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

Guo Feng Wang; Wen Zhao; Yong Ping Guan; Lei Liang

doi:10.4028/www.scientific.net/AMM.353-356.828

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 353-356Study on the Deformation Forecasting for the...

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

Abstract:

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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Periodical:

Applied Mechanics and Materials (Volumes 353-356)

Pages:

828-832

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.353-356.828

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Online since:

August 2013

Authors:

Guo Feng Wang, Wen Zhao, Yong Ping Guan, Lei Liang

Keywords:

Data Normalization, Mapping, Measure, Neural Network (NN), Rock Pressure, Tunnel Surrounding Rock Deformation

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References

[1] Zong-li Jiang. Introduction to Artificial Neural Networks[M], Beijing: Higher Education Press: (2001).

Google Scholar

[2] Wu-sheng Hu, Neural network theory and its engineering applications[M], Beijing: The publishing house of Surveying and mapping, (2006).

Google Scholar

[3] De-feng Zhang, The MATLAB Neural networks application design[M], Beijing: Machinery Industry Press, (2009).

Google Scholar

[4] An-nan Jiang, Xia-ting Feng. Case-Based SVM Method for Maximal Deformation Forecasting of Surrounding Rocks of Tunnels, Vol. 25(2004), pp.793-795.

Google Scholar

[5] Yong-ping Guan, Jian Song, Su-hong Wang, Yu Liu, Back Analysis of Mechanical Parameters of Surrounding Rocks Based on GA-BP Algorithm, Vol. 33(2012), pp.276-279.

Google Scholar