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dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorXin-ping, Wangen_US
dc.contributor.editorXuejun Zhouen_US
dc.date.accessioned2017-04-24T11:20:52Z
dc.date.available2017-04-24T11:20:52Z
dc.date.issued2011en_US
dc.date.modified2012-03-14T05:33:57Z
dc.identifier.issn1662-7482en_US
dc.identifier.doi10.4028/www.scientific.net/AMM.99-100.444en_US
dc.identifier.urihttp://hdl.handle.net/10072/43609
dc.description.abstractEfficient use of public funds for structural integrity of bridge networks requires an effective bridge asset management technology. To achieve this, a reliable deterioration model is essential in any Bridge Management System (BMS). The deterioration rate is calculated based on historical condition ratings obtained from the structural element-level bridge inspections. Although most bridge authorities have previously conducted inspection and maintenance tasks, these past inspection records are incompatible with what are required by a typical BMS as input. Such incompatibility is a major cause for the deficiency of the current BMS outcomes. Artificial Intelligence (AI)-based bridge deterioration model has recently been developed to minimise uncertainties in predicting deterioration of structural bridge members (e.g. beams, piers etc). This model contains two components: (1) using Neural Network-based Backward Prediction Model (BPM) to generate unavailable historical condition ratings; and (2) using Time Delay Neural Network (TDNN) to perform long-term performance prediction of bridge structural members. However new problems have emerged in the process of TDNN prediction. This is because the BPM-generated condition ratings are used together with the actual condition ratings. The incompatibility between the two sets of data produces unreliable prediction outcomes during the TDNN process. This research is thus to develop a new process based on the existing method, thereby overcoming the abovementioned problems. To achieve this, the actual overall condition ratings are replaced by the BPM forward predicted condition ratings. Consequently, the outcome of this study can improve accuracy of long-term bridge deterioration prediction.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent1330340 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.publisherTrans Tech Publicationsen_US
dc.publisher.placeSwitzerlanden_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom444en_US
dc.relation.ispartofpageto453en_US
dc.relation.ispartofjournalApplied Mechanics and Materialsen_US
dc.relation.ispartofvolume99-100en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleModelling Long-term Bridge Deterioration at Structural Member Level Using Artificial Intelligence Techniquesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2011 Trans Tech Publications. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_US
gro.date.issued2011
gro.hasfulltextFull Text


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