Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks

Omar Kammouh, Paolo Gardoni, Gian Paolo Cimellaro

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.

Original languageEnglish
Article number106813
Number of pages20
JournalReliability Engineering and System Safety
Volume198
DOIs
Publication statusPublished - 2020

Keywords

  • Bayesian network
  • Critical infrastructure
  • Dynamic Bayesian network
  • Recovery
  • Resilience analysis
  • Resilience indicators

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