Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression

A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck pile under the action of explosive energy. Considering the difficulties in collecting measurements at the post-blast ore boundary, the distribution law and prediction of blast-induced rock movement were studied in this paper. With the application of a blast movement monitoring system, which can obtain the most accurate data to determine the ore boundary after the blast, four blast movement trials were carried out to collect data. Then, statistical analysis, an artificial neural network (ANN) model, a random forest (RF) model and a gray wolf optimizer algorithm-support vector regression (GWO-SVR) model were used to analyze the database. The results of the statistical analysis show that the horizontal, vertical and 3D movements first increase and then decrease, with the maximum displacement occurring near the top of the charging section. Furthermore, the horizontal movement exhibited a good linear relationship with the 3D movement, indicating that the horizontal movement can be considered instead of the 3D movement to facilitate shovel guidance for reducing ore loss and dilution. The results also indicate that the GWO-SVR model is more accurate than the ANN and RF models and that the blast-induced rock movement can be controlled by increasing the burden x spacing and reducing the power factor variables during the mining process.