A Bayesian kernel approach to modeling resilience-based network component importance. (February 2018)

• Record Type: Journal Article
• Authors: Baroud, Hiba; Barker, Kash
• Is Part Of: Reliability engineering & system safety. Volume 170(2018)
• Page Start: 10
• Page End: 19
• Publication Date: 2018-02
• Subjects: Resilience -- Component importance -- Bayesian kernel methods -- Infrastructure systems; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.09.022
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: We develop a Bayesian kernel approach to model the probability distribution of a component importance measure. Component importance measures relate to community resilience. We apply the approach to study locks and dams along the Mississippi River Navigation System. 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.