Prediction Of Blast-induced Ground Vibration Using Principal Component Analysis (Pca)-based Classification And Logarithmic Regression Technique

Principal component analysis (PCA) is one of the dimension reduction techniques widely used to classify data based on features. Ground vibration is a major hazard arising from rock blasting operations, and accurate prediction of the vibration is necessary for designing controlled blasting parameters. Most of the widely accepted predictors of ground vibration consider the maximum explosive charge weight per delay (MCPD) and distance as the parameters responsible for ground vibration, but mining sites with bigger production targets have varying geometrical parameters to suit the excavator utility. Accordingly, other blast design parameters will also have impacts on ground-vibration intensity. A blast vibration predictor considering all the blast design parameters would be complicated, and some of the parameters will have overlapping contributions to ground-vibration intensity. Data classification using PCA is a feasible solution, and regression analysis may be performed to develop predictors using the classified input data. In this study, PCA was used along with multivariate logarithmic regression to predict ground vibration, and the performance of this technique to predict ground vibration was compared with the performances of various existing empirical predictors. The evaluation suggests that the predictor with logarithmic regression followed by PCA performs better than the existing empirical predictors.