Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.