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Deep CNN-based semi-supervised learning approach for identifying and segmenting corrosion in hydraulic steel and water resources infrastructure

Author(s): ORCID (University of Illinois Urbana-Champaign, Urbana, IL, USA)
(Construction Engineering Research Laboratory, Engineer Research and Development Center, Champaign, IL, USA)
(Construction Engineering Research Laboratory, Engineer Research and Development Center, Champaign, IL, USA)
ORCID (Construction Engineering Research Laboratory, Engineer Research and Development Center, Champaign, IL, USA)
(University of Illinois Urbana-Champaign, Urbana, IL, USA)
(Construction Engineering Research Laboratory, Engineer Research and Development Center, Champaign, IL, USA)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241305039
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: 10.1177/14759217241305039.
  • About this
    data sheet
  • Reference-ID
    10816779
  • Published on:
    03/02/2025
  • Last updated on:
    03/02/2025
 
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