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dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.authorSon, Jungen_US
dc.contributor.editorFrangomeni et alen_US
dc.date.accessioned2017-04-04T18:47:24Z
dc.date.available2017-04-04T18:47:24Z
dc.date.issued2010en_US
dc.identifier.refurihttp://www.vu.edu.au/acmsm21en_US
dc.identifier.doi10.1201/b10571-140en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent432964 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherTaylor & Francis Groupen_US
dc.publisher.placeLondonen_US
dc.relation.ispartofstudentpublicationYen_US
dc.relation.ispartofconferencename21st Australasian Conference on the Mechanics of Structures and Materials (ACMSM)en_US
dc.relation.ispartofconferencetitleIncorporating Sustainable Practice in Mechanics of Structures and Materialsen_US
dc.relation.ispartofdatefrom2010-12-07en_US
dc.relation.ispartofdateto2010-12-10en_US
dc.relation.ispartoflocationMelbourne, Australiaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchSimulation and Modellingen_US
dc.subject.fieldofresearchcode090505en_US
dc.subject.fieldofresearchcode080110en_US
dc.titleANN-based Structural Performance Model for Reliable Bridge Asset Managementen_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 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.en_US
gro.date.issued2015-06-02T05:40:44Z
gro.hasfulltextFull Text


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

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