Application of Neural Networks to Prediction of Powder Factor and Peak Particle Velocity in Tunnel Blasting

The main purpose of this study was to apply the artificial neural network (ANN) models to determine optimal powder factors and predict peak particle velocities, based on a series of observations and input parameters. The used blasthole pattern was V-cut. The locations of blastholes and ignition patterns remained almost constant. The input parameters were 14 geological conditions (i.e., dip, dip direction, spacing, separation, and persistence of 2 discontinuity sets, tunnel orientation, rock strength, RQD, and RMR) and 4 blasting conditions (i.e., distance between blasting points and measuring points, charge weight per delay, drilling length, and blasting efficiency). Data for the ANN application in this study were recently collected in a highway tunnel under construction in Korea. The main rock type in the site was biotitic granite. An optimum ANN model was determined by training models with collected data. The trained model was used to evaluate the prediction capability of the optimum model. It was shown that the ANN model could predict the powder factor and the peak particle velocity depended upon the selected input parameters. Particularly, the ANN model predicted more accurate peak particle velocities than those obtained by the blasting vibration equation. Moreover, the more dominant factors among the input parameters could be determined by analysis of the relative strength effect.