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 arecomputer programs that use parallel processing, similar to the human brain, to analyze data for trends andcorrelation. Two practical neural network applications in the mining industry would be rockburst predictionand stope dilution estimates. This paper summarizes neural network data
3、analysis results for a 1995Goldcorp/Canmet study on rockbursting and a 1986 UBC/Canmet study on open stope dilution at theRuttan Mine.1 INTRODUCTIONMany aspects of mine design are based upon empirical data. Neural Networks analyze data and predictions based on previous results. Neural networks havea
4、dvantages over conventional empirical designapproaches.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 new data
5、 becomes available making them a more dynamicand 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 illustrated in a
6、 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 isthe prediction of rockbursts through physical inputs. To quote directly from the Ontario Ministry ofLabor“.
7、we do not have the ability to predict when and where rockbursts will occur, and the experts in the fieldagree that we are not close to make such predictions” 1. Between 1984 and 1993 eight undergroundminers were killed in Ontario due to rockbursts. This accounted for approximately 10% of underground
8、 fatalities during this period. If neuralnetworks were to have success in predicting where rockburstsoccur, additional ground support, remote equipment, and/or design modifications could reduce or possibly eliminate fatalities due to rockburst. As safety is the primary responsibility of mining engin
9、eers, thepotential 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 EmpiricalDesign Techniques in Burst Prone Ground at A. W. White Mine” 2. Part of the study was to collect i
10、nputinformation on rockburst, caving, ground wedge, and roof fall failures at the A. W. White Mine between1992 and 1995. This resulted in a failure database consisting of 88 ground failures with correspondinginputs for each failure. The six inputs collected for each failure were RMR 3, Q 4, span 5,
11、SRF2,RMR adjustment, and depth. These input factors were set up and run in a neural network with 73 examplesbeing used for training and 15 examples being used to test the network. The output factor, stability, can beone of four failures2 - PUN-RF (potentially unstable roof fall), PUN-GW (potentially
12、 unstable groundwedge), BUR (rockburst), and CAV (cave). A brief description of the input and output factors are listed below.Input factorsRMR - The RMR system, initially developed by Bieniawski in 19733, bases rock mass quality on fiveparameters.These parameters are: Uniaxial compressive strength o
13、f 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 anumber between 0 and 100 with zero being very poor rock and 100 being extre
14、mely good rock. The groundwater 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 Qfactor is based on six factors, which are: RQD - rock quality desig
15、nation 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/JaJw/SRF.The Jw/SRF factor was assumed to be 1.0 for this study because dry conditions are assumed. Stress is factor
16、edthrough modelling and strain measurements. The Q factor ranges on a logarithmic scaleranging from 0.001 to1,000 where 0.001 is extremely poor rock and 1,000 is virtually perfect rock.Span5 - the meaning of span refers to the width of an underground opening in plan view. Span can bedetermined through the largest diameter of a circle within an underground excavation.SRF
