Applying Neural Network to Predict Roadway Surrounding Rock Displacement

2020 ◽  
Author(s):  
TANG Jun-Yi ◽  
ZHANG Min ◽  
ZHANG Miao ◽  
YU Wan-Jun ◽  
TIAN Yu
Keyword(s):  
Neural Network ◽  
Surrounding Rock

Time Series Forecasting of Tunnel Surrounding Rock Displacement

2011 ◽  
Vol 261-263 ◽  
pp. 1789-1793 ◽  
Author(s):  
Guang Xiang Mao ◽  
Yuan You Xia ◽  
Ling Wei Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Stress ◽  
Rock Types ◽  
Network Time ◽  
Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

Identification for Bolt-Surrounding Rock System Based on Wavelet Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 738-742
Author(s):  
Jun Qiang Hu ◽  
Yong Xing Zhang ◽  
Jian Gong Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analytical Method ◽  
Dynamic Testing ◽  
Wavelet Neural Network ◽  
Surrounding Rock ◽  
Structural System ◽  
Rock System ◽  
Anchoring System

The dynamic testing is widely used in undestructive testings of bolt’s anchoring quality. But it’s difficult to estimate bolt’s anchoring quality according to the dynamic response of bolt. The bolt’s anchorage quality depends mainly on bolt-surrounding rock structural system which features must be identified. A new analytical method used in identification for bolt-surrounding rock structural system is put forward, which combine with advantages of wavelet analysis and artificial neural network. The results indicate that this wavelet neural network after training can best identify the bolt’s side rigidity factors and can be a useable intelligentized mean to assess the quality of bolt’s anchoring system.

Study on the Deformation Forecasting for the Tunnel Surrounding Rock Using the Neural Network

2013 ◽  
Vol 353-356 ◽  
pp. 828-832
Author(s):  
Guo Feng Wang ◽  
Wen Zhao ◽  
Yong Ping Guan ◽  
Lei Liang
Keyword(s):  
Neural Network ◽  
Maximum Error ◽  
Mining Area ◽  
Surrounding Rock ◽  
Rock Deformation ◽  
The Neural Network ◽  
Sublevel Caving ◽  
Iron Mine ◽  
Set Up ◽  
Surrounding Rock Deformation

The non-pillar sublevel caving method is used in Iron Mine in Banshi. In the mining area, there are many folds and faults, the inclination of ore body changes greatly, and ore and rock are fragmentized. The tunnel often collapsed and the surrounding rock deformation was getting large during the construction stage. Using the data of tunnel surrounding rock deformation, we adopt the neural network method to set up the mapping relation between the tunnel surrounding rock deformation and the project factors, including tunnel deepness, tunnel dimension, measuring time and surrounding rock quality. The analyzing results show that the maximum error between the forecast and the testing data is 13%, which indicates that this method is useful and feasible to the mining engineering. Key words: rock pressure; measure, deformation of the tunnel surrounding rock; neural network; data normalization; mapping

scholarly journals Displacement Prediction of Surrounding Rock in Tunnel Based on cellular ant algorithm

2021 ◽  
Vol 236 ◽  
pp. 03026
Author(s):  
Li Yongyu ◽  
Wang Yu ◽  
Wang Shihua
Keyword(s):  
Neural Network ◽  
Linear Relationship ◽  
Neural Network Model ◽  
Surrounding Rock ◽  
Ant Algorithm ◽  
Displacement Prediction ◽  
Stability Of Surrounding Rock ◽  
Key Factor ◽  
The Stability ◽  
And Control

The deformation of tunnel surrounding rock is the key factor to analyze the stability of surrounding rock. However, due to the influence of many factors and the strong non-linear relationship between the factors, it is difficult to predict the deformation effectively. In this paper, a method based on cellular ant neural network model is proposed to simulate the displacement of surrounding rock with time. The results show that this method is efficient and feasible, and can meet the requirements of engineering and control.

The Tunnel Surrounding Rock Parameters Identification Method Based on PSO-ANN

2011 ◽  
Vol 94-96 ◽  
pp. 637-640 ◽  
Author(s):  
Zhan Ping Song ◽  
Song Bo Ren ◽  
Zhen Chao Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrounding Rock ◽  
Parameters Identification ◽  
Soil Parameters ◽  
Data Set ◽  
Identification Method ◽  
Artificial Neural ◽  
Rock Parameters ◽  
Rock And Soil

Aiming at the complexity and uncertainty of rock and soil body, the paper proposed a tunnel surrounding rock parameters identification method combining numerical simulation, particle swarm optimization and artificial neural network. The method acquired data set between rock soil parameters and monitoring displacement and trained artificial neural network. The analytical theory and method are introduced in detail, analyzes the tunnel of Dalian Metro by the proposed method, and gets satisfied results. Which states that the parameters identification method based on PSO-ANN is feasible and has good foreground.

Self-stability evaluation model of surrounding rock based on improved BP neural network

2013 ◽  
Vol 32 (4) ◽  
pp. 1056-1059
Author(s):  
Duo-dian WANG ◽  
Guo-qing QIU ◽  
Ting-ting DAI ◽  
Yue WANG
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Surrounding Rock ◽  
Stability Evaluation
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