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dc.contributor.authorSon, JB
dc.contributor.authorLee, JH
dc.contributor.authorGuan, H
dc.contributor.authorLoo, YC
dc.contributor.authorBlumenstein, M
dc.contributor.editorFragomeni, S
dc.contributor.editorVenkatesan, S
dc.contributor.editorLam, NTK
dc.contributor.editorSetunge, S
dc.date.accessioned2017-05-03T14:06:21Z
dc.date.available2017-05-03T14:06:21Z
dc.date.issued2011
dc.identifier.isbn978-0-415-61657-7
dc.identifier.refurihttp://www.vu.edu.au/acmsm21
dc.identifier.doi10.1201/b10571-140
dc.identifier.urihttp://hdl.handle.net/10072/37233
dc.description.abstractBridge Management Systems (BMSs) have been developed to assist in the management of a large bridge network. Historical condition ratings obtained from bridge inspections are major resources for predicting future deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for predicting reliable future structural performance. To alleviate this problem, A Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings which is crucial for bridge deterioration models to be able to predict more accurate solutions. Nevertheless, there are still considerable limitations in the existing bridge deterioration models. In view of this, feasibility study of Time Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. It is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent432964 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherTaylor & Francis Group
dc.publisher.placeLondon
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename21st Australasian Conference on the Mechanics of Structures and Materials (ACMSM)
dc.relation.ispartofconferencetitleINCORPORATING SUSTAINABLE PRACTICE IN MECHANICS OF STRUCTURES AND MATERIALS
dc.relation.ispartofdatefrom2010-12-07
dc.relation.ispartofdateto2010-12-10
dc.relation.ispartoflocationVictoria Univ City Campus, Melbourne, AUSTRALIA
dc.relation.ispartofpagefrom775
dc.relation.ispartofpagefrom6 pages
dc.relation.ispartofpageto780
dc.relation.ispartofpageto6 pages
dc.rights.retentionY
dc.subject.fieldofresearchModelling and simulation
dc.subject.fieldofresearchInfrastructure engineering and asset management
dc.subject.fieldofresearchcode460207
dc.subject.fieldofresearchcode400508
dc.titleANN-based Structural Performance Model for Reliable Bridge Asset Management
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2010 Taylor & Francis. This is the author-manuscript version of the paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the publisher's website for access to the definitive, published version.
gro.date.issued2015-06-02T05:40:44Z
gro.hasfulltextFull Text
gro.griffith.authorLoo, Yew-Chaye
gro.griffith.authorGuan, Hong


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

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