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dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.editorIABSEen_US
dc.date.accessioned2017-05-03T14:06:19Z
dc.date.available2017-05-03T14:06:19Z
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10072/24101
dc.description.abstractComputer-aided Bridge Management Systems (BMSs) as Decision Support Systems (DSSs) for an effective bridge asset management are used to establish the feasible bridge maintenance, repair and rehabilitation (MR&R) strategies which ensure an adequate level of safety at the lowest possible bridge life-cycle cost. To achieve this goal, keeping up-to-date bridge condition ratings are crucial for a BMS software package. Although most bridge agencies in the past have conducted inspections and maintenance, the form of such bridge inspection records is dissimilar to those required by BMSs. These data inconsistencies inevitably slow down the BMS implementations. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating unavailable years of 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. This paper covers a brief description of the BPM methodology and presents nine case studies. The outcome of this study can help establish a comprehensive condition rating database, which will in turn assist to predict reliable future bridge depreciations.en_US
dc.description.publicationstatusYesen_US
dc.format.extent277430 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherNo data provideden_US
dc.publisher.urihttp://www.iabse.org/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename17th Congress of IABSEen_US
dc.relation.ispartofconferencetitleCreating and Renewing Urban Structuresen_US
dc.relation.ispartofdatefrom2008-09-17en_US
dc.relation.ispartofdateto2008-09-19en_US
dc.relation.ispartoflocationChicago, USAen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleAn ANN-Based Backward Prediction Model for Reliable Bridge Management System Implementations Using Limited Inspection Records – Case Studies.en_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.rights.copyrightCopyright 2008 IABSE. The attached file is posted here in accordance with the copyright policy of the publisher, for your personal use only. No further distribution permitted. Use hypertext link for access to publisher's website. This article was first published in the 17th Congress Report of IABSE - Chicago 2008 on Creating and Renewing Urban Structures - Tall Buildings, Bridges and Infrastructure (www.iabse.org/publications/congressreorts/17cong.php).en_US
gro.date.issued2015-06-02T05:40:33Z
gro.hasfulltextFull Text


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

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