Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems

Abstract

This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tulu open-pit boron mine, the Kirka open-pit boron mine, and the TKI Can coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r 2 = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r 2 = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.