Journal
INFORMATION SCIENCES
Volume 566, Issue -, Pages 103-117Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.02.064
Keywords
Structural damage identification; Structural health monitoring (SHM); Convolutional neural network (CNN); Bidirectional gated recurrent unit& nbsp; (Bidirectional GRU); Deep learning
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This paper proposes a novel end-to-end structural damage detection neural model, utilizing the advantages of Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel to achieve better detection effect than existing methods.
With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for effective damage-intensive feature extraction and representation. To overcome such a problem, this paper proposes a novel end-to end structural damage detection neural model by taking the advantages of the Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel. The well-known IASC-ASCE benchmark and TCRF dataset are used for evaluation. The experimental results show that the proposed approach can achieve a better detecting effect than other existing manners. (c) 2021 Published by Elsevier Inc.
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