Forecasting blast-induced ground vibration developing a CART model

Drilling and blasting is an extensively used method for the rock fragmentation in surface mines and tunneling projects. Ground vibration is one of the most important environmental effects produced by blasting operations. In this research work, classification and regression tree (CART), multiple regression (MR), and different empirical models are used to develop predictions for ground vibrations induced by blasting operations conducted in the Miduk copper mine, Iran. To achieve this aim, a number of 86 blasting events were monitored, and the values of peak particle velocity (PPV) in terms of millimeter per second and two effective parameters on the PPV, namely, distance between blast-face and monitoring station in terms of meter and maximum charge used per delay in terms of kilogram, were measured. Performance of models established was evaluated using coefficient of correlation (R-2), Nash and Sutcliffe (NS), and root mean square error (RMSE). The results revealed that the CART technique with R-2 = 0.95, NS = 0.17, and RMSE = 0.17 provides a better performance when compared with empirical and MR models and has the capacity to generalize.