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dc.contributor.authorLee, Jaeho
dc.contributor.authorLe, Khoa
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorLoo, Yew-Chaye
dc.contributor.editorSyed M.Ahmed, Salman Azhar and Sherif Mohamed
dc.date.accessioned2017-05-03T13:03:22Z
dc.date.available2017-05-03T13:03:22Z
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/10072/17627
dc.description.abstractIn almost the last two decades, commercial Bridge Management System (BMS) packages have been remarkably developed. However, inconsistency between BMS inputs and bridge agencies' existing data is an obstacle to implement and to operate a BMS software application. A large number of bridge datasets for a BMS database is an essential requirement to analyze a bridge network. Among many requirements, historical structural datasets are vital to compute the prioritization of bridge stock for maintenance and repair activities and are mostly unavailable for bridges of more than 20 years in age. This study focuses on the abovementioned difficulty to overcome the lacking historical data problem faced by bridge agencies to effectively use BMS applications. This paper proposes an artificial neural network (ANN) technique to predict missing components of time-series datasets to estimate historical bridge element condition ratings. Although this study only estimates historical condition ratings, the proposed concept can be used to compute other historical dataset requirements in the BMS database and hence improving the reliability of various BMS analysis modules.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent178713 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherFlorida International University
dc.publisher.placeUSA
dc.publisher.urihttp://www.fiu.edu/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameFourth International Conference on Construction in the 21st Century (CITC-IV)
dc.relation.ispartofconferencetitleProceedings of the Fourth International Conference on Construction in the 21st Century : Accelerating Innovation in Engineering, Management and Technology
dc.relation.ispartofdatefrom2007-07-11
dc.relation.ispartofdateto2007-07-13
dc.relation.ispartoflocationGold Coast, Australia
dc.rights.retentionY
dc.subject.fieldofresearchcode290804
dc.titleDevelopment of a Backward Prediction Model Based on Limited Historical Datasets
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2007 CITC-IV, USA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.date.issued2015-06-02T05:43:23Z
gro.hasfulltextFull Text
gro.griffith.authorLoo, Yew-Chaye
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorLee, Jaeho
gro.griffith.authorLe, Khoa N.


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

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