CLC number: TU391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-05-18
Cited: 0
Clicked: 5171
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.
@article{title="Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm",
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",
number="6",
pages="467-480",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000316"
}
%0 Journal Article
%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
%N 6
%P 467-480
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000316
TY - JOUR
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
ER -
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.
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