| Issue |
E3S Web Conf.
Volume 136, 2019
2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|
|
|---|---|---|
| Article Number | 04033 | |
| Number of page(s) | 6 | |
| Section | Urban Public Safety | |
| DOI | https://doi.org/10.1051/e3sconf/201913604033 | |
| Published online | 10 December 2019 | |
Finite Element Model Modification of Arch Bridge Based on Radial Basis Function Neural Network
1 School of Civil Engineering and Architecture, University of Jinan, Jinan 250022, China.
2 School of Civil Engineering, Shandong Jianzhu University, Jinan 250101, China.
* Corresponding author’s e-mail: WANG Lei, cea_wangl@ujn.edu.cn
Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function. In this paper, the finite element model of a through tied arch bridge is modified based on Neural Network. The approximation function of RBF neural network is utilized to fit the implicit function relationship between the response of the bridge and its design parameters. Then the finite element model of the bridge structure is modified. The results show that RBF neural network is efficient to modify the model of a through tied arch bridge.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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