A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

A. Kosgodagan, O. Morales-Napoles, T.G. Yeung, Wim M.G. Courage, J. Maljaars, B. Castanier

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)
1 Downloads (Pure)

Abstract

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.
Original languageEnglish
Pages (from-to)641-656
Number of pages16
JournalComputer-Aided Civil and Infrastructure Engineering
Volume32
Issue number8
DOIs
Publication statusPublished - Aug 2017

Fingerprint Dive into the research topics of 'A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network'. Together they form a unique fingerprint.

Cite this