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An unsupervised context-free forecasting method for structural health monitoring by generative adversarial networks with progressive growing and self-attention

Author(s): (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
(Department of Civil Engineering, University of British Columbia, Vancouver, BC, Canada)
ORCID (School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China)
ORCID (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
ORCID (Department of Disaster Mitigation for Structures, Tongji University, Shanghai, China)
(Department of Disaster Mitigation for Structures, Tongji University, Shanghai, China)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241269702
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: 10.1177/14759217241269702.
  • About this
    data sheet
  • Reference-ID
    10797474
  • Published on:
    01/09/2024
  • Last updated on:
    01/09/2024
 
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