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dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorLe, Khoaen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.editorSyed M.Ahmed, Salman Azhar and Sherif Mohameden_US
dc.date.accessioned2017-05-03T13:03:22Z
dc.date.available2017-05-03T13:03:22Z
dc.date.issued2007en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent178713 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherFlorida International Universityen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://www.fiu.edu/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameFourth International Conference on Construction in the 21st Century (CITC-IV)en_US
dc.relation.ispartofconferencetitleProceedings of the Fourth International Conference on Construction in the 21st Century : Accelerating Innovation in Engineering, Management and Technologyen_US
dc.relation.ispartofdatefrom2007-07-11en_US
dc.relation.ispartofdateto2007-07-13en_US
dc.relation.ispartoflocationGold Coast, Australiaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode290804en_US
dc.titleDevelopment of a Backward Prediction Model Based on Limited Historical Datasetsen_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 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.en_US
gro.date.issued2015-06-02T05:43:23Z
gro.hasfulltextFull Text


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

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