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Fully automated natural frequency identification based on deep-learning-enhanced computer vision and power spectral density transmissibility

Author(s): ORCID (Department of Civil Engineering, Xiamen University, Xiamen, China)
(Department of Civil Engineering, Xiamen University, Xiamen, China)
(Department of Civil Engineering, Xiamen University, Xiamen, China)
(State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau, China)
ORCID (Department of Civil Engineering, Xiamen University, Xiamen, China)
(Xiamen Municipal Baicheng Construction & Investment Co. Ltd, Xiamen, China)
Medium: journal article
Language(s): English
Published in: Advances in Structural Engineering, , n. 13, v. 25
Page(s): 136943322211075
DOI: 10.1177/13694332221107572
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: 10.1177/13694332221107572.
  • About this
    data sheet
  • Reference-ID
    10678383
  • Published on:
    18/06/2022
  • Last updated on:
    19/04/2023
 
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