Abstract
Goal of blasting operations is to achieve desired fragment size to operate the mine and plant economically while maintaining safety that includes prevention of flyrock accidents. This paper focuses on the simultaneous prediction of flyrock distance and fragmentation using back propagation neural network techniques. Thus, linear charge concentration, burden, spacing, stemming length, specific charge, unconfined compressive strength and rock quality designation are taken as input. Flyrock distance and fragment size are chosen as output. The predicted outputs by back propagation neural network (BPNN), multi variate regression analysis (MVRA) have been compared. The quite lower root mean square error (RMSE) and mean absolute error (MAE) in BPNN than MVRA prove that BPNN is a better prediction method. Also, the predicted output in BPNN correlates better with the observed output than MVRA. Sensitivity analysis for both independent variables for BPNN and MVRA is also included in this paper.
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