Estimation of Rock Mass Rating System with an Artificial Neural Network

Authors:
Zhi Qiang Zhang;Qing Ming Wu;Qiang Zhang;Zhi Chao Gong
Affiliations:
School of Power and Mechanical Engineering, Wuhan University, Wuhan, China 430072 and Hubei Provincial Key Laboratory of Fluid Machinery and Power Equipment Technology, Wuhan, China 430072;School of Power and Mechanical Engineering, Wuhan University, Wuhan, China 430072;School of Power and Mechanical Engineering, Wuhan University, Wuhan, China 430072;School of Power and Mechanical Engineering, Wuhan University, Wuhan, China 430072
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

The geo-mechanical classification - rock mass rating (RMR) - is used for categorizing rock mass. Assessing RMR is an important factor for successful accomplishment of a tunneling project. In the rock mechanics and mining literatures, some empirical methods exist between rock mass and other rock properties, such as using characteristic of the rock, geological structure etc. However, those means have some limitations by special rock types. After analyzed the information to identify RMR, a new parameter as one of the input neurons was used to develop predictive relations. There are eight parameters as the input parameters are presented based on artificial neural networks (ANN). The situ-test data of the tunnel face were measured and the experimental results indicate the proposed method was effective.