CLC number: U443; TP183
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-07-27
Cited: 0
Clicked: 4369
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0001-9230-4308
Nhi Ngo-Kieu, Thao Nguyen-Da, Toan Pham-Bao, Tam Nguyen-Nhat, Hung Nguyen-Xuan. Deep learning-based signal processing for evaluating energy dispersal in bridge structures[J]. Journal of Zhejiang University Science A, 2021, 22(8): 672-680.
@article{title="Deep learning-based signal processing for evaluating energy dispersal in bridge structures",
author="Nhi Ngo-Kieu, Thao Nguyen-Da, Toan Pham-Bao, Tam Nguyen-Nhat, Hung Nguyen-Xuan",
journal="Journal of Zhejiang University Science A",
volume="22",
number="8",
pages="672-680",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000414"
}
%0 Journal Article
%T Deep learning-based signal processing for evaluating energy dispersal in bridge structures
%A Nhi Ngo-Kieu
%A Thao Nguyen-Da
%A Toan Pham-Bao
%A Tam Nguyen-Nhat
%A Hung Nguyen-Xuan
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 672-680
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000414
TY - JOUR
T1 - Deep learning-based signal processing for evaluating energy dispersal in bridge structures
A1 - Nhi Ngo-Kieu
A1 - Thao Nguyen-Da
A1 - Toan Pham-Bao
A1 - Tam Nguyen-Nhat
A1 - Hung Nguyen-Xuan
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 8
SP - 672
EP - 680
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000414
Abstract: In this paper, we use deep learning to investigate a loss factor function (LF) for measuring energy dispersal in bridge structures in Ho Chi Minh City, Vietnam. The LF is calculated from the power spectral density (PSD) of random vibration signals to account for the mechanical parameters required for detecting structural changes. The LF is applied to many different types of bridge decks such as a prestressed concrete bridge, precast reinforced concrete bridge, and cable-stayed bridge. In addition, to ensure the new parameters are working effectively for the evaluation, a deep learning-based signal processing platform is used along with a convolutional neural network (CNN) to create the training. The training process helps eliminate interference values and errors. This demonstrates that the LF is sensitive to many different real-life structures while previous parameters are sensitive to only particular structures.
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