Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting

In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major significance. Therefore, it is essential to minimize the related environmental damage by precisely evaluating the intensity of AOp before any blasting operation. The present study primarily aimed to develop two different tree-based data mining algorithms, namely M5 ' decision tree and genetic programming (GP) for accurately predicting blast-induced AOp in granite quarries. In addition, a multiple linear regression technique was adopted to check the accuracy of the GP and M5 ' models. To achieve the aims of this research, 125 blasts were explored and their respective AOp values were carefully recorded. In each operation, six influential parameters of AOp, i.e., stemming length, powder factor, blasting index, joint aperture, maximum charge weight per delay and distance of the blast points, were recorded and considered as inputs for modeling. After developing the predictive models of AOp, their performances were examined in terms of coefficient of determination (R-2), root-mean-squared error (RMSE) and mean absolute error (MAE). Based on the computed results, the GP (with RMSE of 1.3997, R-2 of 0.8621 and MAE of 0.9472) outperformed the other developed models. Then, a sensitivity analysis was employed to identify the most influential parameters in predicting the AOp values. Finally, the generality of the proposed GP model was validated by investigating its predictive results with respect to the two most effective predictor variables. The study findings demonstrated the robustness and applicability of the proposed GP model for predicting blast-induced AOp.

Automated summary

The study aimed to develop tree-based data mining algorithms to accurately predict blast-induced air overpressure in granite quarries. The genetic programming (GP) model outperformed other models in terms of RMSE, R-2, and MAE, demonstrating its robustness and applicability.