Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/2329
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dc.contributor.authorKilic, G.-
dc.contributor.authorUnluturk, M.S.-
dc.date.accessioned2023-06-16T14:38:50Z-
dc.date.available2023-06-16T14:38:50Z-
dc.date.issued2016-
dc.identifier.issn0090-3973-
dc.identifier.urihttps://doi.org/10.1520/JTE20150057-
dc.description.abstractGround penetrating radar (GPR) uses data collected with the aid of electromagnetic waves transmitted into a structure by antenna to assess and monitor the structural health of many different kinds of civil infrastructure. With GPR technology promoting their system with promises of the achievement of in excess of 1000 sample points per scan, this research demonstrated on the basis of the Nyquist theorem that 256 sample points per scan provided equally reliable inspection results. Furthermore, 256 sample points per scan GPR data were further analyzed by order statistic filtering with neural networks to locate cracks within concrete materials. The results showed that the neural network order statistic filters are effective in their use of detecting cracks in noisy environments using 256 sample points per scan GPR data. Copyright © 2014 by ASTM.en_US
dc.language.isoenen_US
dc.publisherASTM Internationalen_US
dc.relation.ispartofJournal of Testing and Evaluationen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCracken_US
dc.subjectGpren_US
dc.subjectNeural Networken_US
dc.subjectNyquist Theoremen_US
dc.subjectStructural Healthen_US
dc.titleGpr Raw-Data Analysis To Detect Crack Using Order Statistic Filteringen_US
dc.typeArticleen_US
dc.identifier.doi10.1520/JTE20150057-
dc.identifier.scopus2-s2.0-84979642633-
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authoridKILIC, GOKHAN/0000-0001-6928-226X-
dc.authorscopusid40761843000-
dc.authorscopusid6508114835-
dc.identifier.volume44en_US
dc.identifier.issue3en_US
dc.identifier.startpage1319en_US
dc.identifier.endpage1328en_US
dc.identifier.wosWOS:000382229300032-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.identifier.wosqualityQ3-
item.openairetypeArticle-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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