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An investigation into using artificial neural networks for empirical design in the mining industry

University of British Columbia

The mining industry relies heavily upon empirical analysis for design and prediction. Neural networks are computer programs that use parallel processing, similar to the human brain, to analyze data for trends and correlation. Neural networks will analyze input factors by assigning a weighting or numerical value to each input factor or each input factor combination. This is done to provide an estimate or prediction for an output factor. As more information becomes available the neural network will continually adjust and change the input factor weightings to improve output prediction. The objective of this thesis is to see if neural networks can provide assistance in analyzing data within the mining industry. Neural networks were used to study rockburst prediction, ELOS (equivalent linear overbreak/slough ), and dilution estimates in open stopes. There were two aspects of the neural network rockburst prediction analysis. The first aspect was to see if the neural network could predict rockbursts through the physical inputs such as RMR and span. The neural network was very successful in predicting rockbursts. The other aspect of the rockburst study was to see if neural networks, using seismic data, could predict when a rockburst would occur. The neural networks were unsuccessful in predicting when a rockburst would occur. Further analysis with seismic data presented in a different format may improve results. Neural networks were also used in this study to assist in ELOS prediction. A joint UBC - Canmet research project ( Clarke, 1997 ) recorded ELOS from 75 open stopes while recording 23 relevant input factors for each stope. This neural net analysis was conducted to assist in determining the most relevant factors. Rock quality, particularly RMR, proved to be the most significant factor in ELOS determination. Blasting factors such as blasthole diameter, length, and layout were also very relevant and helped predict outlier points. This analysis shows some potential advantages and shortcomings of using neural networks in the mineral industry. The final neural network was to compare open stope dilution formula estimates with neural network estimates. Using the same database for neural net training as the formula derivation, the neural net dilution estimates on unseen data were, on average, more accurate than the formula predictions. The conclusion focuses on how neural networks could be practically applied in the mining industry.

Thesis/Dissertation

application/pdf

2009-05-28

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http://hdl.handle.net/2429/8355

Mining Engineering

University of British Columbia

UBCV

DSpace