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Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

Auteur(s): (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
ORCID (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
(Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
ORCID (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing, China)
(Research Institute of Structural Engineering and Disaster Reduction, College of Civil Engineering, Tongji University, Shanghai, China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , n. 3, v. 21
Page(s): 147592172110219
DOI: 10.1177/14759217211021942
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/14759217211021942.
  • Informations
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  • Reference-ID
    10610485
  • Publié(e) le:
    08.06.2021
  • Modifié(e) le:
    09.05.2022
 
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