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CLC number: TU391

On-line Access: 2021-06-21

Received: 2020-07-18

Revision Accepted: 2020-10-18

Crosschecked: 2021-05-18

Cited: 0

Clicked: 2114

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Magd Abdel Wahab

https://orcid.org/0000-0002-3610-865X

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.6 P.467-480

http://doi.org/10.1631/jzus.A2000316


Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm


Author(s):  Long Viet Ho, Duong Huong Nguyen, Guido de Roeck, Thanh Bui-Tien, Magd Abdel Wahab

Affiliation(s):  Soete Laboratory, Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Faculty of Engineering and Architecture, Ghent University, Gent 9000, Belgium; more

Corresponding email(s):   HoViet.Long@ugent.be, huongduong.nguyen@ugent.be, guido.deroeck@kuleuven.be, btthanh@utc.edu.vn, magd.abdelwahab@tdtu.edu.vn

Key Words:  Feedforward neural network-particle swarm optimization and gravitational search algorithm (FNN-PSOGSA), Modal damage indices, Damage detection, Hybrid algorithm PSOGSA


Long Viet Ho, Duong Huong Nguyen, Guido de Roeck, Thanh Bui-Tien, Magd Abdel Wahab. Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm[J]. Journal of Zhejiang University Science A, 2021, 22(6): 467-480.

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author="Long Viet Ho, Duong Huong Nguyen, Guido de Roeck, Thanh Bui-Tien, Magd Abdel Wahab",
journal="Journal of Zhejiang University Science A",
volume="22",
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pages="467-480",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000316"
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%T Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm
%A Long Viet Ho
%A Duong Huong Nguyen
%A Guido de Roeck
%A Thanh Bui-Tien
%A Magd Abdel Wahab
%J Journal of Zhejiang University SCIENCE A
%V 22
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%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000316

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T1 - Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm
A1 - Long Viet Ho
A1 - Duong Huong Nguyen
A1 - Guido de Roeck
A1 - Thanh Bui-Tien
A1 - Magd Abdel Wahab
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 6
SP - 467
EP - 480
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000316


Abstract: 
Over recent decades, the artificial neural networks (ANNs) have been applied as an effective approach for detecting damage in construction materials. However, to achieve a superior result of defect identification, they have to overcome some shortcomings, for instance slow convergence or stagnancy in local minima. Therefore, optimization algorithms with a global search ability are used to enhance ANNs, i.e. to increase the rate of convergence and to reach a global minimum. This paper introduces a two-stage approach for failure identification in a steel beam. In the first step, the presence of defects and their positions are identified by modal indices. In the second step, a feedforward neural network, improved by a hybrid particle swarm optimization and gravitational search algorithm, namely FNN-PSOGSA, is used to quantify the severity of damage. Finite element (FE) models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method. For comparison, a traditional ANN is also used to estimate the severity of the damage. The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

使用前馈神经网络结合混合粒子群优化和引力搜索算法对钢板进行损伤检测

目的:使用模态损伤指数建立一个简单、有效的结构健康监测评估工具,并对钢板进行数值研究,以确认该方法的可行性.
创新点:为使研究可应用于实际结构,本文放弃了目前的大量研究中的刚度折减假设,并在有限元模型中模拟了钢板的切割,以代表实际结构的失效.
方法:1. 一个有名的混合优化算法,即粒子群优化-引力搜索算法(PSOGSA),被用于优化前馈神经网络(FNN)的连接权重和偏差,以增强其训练性能.2. 模型的输入变量为由柔度矩阵变化推导出的两个损伤指数,而输出变量则是损伤严重程度.3. 预测值和目标值之间的均方误差(MSE)是优化过程的适应度函数.
结论:1. 随机的FNN-PSOGSA方法获得了比传统人工神经网络(ANN)更好的损伤量化结果;其在两种破坏场景下目标和估计之间的严重性差异分别为−0.06%和0.89%,而在ANN中为−1.91%和1.01%.2. 所提出的方法可以在损伤指数和相应的严重程度之间建立联系,而如果仅使用损伤指数则无法观察到该联系.3. FNN-PSOGSA方法的准确性和易实施性说明它具有作为真实结构损伤评估工具的潜力.

关键词:FNN-PSOGSA;模态损伤指数;损伤检测;混合算法PSOGSA

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

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