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Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems

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  • Tien, Iris
  • Der Kiureghian, Armen

Abstract

Novel algorithms are developed to enable the modeling of large, complex infrastructure systems as Bayesian networks (BNs). These include a compression algorithm that significantly reduces the memory storage required to construct the BN model, and an updating algorithm that performs inference on compressed matrices. These algorithms address one of the major obstacles to widespread use of BNs for system reliability assessment, namely the exponentially increasing amount of information that needs to be stored as the number of components in the system increases. The proposed compression and inference algorithms are described and applied to example systems to investigate their performance compared to that of existing algorithms. Orders of magnitude savings in memory storage requirement are demonstrated using the new algorithms, enabling BN modeling and reliability analysis of larger infrastructure systems.

Suggested Citation

  • Tien, Iris & Der Kiureghian, Armen, 2016. "Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 134-147.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:134-147
    DOI: 10.1016/j.ress.2016.07.022
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    References listed on IDEAS

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    1. Salmeron, Antonio & Cano, Andres & Moral, Serafin, 2000. "Importance sampling in Bayesian networks using probability trees," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 387-413, October.
    2. Der Kiureghian, Armen & Song, Junho, 2008. "Multi-scale reliability analysis and updating of complex systems by use of linear programming," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 288-297.
    3. Yeh, Wei-Chang, 2006. "A new algorithm for generating minimal cut sets in k-out-of-n networks," Reliability Engineering and System Safety, Elsevier, vol. 91(1), pages 36-43.
    4. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
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    Cited by:

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