Displacement Back Analysis Based on GA-BP and PSO-BP Neural Network
Gu Dongdong, Tan Yunliang
Available Online October 2016.
- https://doi.org/10.2991/coal-16.2016.34How to use a DOI?
- back analysis of displacements, BP neural network, genetic algorithm, particle swarm optimization
- 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.
- 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 - Proceedings of the 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 -