Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting

In bench blasting, backbreak is the unwanted result that causes instability to the highwall and can lead to safety hazards. Hence, it is utmost necessary to minimize the generation of backbreak to improve mine’s safety. It has always been difficult to predict the backbreak because of various parameters involved, i.e. blast design, explosive properties, rock mass, etc. In this study, multivariate regression analysis (MVRA) and genetic programming (GP) techniques were performed on 70 blast data sets of previously published papers. Both the models have been developed and tested with the same mine data set. For validation of the models, a total of 14 trial blasts have been conducted in Indian coal mines with different geological strata and other parameters. The values of R2, RMSE, MAPE and prediction level at 25% and 90% were computed for GP and MVRA techniques. Also, the GP model is compared with the other state-of-the-art techniques. It has been found that the level of prediction for validation data set at 25% using GP is 78.57% and for MVRA is 21.42%. The mean magnitude of relative error (MMRE) value for GP and MVRA is 0.18 and 0.42, respectively. The results show that the GP is a more efficient tool for prediction of backbreak in comparison with MVRA. On performing sensitivity analysis, it has been found that stemming length and powder factor are the most influencing parameters to backbreak.