Block size prediction for bench blasting in open-pit mine based on three neural network algorithms
To predict the blasting effect in open-pit mine bench blasting,an empirical data set of blasting parameters is constructed based on established blasting experience formulas and field blasting datas.Intelligent algorithms,including BP,FNN,and RBF neural networks,are used to research and analyze the optimization of blasting parameters.Based on a deep neural network algorithm,the relationship between blasting parameters and rock fragmentation is examined,leading to the establishment of a predictive model for blasting parameters and large block rate.Additionally,a sensitivity analysis of the blasting parameters is conducted,and the prediction results are compared with practical examples.The research results indicate that the loss values of the three models during training process are all below 0.05.The sensitivity analysis reveals that hole spacing and row spacing exert the most significant influence on the model prediction results.In the training and testing phases of the data set,BP model demonstrates superior prediction accuracy,while the FNN model exhibits balanced performance in all aspects.Additionally,RBF model displays notable stability.In practical applications,the relative errors of the three models do not exceed 10%,confirming their high accuracy in predicting blasting parameters.The models and results presented in this article can provide a reference for blasting engineering practice.
万方数据
维普
