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dc.contributor.convenorItalian Group of IABSE
dc.contributor.authorLee, J
dc.contributor.authorBlumenstein, M
dc.contributor.authorGuan, H
dc.contributor.authorLoo, YC
dc.contributor.editorIABSE
dc.date.accessioned2017-05-03T14:06:21Z
dc.date.available2017-05-03T14:06:21Z
dc.date.issued2010
dc.identifier.isbn9783857481222
dc.identifier.refurihttp://www.iabse.org/conferences/venice2010/index.php
dc.identifier.urihttp://hdl.handle.net/10072/37232
dc.description.abstractA bridge is principally designed to have a long service life. However, due to number factors, it could fail prematurely, and could cause loss of human life. In order to ensure the optimum bridge serviceability, systematic asset management is essential for effective decision-making of maintenance, repair and rehabilitation (MR&R). Systematic asset management can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major resource for predicting future bridge deterioration via BMSs. However, available historical condition ratings from most bridge agencies are very limited, thus posing a major barrier for predicting reliable future bridge performance. This paper presents the progressive research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques. The development is organised in three major steps: (1) generating unavailable past bridge element condition ratings using the Backward Prediction Model (BPM) - this helps to provide sufficient historical deterioration patterns for each element; (2) predicting long-term condition ratings based on the outcome of Step 1 using Time Delay Neural Networks (TDNNs); and (3) improving long-term prediction accuracy of Step 2 by employing Case-based Reasoning (CBR). This paper mainly focuses on the first two steps of the research. Promising results are reported for the reliable long-term prediction of bridge element performance.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent349451 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIABSE
dc.publisher.placeItaly
dc.publisher.urihttp://www.iabse.org/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename34th International Symposium on Bridge and Structural Engineering
dc.relation.ispartofconferencetitleLarge Structures and Infrastructures for Environmentally Constrained and Urbanised Areas
dc.relation.ispartofdatefrom2010-09-22
dc.relation.ispartofdateto2010-09-24
dc.relation.ispartoflocationVenice, Italy
dc.relation.ispartofpagefrom374
dc.relation.ispartofpageto375
dc.rights.retentionY
dc.subject.fieldofresearchModelling and simulation
dc.subject.fieldofresearchInfrastructure engineering and asset management
dc.subject.fieldofresearchcode460207
dc.subject.fieldofresearchcode400508
dc.titleLong-term Prediction of Bridge Element Performance Using Time Delay Neural Networks (TDNNs)
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2010 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.
gro.date.issued2015-06-02T05:40:40Z
gro.hasfulltextFull Text
gro.griffith.authorLoo, Yew-Chaye
gro.griffith.authorGuan, Hong


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

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