Show simple item record

dc.contributor.convenorIABSE, UKen_US
dc.contributor.authorBu, Guopingen_US
dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.editorIABSE & IASSen_US
dc.date.accessioned2017-05-03T14:06:18Z
dc.date.available2017-05-03T14:06:18Z
dc.date.issued2011en_US
dc.date.modified2013-08-22T22:40:23Z
dc.identifier.refurihttp://www.iabse-iass-2011.com/en_US
dc.identifier.urihttp://hdl.handle.net/10072/44700
dc.description.abstractForecasting long-term performance of bridge by deterioration model is a crucial component in a Bridge Management System (BMS). Markovian-based models are one of the most typical methods to predict long-term bridge performance. The Markovian-based model is selected for predicting bridge deterioration, because it is the most widely accepted prediction model and has been adopted by most State-of-the-Art BMSs. The Markovian-based model is based on transition matrix obtained from overall condition rating of bridges in a network. The change in condition ratings with time provides typical deterioration rates, which can normally be determined from a non-linear regression analysis. Reliable regression analysis requires either large bridge network or sufficient historical condition ratings to obtain accurate transition probability for bridges. Markovian-based model prediction is a simple way to forecast long term performance of individual bridge. However, most bridge agencies do not have adequate condition rating records. This has become a major shortcoming in deterioration modelling. To minimise the abovementioned problem, this paper presents modified Markovian method using previously developed BPM. The BPM is able to generate missing historical condition ratings thereby providing more historical trend of condition depreciation. In this study, BPM-generated condition ratings are used for regression analysis to obtain reliable transition probability required by the Markovian-based model. The results of the proposed study are compared with those of a typical Markovian-based model to identify the advantage of BPM and limitations for further development.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent228974 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherIABSEen_US
dc.publisher.placeUnited Kingdomen_US
dc.publisher.urihttp://www.iabse.org/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename35th International Symposium on Bridge and Structural Engineering (IASBE)en_US
dc.relation.ispartofconferencetitleIABSE-IASS 2011 Symposium - Taller, Longer, Lighteren_US
dc.relation.ispartofdatefrom2011-09-20en_US
dc.relation.ispartofdateto2011-09-23en_US
dc.relation.ispartoflocationLondon, United Kingdomen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleImproving Reliability of Markov-based Bridge Deterioration Model using Artificial Neural Networken_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2011 IASBE. The attached file is posted here in accordance with the copyright policy of the publisher, for your personal use only. No further distribution permitted. Use hypertext link for access to conference website.en_US
gro.date.issued2011
gro.hasfulltextFull Text


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record