Abstract:In order to improve the prediction accuracy and adaptability, based on the blasting project of Meishan Iron Mine, this paper proposes a Dung Beetle Optimized Support Vector Regression (DBO-SVR) model for PPV prediction. The Pearson heat map is used to analyze the correlation between each feature and PPV (Peak Particle Velocity), and the mean square error and coefficient of determination are used as the model evaluation indexes. The prediction results of DBO-SVR, DBO-XGB, SVR and XGB are compared and analyzed. The mean square errors of the four algorithms were 0.028,0.152,1.084,0.226, and the determination coefficients were 0.985,0.917,0.408,0.877, respectively. The results show that the prediction effect of DBO-SVR algorithm is better than that of other models. The DBO-SVR algorithm model comprehensively considers the influence of multiple blasting design parameters on PPV, greatly shortens the training time of sample data, and accelerates the convergence speed of the model to meet the speed prediction requirements of actual blasting vibration. The prediction results are more accurate and the error is smaller, which can provide reference for the prediction of peak vibration velocity of similar blasting projects.
