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Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network

Author(s): ORCID (MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu, China)
(MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu, China)
(MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu, China)
(Chengdu Fourth Construction Engineering of CDCEG, Chengdu, China)
(Chengdu Fourth Construction Engineering of CDCEG, Chengdu, China)
ORCID (MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu, China)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241279095
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: 10.1177/14759217241279095.
  • About this
    data sheet
  • Reference-ID
    10806181
  • Published on:
    10/11/2024
  • Last updated on:
    10/11/2024
 
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