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Particle swarm optimization approach for forecasting backbreak induced by bench blasting

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Abstract

One of the most challenging safety problems in open pit mines is backbreak during blasting operation, and its prediction is very important for a technically and economically successful mining operation. This paper presents application of particle swarm optimization (PSO) technique to estimate the backbreak induced by bench blasting, based on major controllable blasting parameters. Two forms of PSO models, linear and quadratic, are developed based on blasting data from Sungun copper mine, Iran. According to obtained results, both models can be used to predict the backbreak, but the comparison of two models, in terms of statistical performance indices, shows that the quadratic form provides better results than the linear form.

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Acknowledgments

The author would like to thank all the people who helped in the preparation of the paper, especially Mrs. Ifa Mahboobi. The author is also grateful to the anonymous reviewers for their useful comments and constructive suggestions.

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Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, P.O. Box 8415683111, Isfahan, Iran

    Ebrahim Ghasemi

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  1. Ebrahim Ghasemi
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Correspondence to Ebrahim Ghasemi.

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Ghasemi, E. Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Comput & Applic 28, 1855–1862 (2017). https://doi.org/10.1007/s00521-016-2182-2

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  • DOI: https://doi.org/10.1007/s00521-016-2182-2

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