QUT ePrints

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. (In Press)

Abstract

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:

0 citations in Scopus
Search Google Scholar™
0 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

174 since deposited on 03 Jun 2012
60 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 50715
Item Type: Conference Paper
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: 04 Jun 2012 09:34
Last Modified: 20 Oct 2012 14:24

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page