Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems

Iris Tien and Armen Der Kiureghian

Reliability Engineering and System Safety, 2016, vol. 156, issue C, 134-147

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.

Keywords: Bayesian networks; Systems modeling; Algorithms; Reliability assessment; Risk analysis; Infrastructure systems (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:156:y:2016:i:c:p:134-147

DOI: 10.1016/j.ress.2016.07.022

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