Blasting Vibration Control Using an Improved Artificial Neural Network in the Ashele Copper Mine

Abstract

Blasting is currently the most important method for rock fragmentation in metal mines. However, blast-induced ground vibration causes many negative effects, including great damage to surrounding rock masses and projects and even casualties in severe cases. Therefore, prediction of the peak particle velocity (PPV) caused by blasting plays an important role in reducing safety threats. In this paper, a genetic algorithm (GA) and an artificial neural network (ANN) algorithm were jointly used to construct a neural network model with a 4-5-1 topology to predict the PPV. For this model, the ANN parameters were optimized using the GA, and the deviating direction, horizontal distance, vertical distance, Euclidean distance, explosive type, burden, hole spacing, and maximum charge per delay were used as input information. Moreover, principal component analysis (PCA) was used to extract the first four principal components from the eight input factors as the four inputs of the ANN model. The model was successfully applied to protect an underground crushing cave from blasting vibration damage by adjusting the blasting parameters. Compared with several widely used empirical equations, the GA-ANN PPV prediction model produced significantly better results, while the Ambraseys–Hedron method was the best of the empirical methods. Therefore, the improved GA-ANN model can be used to predict the PPV on site and provide a reference for the control of blasting vibration in field production.