Abstract
Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and explosive properties. In this study, the application of the artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) for prediction of backbreak, was described and compared with the traditional statistical model of multiple regression. The performance of these models was assessed through the root mean square error, correlation coefficient (R 2) and mean absolute percentage error. As a result, it was found that the constructed ANFIS exhibited a higher performance than the ANN and multiple regression for backbreak prediction.
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Acknowledgments
The authors are thankful to Mr. Amiri, managing director of Sangan Iron Mine Project (SIMP), for his support and collaboration on this study. Authors also would like to thank Reza Moradidoost who kindly provided valuable grammatical suggestions during this research.
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Esmaeili, M., Osanloo, M., Rashidinejad, F. et al. Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers 30, 549–558 (2014). https://doi.org/10.1007/s00366-012-0298-2
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DOI: https://doi.org/10.1007/s00366-012-0298-2