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dc.contributor.authorLee, Jaeho
dc.contributor.authorGuan, Hong
dc.contributor.authorLoo, Yew-Chaye
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorWang, Xin-ping
dc.contributor.editorZhou, XJ
dc.date.accessioned2017-05-03T14:06:22Z
dc.date.available2017-05-03T14:06:22Z
dc.date.issued2011
dc.date.modified2012-03-14T05:33:57Z
dc.identifier.issn1660-9336
dc.identifier.doi10.4028/www.scientific.net/AMM.99-100.444
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1330340 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherTrans Tech Publications
dc.publisher.placeSwitzerland
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom444
dc.relation.ispartofpageto453
dc.relation.ispartofjournalApplied Mechanics and Materials
dc.relation.ispartofvolume99-100
dc.rights.retentionY
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchInfrastructure engineering and asset management
dc.subject.fieldofresearchcode40
dc.subject.fieldofresearchcode400508
dc.titleModelling Long-term Bridge Deterioration at Structural Member Level Using Artificial Intelligence Techniques
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 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.
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorLoo, Yew-Chaye
gro.griffith.authorGuan, Hong


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