1、翻译部分英文原文Practical Neural Network Applications in the Mining IndustryL. Miller-Tait, R. PakalnisDepartment of Mining and Mineral Process Engineering,University of British Columbia,Vancouver, B.C., CanadaABSTRACTThe mining industry relies heavily upon empirical analysis for design and prediction. Neur
2、al networks are computer programs that use parallel processing, similar to the human brain, to analyze data for trends and correlation. Two practical neural network applications in the mining industry would be rockburst prediction and stope dilution estimates. This paper summarizes neural network da
3、ta analysis results for a 1995 Goldcorp/Canmet study on rockbursting and a 1986 UBC/Canmet study on open stope dilution at the Ruttan Mine.1 INTRODUCTIONMany aspects of mine design are based upon empirical data. Neural Networks analyze data and predictions based on previous results. Neural networks
4、have advantages over conventional empirical design approaches. These advantages include: Neural networks can easily use multiple inputs to analyze data. By using multiple hidden layers and nodes neural networks investigate the combined influence of inputs. Neural networks can be easily retrained as
5、new data becomes available making them a more dynamic and flexible empirical estimation approach. Neural network software is inexpensive and easy to use. Neural networks have demonstrated a more accurate empirical estimate over conventional methods.The advantages of using neural networks are illustr
6、ated in a rockburst prediction example and an open stope dilution example.2 ROCKBURST PREDICTIONThe first example of a potential situation where neural networks could be useful in the mining industry is the prediction of rockbursts through physical inputs. To quote directly from the Ontario Ministry
7、 of Labor “.we do not have the ability to predict when and where rockbursts will occur, and the experts in the field agree that we are not close to make such predictions” 1. Between 1984 and 1993 eight underground miners were killed in Ontario due to rockbursts. This accounted for approximately 10%
8、of underground fatalities during this period. If neural networks were to have success in predicting where rockbursts occur, additional ground support, remote equipment, and/or design modifications could reduce or possibly eliminate fatalities due to rockburst. As safety is the primary responsibility
9、 of mining engineers, the potential for neural networks to assist in predicting rockburst inputs should be investigated. In 1995, a joint project was completed by Goldcorp Inc. and Canmet called “Development of Empirical Design Techniques in Burst Prone Ground at A. W. White Mine” 2. Part of the stu
10、dy was to collect input information on rockburst, caving, ground wedge, and roof fall failures at the A. W. White Mine between 1992 and 1995. This resulted in a failure database consisting of 88 ground failures with corresponding inputs for each failure. The six inputs collected for each failure wer
11、e RMR 3, Q 4, span 5, SRF2,RMR adjustment, and depth. These input factors were set up and run in a neural network with 73 examples being used for training and 15 examples being used to test the network. The output factor, stability, can be one of four failures 2 - PUN-RF (potentially unstable roof f
12、all), PUN-GW (potentially unstable ground wedge), BUR (rock burst), and CAV (cave). A brief description of the input and output factors are listed below.2.1 Input factorsRMR - The RMR system, initially developed by Bieniawski in 19733, bases rock mass quality on five parameters. These parameters are
13、: Uniaxial compressive strength of the rock Rock quality designation (RQD) Spacing of discontinuities Condition of discontinuity Ground water conditions.These factors are given a numerical value and totalled together to get an RMR value. This value will be a number between 0 and 100 with zero being
14、very poor rock and 100 being extremely good rock. The ground water conditions were assumed to be dry conditions.Q -The Q factor refers to the rock quality tunnelling index 4. Developed in 1974, by Barton, Lien and Lunde, from the Norwegian Geotechnical Institute, the Q factor is based on six factors
15、, which are: RQD - rock quality designation Jn -joint set number Jr -joint roughness number Ja -joint alteration number Jw - joint water reduction factor SRF - stress reduction factor.The actual Q formula is Q= RQD/Jn Jr/Ja Jw/SRF.The Jw/SRF factor was assumed to be 1.0 for this study because dry co
16、nditions are assumed. Stress is factored through modelling and strain measurements. The Q factor ranges on a logarithmic scale ranging from 0.001 to 1,000 where 0.001 is extremely poor rock and 1,000 is virtually perfect rock.Span 5 - the meaning of span refers to the width of an underground opening in plan view. Span can be determined through the largest diameter of a cir
