Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

Prediction of welding shrinkage deformation of bridge steel box girder based on Wavelet Neural Network

Authors
Yulong Tao, Yunshui Miao, Jiaqi Han, Feiyun Yan
Corresponding Author
Yulong Tao
Available Online May 2018.
DOI
https://doi.org/10.2991/amcce-18.2018.75How to use a DOI?
Keywords
Bridge Steel Box Girder, Wavelet Neural Network, Prediction Method
Abstract
Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.75How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yulong Tao
AU  - Yunshui Miao
AU  - Jiaqi Han
AU  - Feiyun Yan
PY  - 2018/05
DA  - 2018/05
TI  - Prediction of welding shrinkage deformation of bridge steel box girder based on Wavelet Neural Network
BT  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.75
DO  - https://doi.org/10.2991/amcce-18.2018.75
ID  - Tao2018/05
ER  -