Intelligent framework for unsupervised damage detection in bridges using deep convolutional autoencoder with wavelet transmissibility pattern spectra

Deep Learning has been increasingly utilized in structural damage detection. Existing relevant studies often highlight the benefits of supervised deep learning in the intelligent identification of bridge damage. Notably, however, supervised deep learning methods encounter specific challenges in proc...

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Veröffentlicht in:	Mechanical systems and signal processing Jg. 220; S. 111653
Hauptverfasser:	Li, Shuai, Cao, Yuxi, Gdoutos, Emmanuel E., Tao, Mei, Faisal Alkayem, Nizar, Avci, Onur, Cao, Maosen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2024
Schlagworte:	Bridge health monitoring Damage detection Deep convolutional autoencoder (CAE) Deep learning machine Monitoring data OPTICS clustering Wavelet transmissibility pattern spectra (WTPSs) Monitoring data Wavelet transmissibility pattern spectra (WTPSs) Damage detection Bridge health monitoring Deep learning machine Deep convolutional autoencoder (CAE) OPTICS clustering
ISSN:	0888-3270
Online-Zugang:	Volltext
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