A Bayesian kernel approach to modeling resilience-based network component importance

Hiba Baroud and Kash Barker

Reliability Engineering and System Safety, 2018, vol. 170, issue C, 10-19

Abstract: The resilience of infrastructure networks is an increasingly important consideration in infrastructure planning and risk management. One aspect of resilience-based planning is determining which components in the network are most important to the resilience of the network. This work makes use of a resilience-based component importance measure, the resilience worth, and proposes to model this measure under uncertainty using a Bayesian kernel technique. Such a technique can be useful in modeling component importance as it enables the probability distribution for the importance measure to be updated using data and prior information with a Bayesian kernel model. The proposed approach is applied to study the importance of locks and dams along the Mississippi River Navigation System. The highest predictive overall accuracy is achieved with a uniform prior distribution, and using the posterior distribution and a multicriteria decision analysis technique, we identify the five locks and dams with the largest impact on the system's resilience.

Keywords: Resilience; Component importance; Bayesian kernel methods; Infrastructure systems (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:170:y:2018:i:c:p:10-19

DOI: 10.1016/j.ress.2017.09.022

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