Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs)

Wang, Ruizi, Ma, Lin, Yan, Cheng, & Mathew, Joseph (2012) Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs). In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, IEEE Explore, University of Electronic Science and Technology, Chengdu, Sichuan.


The ability of bridge deterioration models to predict future condition provides significant advantages in improving the effectiveness of maintenance decisions. This paper proposes a novel model using Dynamic Bayesian Networks (DBNs) for predicting the condition of bridge elements. The proposed model improves prediction results by being able to handle, deterioration dependencies among different bridge elements, the lack of full inspection histories, and joint considerations of both maintenance actions and environmental effects. With Bayesian updating capability, different types of data and information can be utilised as inputs. Expert knowledge can be used to deal with insufficient data as a starting point. The proposed model established a flexible basis for bridge systems deterioration modelling so that other models and Bayesian approaches can be further developed in one platform. A steel bridge main girder was chosen to validate the proposed model.

Impact and interest:

3 citations in Scopus
1 citations in Web of Science®
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274 since deposited on 03 Jun 2012
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ID Code: 50715
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Bridge deterioration models, Condition ratings, Dynamic Bayesian Networks (DBNs), Expert knowledge
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Infrastructure Engineering and Asset Management (090505)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Mechanical Engineering not elsewhere classified (091399)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 [please consult the author]
Deposited On: 03 Jun 2012 23:34
Last Modified: 20 Oct 2012 04:24

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