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Data-driven time-variant reliability assessment of bridge girders based on deep learning

Auteur(s): ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
ORCID (School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Médium: article de revue
Langue(s): anglais
Publié dans: Mechanics of Advanced Materials and Structures
Page(s): 1-13
DOI: 10.1080/15376494.2023.2253548
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1080/15376494.2023.2253548.
  • Informations
    sur cette fiche
  • Reference-ID
    10776773
  • Publié(e) le:
    12.05.2024
  • Modifié(e) le:
    12.05.2024
 
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