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Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data

dc.contributor.authorLi, Xiaofeng
dc.contributor.authorLiu, Hai
dc.contributor.authorZhou, Feng
dc.contributor.authorChen, Zhongchang
dc.contributor.authorGiannakis, Iraklis
dc.contributor.authorSlob, Evert
dc.date.accessioned2023-06-24T23:09:52Z
dc.date.available2023-06-24T23:09:52Z
dc.date.issued2022-11-15
dc.identifier209762194
dc.identifierc63bae2c-eb19-42d9-ba07-dcdd93dc4126
dc.identifier85119989361
dc.identifier.citationLi , X , Liu , H , Zhou , F , Chen , Z , Giannakis , I & Slob , E 2022 , ' Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data ' , Computer-Aided Civil and Infrastructure Engineering , vol. 37 , no. 14 , pp. 1834-1853 . https://doi.org/10.1111/mice.12798en
dc.identifier.issn1093-9687
dc.identifier.otherORCID: /0000-0002-7672-8992/work/147566860
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/19620
dc.descriptionFunding Information: The research was funded by the National Natural Science Foundation of China (41974165, 42111530126) and Hubei Key Laboratory of Intelligent Geo‐Information Processing (KLIGIP‐2018A2). The authors thank Zhiwei Duan and Xuefeng Yin for their contributions in the initial stage of the work, and the editor and anonymous reviewers for their constructive comments and suggestions to improve the quality of the paper.en
dc.format.extent20
dc.format.extent7697336
dc.language.isoeng
dc.relation.ispartofComputer-Aided Civil and Infrastructure Engineeringen
dc.subjectTA Engineering (General). Civil engineering (General)en
dc.subjectCivil and Structural Engineeringen
dc.subjectComputer Science Applicationsen
dc.subjectComputer Graphics and Computer-Aided Designen
dc.subjectComputational Theory and Mathematicsen
dc.subject.lccTAen
dc.titleDeep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction dataen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Geology and Geophysicsen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1111/mice.12798
dc.date.embargoedUntil2022-11-26
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85119989361&partnerID=8YFLogxKen
dc.identifier.vol37en
dc.identifier.iss14en


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