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
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Zusammenfassung:	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 processing real-world monitoring data to reflect damage. Typical challenges include: (i) the need for a large dataset derived from a vast number of labeled damaged cases, which are extremely difficult to obtain from real-world monitoring data, experiments, and numerical simulations; (ii) the inability of available damage sample spectra to fully capture the damage information underlying the dynamic responses of bridges; (iii) the likelihood of requiring manual intervention to discriminate damage from the outputs of deep learning models, which is inefficient when dealing with massive amounts of monitoring data. To address these challenges, this study proposes an intelligent unsupervised deep learning framework for damage identification in bridges. The framework is characterized by three innovative technical elements: (1) a Deep Convolutional Autoencoder (CAE) model with a hybrid loss function is developed to provide an intelligent system that can identify bridge damage without requiring a large dataset of damaged cases; (2) Wavelet transmissibility pattern spectra are established to characterize damage information embedded in dynamic responses in a more efficient manner; and (3) an Ordering Points To Identify the Clustering Structure (OPTICS)-based damage picker is proposed to achieve automatic discrimination of damage cases. The feasibility of this framework is numerically demonstrated through the detection of damage in a curved bridge, and its effectiveness is experimentally validated on a laboratory-scale suspension bridge with induced damage. The results indicate that the proposed framework can automatically and accurately identify damage in bridges. This framework provides a solution for intelligent and data-driven bridge damage detection.
ISSN:	0888-3270
DOI:	10.1016/j.ymssp.2024.111653