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dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorLe, Khoaen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.editorChina Academy of Transportation Sciencesen_US
dc.date.accessioned2017-05-03T14:06:18Z
dc.date.available2017-05-03T14:06:18Z
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10072/24100
dc.description.abstractThe total expenditure for bridge maintenance, repair and rehabilitation (MR&R) and bridge asset extension increases rapidly every year in Australia. Computer-aided Bridge Management Systems (BMSs) are used to establish the best possible bridge MR&R strategies which ensure an adequate level of safety at the lowest possible bridge life-cycle cost. To achieve this, keeping up-to-date bridge information is crucial for a BMS software package. Although most bridge agencies in the past have conducted inspections and maintenance, the format of such bridge inspection records is dissimilar to those required by BMSs. These data inconsistencies inhibit correct BMS implementations. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating historical bridge condition ratings using very limited existing inspection records. The BPM employed historical non-bridge datasets such as traffic volumes, populations and climates, to establish correlations with the existing bridge condition ratings from the very limited bridge inspection records. Such correlations can help fill the condition rating gaps required for an effective and accurate BMS implementation. The outcome of this study can contribute to minimising BMS operational problems due to limited inspection records.en_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherChina Communication Audio & Electronic Pressen_US
dc.publisher.placeChinaen_US
dc.publisher.urihttp://www.civil.uminho.pt/ismarti/inicio.htmen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameICTI2008en_US
dc.relation.ispartofconferencetitleThe International Conference on Transport Infrastructure (ICTI2008)en_US
dc.relation.ispartofdatefrom2008-04-24en_US
dc.relation.ispartofdateto2008-04-26en_US
dc.relation.ispartoflocationBeijing, Chinaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleANN-Based Bridge Condition Rating Models Using Limited Structural Inspection Recordsen_US
dc.typeConference outputen_US
dc.type.descriptionE2 - Conference Publications (Non HERDC Eligible)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2015-06-02T05:44:11Z
gro.hasfulltextNo Full Text


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

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