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Faulty data detection and classification for bridge structural health monitoring via statistical and deep‐learning approach

Author(s): ORCID (State Key Laboratory for Disaster Reduction in Civil Engineering Tongji University Shanghai China)
(State Key Laboratory for Disaster Reduction in Civil Engineering Tongji University Shanghai China)
(Department of Bridge Engineering Tongji University Shanghai China)
(State Key Laboratory for Disaster Reduction in Civil Engineering Tongji University Shanghai China)
Medium: journal article
Language(s): English
Published in: Structural Control and Health Monitoring, , n. 11, v. 28
DOI: 10.1002/stc.2824
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: 10.1002/stc.2824.
  • About this
    data sheet
  • Reference-ID
    10624281
  • Published on:
    26/08/2021
  • Last updated on:
    22/10/2021
 
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