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

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

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

2020

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.

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

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

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

2022-02-18

0951-8320

Reliability Engineering & System Safety, 198

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 O. Kammouh, Paolo Gardoni, Gian Paolo Cimellaro