Show simple item record

dc.contributor.convenorIABSEen_US
dc.contributor.authorSon, Jungen_US
dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorPanuwatwanich, Kriengsaken_US
dc.contributor.editorIABSEen_US
dc.date.accessioned2017-05-03T14:06:20Z
dc.date.available2017-05-03T14:06:20Z
dc.date.issued2009en_US
dc.identifier.refuriwww.iabse2009.comen_US
dc.identifier.urihttp://hdl.handle.net/10072/29869
dc.description.abstractBridge Management Systems (BMSs) have been developed since the early 1990s as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. However, available historical condition ratings are very limited in all bridge agencies. This constitutes the major barrier for predicting reliably future structural performances. To alleviate this problem, the Backward Prediction Model (BPM) technique for generating the missing historical condition ratings has been developed, and its reliability has been verified using existing condition ratings available from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and non-bridge factors including climate, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. Based on these generated datasets, the currently available bridge deterioration model can be used to predict future bridge conditions. The existing 4 National Bridge Inventory and 9 BPM-generated historical condition ratings are used as input data to compare the prediction accuracy using deterministic bridge deterioration models. The comparison results show that prediction error decreases as more historical data become available. This suggests that the BPM can be used to generate additional historical condition ratings, which is essential for bridge deterioration models to achieve more accurate prediction results. However, there are still significant limitations identified in the current bridge deterioration models. Hence, further research is necessary to improve the prediction accuracy of bridge deterioration models.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent654694 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherIABSEen_US
dc.publisher.placeThailanden_US
dc.publisher.urihttp://www.iabse.orgen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameIABSE Symposium 2009en_US
dc.relation.ispartofconferencetitleSustainable Infrastructure: Environment Friendly, Safe and Resource Efficienten_US
dc.relation.ispartofdatefrom2009-09-09en_US
dc.relation.ispartofdateto2009-09-11en_US
dc.relation.ispartoflocationBangkok, Thailanden_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleGenerating Historical Condition Ratings for the Reliable Prediction of Bridge Deteriorationsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2009 IASBE. 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 conference website.en_US
gro.date.issued2015-06-02T05:41:58Z
gro.hasfulltextFull Text


Files in this item

This item appears in the following Collection(s)

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

Show simple item record