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Abstract：
The drill-blasting method is a commonly used mining technique in open-pit mines, and the peak particle velocity (PPV) caused by blasting vibrations is an important indicator for evaluating the rationality of blasting mining design parameters. To develop an effective PPV prediction model, a parameter self-optimizing RUN-XGBoost prediction model is implemented using the Runge-Kutta optimization algorithm (RUN) combined with extreme gradient boosting (XGBoost). The factors affecting the prediction of PPV, including maximum explosive (ME), total explosive (TE), blast center distance (BCD), blast hole depth (BHD), and height difference between the measurement location and the blast location (DH), are selected as the influencing indicators. 188 pieces of blasting operation data were measured at the RK open pit copper-cobalt mine. Then, the RUN-XGBoost prediction model for PPV is studied and compared with the Sadovsky empirical formula, traditional XGBoost model, PSO-XGBoost model, and some traditional machine learning models (Ridge, LASSO, SVM, and SVR) using R2, RMSE, VAF, MAE, and MBE as evaluation indicators for model prediction results. Finally, the Shapley Additive Explanations (SHAP) method is used to evaluate the contribution of different influencing indicators to the PPV prediction results. The results show that the RUN-XGBoost prediction model is significantly better than other machine learning models and the Sadovsky empirical formula in the prediction of PPV, further demonstrating that the RUN-XGBoost prediction model can handle the nonlinear features of multiple factors and provide a reliable, simple, and effective PPV prediction model, forming a rapid prediction and evaluation method for blasting vibrations in open-pit mining.
摘要：
钻爆法是露天矿山常用的开采技术,爆破振动引起的粒子速度峰值(PPV)是评价爆破开采设计参数合理性的重要指标。为了建立有效的PPV预测模型，使用Runge-Kutta优化算法（RUN）结合极限梯度提升（XGBoost）实现了参数自优化RUN-XGBoost预测模型。影响PPV预测的因素,包括最大爆炸（ME），总炸药（TE），爆破中心距离(BCD),爆破孔深度(BHD),以及测量位置和爆炸位置之间的高度差（DH），被选为影响指标。在RK露天矿铜钴矿测量了188条爆破作业数据。然后，研究了PPV的RUN-XGBoost预测模型，并与Sadovsky经验公式进行了比较，传统的XGBoost模型，PSO-XGBoost模型,和一些传统的机器学习模型(Ridge，拉索,SVM,和SVR)使用R2、RMSE、VAF,MAE,和MBE作为模型预测结果的评价指标。最后,采用Shapley加性解释(SHAP)方法评价不同影响指标对PPV预测结果的贡献。结果表明,RUN-XGBoost预测模型对PPV的预测效果明显优于其他机器学习模型和Sadovsky经验公式,进一步证明RUN-XGBoost预测模型能够处理多因素的非线性特征,简单,和有效的PPV预测模型，形成了露天开采爆破振动的快速预测和评价方法。
