Full Text:   <56>

CLC number: 

On-line Access: 2020-12-17

Received: 2020-07-18

Revision Accepted: 2020-10-18

Crosschecked: 0000-00-00

Cited: 0

Clicked: 128

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 1998 Vol.-1 No.-1 P.

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, 9000 Gent, 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:  FNN-PSOGSA, Modal damage indices, Damage detection, PSO, GSA, 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, 1998, -1(-1): .

@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="-1",
number="-1",
pages="",
year="1998",
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 -1
%N -1
%P
%@ 1673-565X
%D 1998
%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 - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000316


Abstract: 
Over recent decades, artificial neural networks 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 artificial neural networks, 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 PSOGSA%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>FNN-PSOGSA, is used to quantify the severity of damage. FE models of the beam in 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.

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

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