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A deep learning framework based on improved self‐supervised learning for ground‐penetrating radar tunnel lining inspection

Author(s): (School of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan China)
(School of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan China)
(School of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan China)
(School of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan China)
(China Railway Southwest Research Institute Co. LTD Chengdu China)
(China Railway Southwest Research Institute Co. LTD Chengdu China)
(Remote Sensing Laboratory Bauman Moscow State Technical University Moscow Russia)
(School of Geosciences University of Aberdeen Aberdeen UK)
(Norwegian Geotechnical Institute Oslo Norway)
(Department of Geoscience and Engineering Delft University of Technology Delft The Netherlands)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering, , n. 6, v. 39
Page(s): 814-833
DOI: 10.1111/mice.13042
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1111/mice.13042.
  • About this
    data sheet
  • Reference-ID
    10725624
  • Published on:
    30/05/2023
  • Last updated on:
    20/09/2024
 
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