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dc.contributor.authorLee, Jaeho
dc.contributor.authorGuan, Hong
dc.contributor.authorLoo, Yew-Chaye
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2018-06-28T23:41:46Z
dc.date.available2018-06-28T23:41:46Z
dc.date.issued2014
dc.identifier.issn1076-0342
dc.identifier.doi10.1061/(ASCE)IS.1943-555X.0000197
dc.identifier.urihttp://hdl.handle.net/10072/66341
dc.description.abstractA reliable deterioration model is essential in bridge asset management. Most deterioration modeling requires a large amount of well-distributed condition rating data along with all bridge ages to calculate the probability of condition rating deterioration. This means that the model can only function properly when a full set of data is available. To overcome this shortcoming, an improved artificial intelligence (AI)-based model is presented in this study to effectively predict long-term deterioration of bridge elements. The model has four major components: (1) categorizing bridge element condition ratings; (2) using the neural network-based backward prediction model (BPM) to generate unavailable historical condition ratings for applicable bridge elements; (3) training by an Elman neural network (ENN) for identifying historical deterioration patterns; and (4) using the ENN to predict long-term performance. The model has been tested using bridge inspection records that demonstrate satisfactory results. This study primarily focuses on the establishment of a new methodology to address the research problems identified. A series of case studies, hence, need to follow to ensure the method is appropriately developed and validated.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAmerican Society of Civil Engineers
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom04014013-1
dc.relation.ispartofpageto04014013-10
dc.relation.ispartofissue3
dc.relation.ispartofjournalJournal of Infrastructure Systems
dc.relation.ispartofvolume20
dc.rights.retentionY
dc.subject.fieldofresearchStructural Engineering
dc.subject.fieldofresearchCivil Engineering
dc.subject.fieldofresearchcode090506
dc.subject.fieldofresearchcode0905
dc.titleDevelopment of a Long-term Bridge Element Performance Model Using Elman Neural Networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorLoo, Yew-Chaye
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorGuan, Hong


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