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dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorKamalarasa, Kamalarasaen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.editorM.J. Skibniewskien_US
dc.date.accessioned2017-05-03T14:06:17Z
dc.date.available2017-05-03T14:06:17Z
dc.date.issued2008en_US
dc.identifier.issn09265805en_US
dc.identifier.doi10.1016/j.autcon.2008.02.008en_US
dc.identifier.urihttp://hdl.handle.net/10072/23582
dc.description.abstractThe slow adoption of Bridge Management Systems (BMSs) and its impractical future prediction of the condition rating of bridges are attributed to the inconsistency between BMS inputs and bridge agencies' existing data for a BMS in terms of compatibility and the enormous number of bridge datasets that include historical structural information. Among these, historical bridge element condition ratings are some of the key pieces of information required for bridge asset prioritisation but in most cases only limited data is available. This study addresses the abovementioned difficulties faced by bridge management agencies by using limited historical bridge inspection records to model time-series element-level data. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating historical bridge condition ratings using limited bridge inspection records. The BPM employs historical non-bridge datasets such as traffic volumes, populations and climates, to establish correlations with existing bridge condition ratings from very limited bridge inspection records. The resulting model predicts the missing historical condition ratings of individual bridge elements. The outcome of this study can contribute to reducing the uncertainty in predicting future bridge condition ratings and so improve the reliability of various BMS analysis outcomes.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent563717 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeUKen_US
dc.relation.ispartofstudentpublicationYen_US
dc.relation.ispartofpagefrom758en_US
dc.relation.ispartofpageto772en_US
dc.relation.ispartofissue6en_US
dc.relation.ispartofjournalAutomation in Constructionen_US
dc.relation.ispartofvolume17en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleImproving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM)en_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 2008 Elsevier. 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.issued2015-06-02T05:40:29Z
gro.hasfulltextFull Text


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