The introduction of neural network system and its applications in rock engineering |
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| Authors: | Yi Huang Stefan Wnstedt |
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| Institution: | aDivision of Mining Engineering, LuleåUniversity of Technology, S-97187, Luleå ,Sweden |
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| Abstract: | The neural network system has been developing very fast recently. It has been widely used in many industries such as automation, nuclear power plant, chemical industry, etc. Neural network systems have a great advantage in dealing with problems in which many factors influence the process and result, and the understanding of this process is poor, and there are experimental data or field data. In rock engineering, many problems are of this nature. In this paper, a brief introduction to neural network systems is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in rock engineering, made by us, are presented. Case 1 is ore boundary delineation. In this case, the rock are divided into three classes, i.e.: (1) waste rock; (2) semi-ore; and (3) ore for mining purposes. The neural network system built can judge whether it is ore, semi-ore or waste rock along the borehole according its corresponding geophysical logging data, such as gamma-ray, gamma-gamma, neutron and resistivity. Case 2 is aggregate quality prediction. In this case, the quality parameters: (1) impact value; (2) abrasion value I; and (3) abrasion value II are predicted by using a neural network system based on density, point load, content of quarts and content of brittle minerals. Case 3 is rock indentation depth prediction. In this case, the rock indentation depth under indentation load is predicted by the established neural network system based on the indentation load on rock, indenter type and rock mechanical properties, such as uniaxial compressive strength, Young's modulus. Poisson's ratio, critical energy release rate and density. In all these cases, the neural network systems have been applied successfully. The testing results are satisfactory and better than the existing techniques. |
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| Keywords: | Neural network Application Rock engineering Ore boundary Aggregate quality Rock indentation |
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