Proceedings of the 8th Russian-Chinese Symposium “Coal in the 21st Century: Mining, Processing, Safety"

Displacement Back Analysis Based on GA-BP and PSO-BP Neural Network

Authors
Gu Dongdong, Tan Yunliang
Corresponding Author
Gu Dongdong
Available Online October 2016.
DOI
https://doi.org/10.2991/coal-16.2016.34How to use a DOI?
Keywords
back analysis of displacements, BP neural network, genetic algorithm, particle swarm optimization
Abstract
In order to study the back analysis accuracy of different algorithms of neural network for back analysis of tunnels surrounding rock. Firstly, the main parameters influencing the deformation of tunnels surrounding rock are analyzed by orthogonal test. Secondly, a FLAC3D numerical model of the tunnels was established based on the field conditions for getting learning samples of the neural work. Thirdly, the BP neural network is optimized by genetic algorithm, particle swarm optimization and normalization method, respectively. At last, the back analysis of the tunnels displacement is carried out, and the mechanical parameters of the surrounding rock are forecasted for comparing accuracy of the three optimized methods. The results show that it is faster and precise to use artificial neural network to inverse mechanical parameters of the tunnels surrounding rock.
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Proceedings
8th Russian-Chinese Symposium “Coal in the 21st Century: Mining, Processing, Safety"
Part of series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-233-6
ISSN
2352-5401
DOI
https://doi.org/10.2991/coal-16.2016.34How 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  - Gu Dongdong
AU  - Tan Yunliang
PY  - 2016/10
DA  - 2016/10
TI  - Displacement Back Analysis Based on GA-BP and PSO-BP Neural Network
BT  - 8th Russian-Chinese Symposium “Coal in the 21st Century: Mining, Processing, Safety"
PB  - Atlantis Press
SP  - 168
EP  - 173
SN  - 2352-5401
UR  - https://doi.org/10.2991/coal-16.2016.34
DO  - https://doi.org/10.2991/coal-16.2016.34
ID  - Dongdong2016/10
ER  -