Abstract:
Despite being a versatile and low-cost method, rock blasting produces undesirable severe effects. The pre-sent study aims to examine the ground vibrations pro-duced by blasting, which are of serious concern to mine operators as well as the nearby inhabitants. Forty-nine field-scale trial blasts were conducted and recor-ded to measure ground vibrations produced by blasting in a limestone quarry in Rajasthan, India. The multi-variate linear regression (MLR) and artificial neural network (ANN) techniques were used to predict the peak particle velocity (PPV) with distance between the blasting site and measuring station, charge per delay and scaled distance as the input parameters. Subse-quently, a coefficient of determination (R2) was calculated using MLR and ANN approaches. Additionally, to verify whether the recorded events exceeded the threshold levels, the values of PPV and dominant frequency pro-pounded by the United States Bureau of Mines (USBM), German standard (DIN), and Director General of Mines Safety, India were carefully scrutinized. Results were compared based on R2 values obtained by the USBM predictor equation, MLR and ANN techniques. It was found that ANN provided a good prediction with a high degree of correlation (0.901) in compari-son to MLR (0.754). Also, frequency analysis for the study field showed that the dominance of frequencies was in the range 10–40 Hz. Although the values were within safe limits, disturbances may be witnessed in nearby structures if PPV values are high at lower fre-quency range.
