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Anomaly detection for bridge health monitoring data based on multiple encoded images and convolutional neural network

Author(s): ORCID (School of Civil Engineering and Architecture, Guangxi University, Nanning, China)
(School of Civil Engineering and Architecture, Guangxi University, Nanning, China)
(China Railway Construction Investment Group Corporation Limited, Beijing, China)
(School of Civil Engineering and Architecture, Guangxi University, Nanning, China)
ORCID (School of Civil Engineering and Architecture, Guangxi University, Nanning, China)
Medium: journal article
Language(s): English
Published in: Structure and Infrastructure Engineering
Page(s): 1-16
DOI: 10.1080/15732479.2024.2421349
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: 10.1080/15732479.2024.2421349.
  • About this
    data sheet
  • Reference-ID
    10801856
  • Published on:
    10/11/2024
  • Last updated on:
    10/11/2024
 
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