Full Text:   <1163>

Summary:  <734>

Suppl. Mater.: 

CLC number: U443; TP183

On-line Access: 2021-08-20

Received: 2020-09-17

Revision Accepted: 2021-01-24

Crosschecked: 2021-07-27

Cited: 0

Clicked: 2066

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Nhi Ngo-Kieu

https://orcid.org/0000-0001-9230-4308

Toan Pham-Bao

https://orcid.org/0000-0002-2105-2403

Tam Nguyen-Nhat

https://orcid.org/ 0000-0003-4201-7129

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.672-680

http://doi.org/10.1631/jzus.A2000414


Deep learning-based signal processing for evaluating energy dispersal in bridge structures


Author(s):  Nhi Ngo-Kieu, Thao Nguyen-Da, Toan Pham-Bao, Tam Nguyen-Nhat, Hung Nguyen-Xuan

Affiliation(s):  Laboratory of Applied Mechanics (LAM), Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam; more

Corresponding email(s):   ngx.hung@hutech.edu.vn

Key Words:  Structural health monitoring (SHM), Convolutional neural network (CNN), Deep learning, Bridge monitoring, Viscoelastic model, Material properties, Loss factor


Share this article to: More <<< Previous Article|

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.

评估桥梁结构能量扩散的基于深度学习的信号处理方法

目的:1. 使用基于振动的损伤检测方法进行结构健康监测.2. 基于材料力学性能评价提出一种新的结构健康监测方法.
创新点:1. 通过一个被称为损失函数(LF)的新指标描述材料粘弹性参数与振动参数之间的相关性.2. 使用卷积神经网络(CNN)提取自动特征和损坏敏感性,以评估结构状况.
方法:1. 测量真实桥梁的振动响应.2. 在频域中进行信号处理以揭示振动能量损失.3. 基于深度学习和CNN对桥梁状况进行分类.
结论:1. 在真实结构中总是会发生能量扩散.2. 基于振动能量损失变化的LF评估,可以对桥梁进行健康监测.3. 基于深度学习的能量扩散评估是可实现的,并且在多个实际桥梁中具有较高的可实施性.

关键词:结构健康监测;卷积神经网络;深度学习;桥梁监测;粘弹性模型;材料性能;损耗因子

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Anitescu C, Atroshchenko E, Alajlan N, et al., 2019. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 59(1):345-359.

[2]Gao YQ, Mosalam KM, 2018. Deep transfer learning for image-based structural damage recognition. Computer-aided Civil and Infrastructure Engineering, 33(9):748-768.

[3]Guo HW, Zhuang XY, Rabczuk T, 2019. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 59(2):433-456.

[4]Nguyen TD, Nguyen HQ, Pham TB, et al., 2021. A novel proposal in using viscoelastic model for bridge condition assessment. In: Bui TQ, Cuong LT, Khatir S (Eds.), Structural Health Monitoring and Engineering Structures. Springer, Singapore.

[5]Samaniego E, Anitescu C, Goswami S, et al., 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362:112790.

[6]Wang ZL, Cha YJ, 2021. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Structural Health Monitoring, 20(1):406-425.

[7]Yu YJ, Cao H, Yan XY, et al., 2020. Defect identification of wind turbine blades based on defect semantic features with transfer feature extractor. Neurocomputing, 376:1-9.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE