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Unsupervised deep learning approach for structural anomaly detection using probabilistic features

Author(s): ORCID (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China)
(College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China)
(College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China)
ORCID (Department of Structural Engineering, University of California, San Diego, La Jolla, CA, USA)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241226804
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: 10.1177/14759217241226804.
  • About this
    data sheet
  • Reference-ID
    10775655
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
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