| dc.contributor.author | Li, Xiaofeng | |
| dc.contributor.author | Liu, Hai | |
| dc.contributor.author | Zhou, Feng | |
| dc.contributor.author | Chen, Zhongchang | |
| dc.contributor.author | Giannakis, Iraklis | |
| dc.contributor.author | Slob, Evert | |
| dc.date.accessioned | 2022-11-26T00:07:46Z | |
| dc.date.available | 2022-11-26T00:07:46Z | |
| dc.date.issued | 2021-11-26 | |
| dc.identifier.citation | Li , X , Liu , H , Zhou , F , Chen , Z , Giannakis , I & Slob , E 2021 , ' Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data ' , Computer-Aided Civil and Infrastructure Engineering . https://doi.org/10.1111/mice.12798 | en |
| dc.identifier.issn | 1093-9687 | |
| dc.identifier.other | PURE: 209762194 | |
| dc.identifier.other | PURE UUID: c63bae2c-eb19-42d9-ba07-dcdd93dc4126 | |
| dc.identifier.other | Scopus: 85119989361 | |
| dc.identifier.uri | http://aura-test.abdn.ac.uk/handle/2164/19303 | |
| dc.description | Funding 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.extent | 20 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Computer-Aided Civil and Infrastructure Engineering | en |
| dc.rights | This is the peer reviewed version of the article which has been published in final form at https://doi.org/10.1111/mice.12798. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | en |
| dc.subject | TA Engineering (General). Civil engineering (General) | en |
| dc.subject | Civil and Structural Engineering | en |
| dc.subject | Computer Science Applications | en |
| dc.subject | Computer Graphics and Computer-Aided Design | en |
| dc.subject | Computational Theory and Mathematics | en |
| dc.subject.lcc | TA | en |
| dc.title | Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data | en |
| dc.type | Journal article | en |
| dc.contributor.institution | University of Aberdeen.Geology and Geophysics | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Postprint | en |
| dc.identifier.doi | https://doi.org/10.1111/mice.12798 | |
| dc.date.embargoedUntil | 2022-11-26 | |
| dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85119989361&partnerID=8YFLogxK | en |
