An artificial neural network for forecasting the amount of Chinese colliery roadway 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

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.94-96.637 ◽

2011 ◽

Vol 94-96 ◽

pp. 637-640 ◽

Cited By ~ 3

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.

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Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

Mathematical Problems in Engineering ◽

10.1155/2014/351496 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 8

Author(s):

Qingdong Wu ◽

Bo Yan ◽

Chao Zhang ◽

Lu Wang ◽

Guobao Ning ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Surrounding Rock ◽

Support Vector ◽

Tunnel Construction ◽

Displacement Prediction ◽

Vector Machines ◽

Artificial Neural ◽

Comparative Results ◽

Artificial Neural Network Ann

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data ofFangtianchongtunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

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