Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks
The main objective in production blasting is to achieve a proper fragmentation. In this paper, rock fragmentation the Sarcheshmeh copper mine has been predicted by developing a model using artificial neural network. To construct the model, parameters such as burden to spacing ratio, hole-diameter, s...
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| Veröffentlicht in: | Geotechnical and geological engineering 2010-07, Vol.28 (4), p.423-430 |
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| container_title | Geotechnical and geological engineering |
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| creator | Monjezi, M. Amiri, H. Farrokhi, A. Goshtasbi, K. |
| description | The main objective in production blasting is to achieve a proper fragmentation. In this paper, rock fragmentation the Sarcheshmeh copper mine has been predicted by developing a model using artificial neural network. To construct the model, parameters such as burden to spacing ratio, hole-diameter, stemming, total charge-per-delay and point load index have been considered as input parameters. A model with architecture 9-8-5-1 trained by back propagation method was found to be optimum. To compare performance of the neural network, statistical method was also applied. Determination coefficient (
R
2
) and root mean square error were calculated for both the models, which show absolute superiority of neural network over traditional statistical method. |
| doi_str_mv | 10.1007/s10706-010-9302-z |
| format | Article |
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R
2
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R
2
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R
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| subjects | Artificial neural networks Back propagation BLASTING Civil Engineering COMPUTER SIMULATION Copper COPPER RESOURCES Earth and Environmental Science Earth Sciences Fragmentation Geotechnical Engineering & Applied Earth Sciences Hydrogeology MATHEMATICAL ANALYSIS Mathematical models Neural networks Original Paper Parameters Predictions Rock Rocks Statistical analysis Statistical methods Statistics Terrestrial Pollution Waste Management/Waste Technology |
| title | Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks |
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