Research on PPV Prediction Model of Open-pit Mine Based on PSO-XGBoost
The peak particle velocity(PPV)resulting from blasting vibration serves as a crucial metric in assessing the efficacy of blasting and mining design parameters.To enhance the accuracy of PPV predictions,a novel parameter self-optimizing PSO-XGBoost prediction model is introduced,leveraging the Particle Swarm Optimization(PSO)algorithm in conjunction with optimized Extreme Gradient Boosting(XGBoost).The research focuses on the LK open-pit copper-cobalt mine and examines five influencing factors,namely the maximum single explosive charge,total explosive charge,measured distance to blast center,average depth of blastholes,and elevation difference,as study parameters.A total of 187 sets of measured data from on-site blasting operations are gathered for further investigation into predicting PPV using the PSO-XGBoost model.The findings indicate that the PSO-XGBoost prediction model produces superior prediction evaluation metrics(R2=0.921,RMSE=0.0752,MAE=0.0717,MBE=0.0683),compared to alternative models,including the traditional XGBoost model,the SSA-XGBoost hybrid optimization model,and the Sariakaliski empirical formula.The sensitivity analysis reveals that the total explosive charge,significantly influences PPV prediction outcomes,underscoring the importance of using an appropriate amount of explosives to avoid energy inefficiency in blasting activities.Furthermore,the research demonstrates that the PSO-XGBoost prediction model,is capable of effectively addressing the nonlinear attributes of various factors,as well as integrating nonlinear and discrete data to develop a dependable,straightforward,and efficient PPV prediction model.This study offers valuable insights for promptly predicting blasting vibration in open-pit mining and assessing the impacts of blasting activities.
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