Structure identification of fuzzy model
Fuzzy Sets and Systems
Resolution principles in possibilistic logic
International Journal of Approximate Reasoning
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IEEE Transactions on Computers - Special issue on artificial neural networks
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IEEE Transactions on Neural Networks
Developing a new fuzzy model to predict burden from rock geomechanical properties
Expert Systems with Applications: An International Journal
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Although blasting is the most principal method of fragmentation in hard rock mining, the significance of the costs of blast induced rockmass damage in terms of mining efficiency and safety is becoming increasingly recognized. Backbreak is one of the adverse phenomena in blasting operations that causes the instability of mine walls, falling down of equipments, improper fragmentation, reduced efficiency of drilling, etc., and consequently increases the total cost of a mining operation. In this paper, predictive models based on fuzzy set theory and multivariable regression have been developed for predicting backbreak in Gol-E-Gohar iron mine of Iran. To evaluate performance of the employed models, the coefficient of correlation (R^2) and the root mean square error (RMSE) indices were calculated. It was concluded that performance of the fuzzy model is considerably better than regression model. For the fuzzy and regression models, R^2 and RMSE were equal to 95.43% and 0.44 and 34.08% and 1.63, respectively. The fuzzy model sensitivity analysis shows that the most effective parameters on backbreak phenomenon are stemming length, hole depth, burden and hole spacing. Application of this model in the Gol-E-Gohar iron mine considerably minimized backbreak and improved blasting efficiency.