Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Optimization Method of Wavelet Neural Network for Suspension Bridge Damage Identification

Authors
Deqing Guan, Jie Li, Jun Chen
Corresponding Author
Deqing Guan
Available Online November 2016.
DOI
10.2991/aiie-16.2016.45How to use a DOI?
Keywords
wavelet transform; neural network; particle swarm optimization; damage identification; suspension bridge
Abstract

In this paper, an optimization method of wavelet neural network for structure damage identification is established. The suspension bridge is used as the research object. Firstly, wavelet coefficients modulus maxima are obtained by wavelet transform to determine the location of structural damage. Then, the connection weights and thresholds of the neural network are optimized by the particle swarm optimization, the optimized neural network is constructed to determine the degree of damage of the bridge. The validity of the method is verified by numerical simulation of multi span suspension bridge.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
10.2991/aiie-16.2016.45
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.45How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Deqing Guan
AU  - Jie Li
AU  - Jun Chen
PY  - 2016/11
DA  - 2016/11
TI  - Optimization Method of Wavelet Neural Network for Suspension Bridge Damage Identification
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 194
EP  - 197
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.45
DO  - 10.2991/aiie-16.2016.45
ID  - Guan2016/11
ER  -