Prediction accuracy of underground blast variables: decision tree and artificial neural network

Abstract

Accurate prediction of blast induced ground vibration variables such as particle velocity and frequency are of interest for safe design of controlled blasting operations for mining, tunnelling or excavation projects. There are certain limitations in the widely used empirical and numerical approaches especially when number of variables is large. Various data driven approaches have been employed for producing correct estimates for such cases. Decision tree (DT), earlier successfully employed for solving variety of civil engineering problems, is employed for prediction of blast variables for the first time in this article. The performance of DT models was found to be equally good (for particle velocity variable) or better than (for frequency variables) ANN models developed in this study, and unequivocally superior to the SVM or RF models reported in literature. Additionally, the clarity in decision rule-based estimation foster easy comprehension and future implementation.