Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27603
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorZhou, XTen_US
dc.creatorNi, YQen_US
dc.creatorZhang, FLen_US
dc.date.accessioned2015-05-26T08:17:10Z-
dc.date.available2015-05-26T08:17:10Z-
dc.identifier.issn1024-123Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/27603-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCopyright © 2014 X. T. Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following article: Zhou, X. T., Ni, Y. Q., & Zhang, F. L. (2014). Damage localization of cable-supported bridges using modal frequency data and probabilistic neural network. Mathematical Problems in Engineering, 2014, is available at https//doi.org/10.1155/2014/837963en_US
dc.titleDamage localization of cable-supported bridges using modal frequency data and probabilistic neural networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage10en_US
dc.identifier.volume2014en_US
dc.identifier.doi10.1155/2014/837963en_US
dcterms.abstractThis paper presents an investigation on using the probabilistic neural network (PNN) for damage localization in the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) from simulated noisy modal data. Because the PNN approach describes measurement data in a Bayesian probabilistic framework, it is promising for structural damage detection in noisy conditions. For locating damage on the TMB deck, the main span of the TMB is divided into a number of segments, and damage to the deck members in a segment is classified as one pattern class. The characteristic ensembles (training samples) for each pattern class are obtained by computing the modal frequency change ratios from a 3D finite element model (FEM) when incurring damage at different members of the same segment and then corrupting the analytical results with random noise. The testing samples for damage localization are obtained in a similar way except that damage is generated at locations different from the training samples. For damage region/type identification of the TKB, a series of pattern classes are defined to depict different scenarios with damage occurring at different portions/components. Research efforts have been focused on evaluating the influence of measurement noise level on the identification accuracy.-
dcterms.accessRightsopen access-
dcterms.bibliographicCitationMathematical problems in engineering, 2014, v. 2014, 837963, p. 1-10en_US
dcterms.isPartOfMathematical problems in engineeringen_US
dcterms.issued2014-
dc.identifier.isiWOS:000337454000001-
dc.identifier.scopus2-s2.0-84903649788-
dc.identifier.eissn1563-5147en_US
dc.identifier.rosgroupidr70352-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaVersion of Record-
dc.identifier.FolderNumbera0744-n33-
dc.identifier.SubFormID1405-
dc.description.fundingSourceRGC-
dc.description.fundingSourceOthers-
dc.description.fundingTextRGC: PolyU 5224/13E-
dc.description.fundingTextOthers: P0020027-
dc.description.pubStatusPublished-
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