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

Kammouh, O.; Gardoni, Paolo; Cimellaro, Gian Paolo

doi:10.1016/j.ress.2020.106813

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

Title

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

Author

Kammouh, O. (TU Delft Integral Design and Management)
Gardoni, Paolo (University of Illinois at Urbana-Champaign)
Cimellaro, Gian Paolo (Politecnico di Torino)

Date

2020

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.

Subject

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

To reference this document use:

http://resolver.tudelft.nl/uuid:b6dd3e3c-a35a-419c-8d35-3a037ff1c2eb

DOI

https://doi.org/10.1016/j.ress.2020.106813

Embargo date

2022-02-18

ISSN

0951-8320

Source

Reliability Engineering & System Safety, 198