiño-Rodríguez, Carmen E. & Villegas, Juan G., 2016. "Analysis of transportation networks subject to natural hazards – Insights from a Colombian case," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 151-165.
    15. Joshua L. Pulsipher & Victor M. Zavala, 2020. "Measuring and optimizing system reliability: a stochastic programming approach," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 626-645, October.
    16. Kawahara, Jun & Sonoda, Koki & Inoue, Takeru & Kasahara, Shoji, 2019. "Efficient construction of binary decision diagrams for network reliability with imperfect vertices," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 142-154.
    17. Bistouni, Fathollah & Jahanshahi, Mohsen, 2015. "Evaluating failure rate of fault-tolerant multistage interconnection networks using Weibull life distribution," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 128-146.
    18. Dehghani, Nariman L. & Zamanian, Soroush & Shafieezadeh, Abdollah, 2021. "Adaptive network reliability analysis: Methodology and applications to power grid," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
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