Blasting-Induced Peak Particle Velocity Prediction of Hole-by-Hole Blasting Operation Using Digital Electronic Detonator in Open-Pit Mine
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摘要: 针对目前露天矿爆破质点峰值振速预测研究存在模型可解释性不足、不适用于数码电子雷管逐孔起爆条件等问题，通过现场试验记录每孔爆破参数与测取爆破振动信号，结合轻型梯度提升机（LightGBM）算法与SHAP模型可解释性框架，建立了露天矿数码电子雷管逐孔起爆条件下的三轴质点峰值振速预测模型.从测试集均方根误差RMSE和拟合优度R2而言，LightGBM总体RMSE相比于支持向量机与神经网络分别降低了25.9%和28.9%，总体R2分别提高了12.7%和9.9%.LightGBM与萨道夫斯基经验公式相比，RMSE在径向X、切向Y和垂向Z上分别降低了63.4%、39.5%和68.3%，R2分别提高了18.9%、27.7%和42.4%.除方向轴变量外，监测点距离、总药量、最小排距、平均装药长度、孔径与最大孔距为对质点峰值振速影响程度最大的6个变量，其中监测点距离与质点峰值振速为负相关关系，总药量、最小排距、平均装药高度与最大孔距则与质点峰值振速呈正相关关系.Abstract: Current research on blasting-induced peak particle velocity prediction in open-pit mines is infeasible for the hole-by-hole blasting operation using digital electronic detonators, and its models lack interpretability. By recording the blasting parameters for each blasthole and measuring the induced triaxial particle velocity, a model to predict the triaxial peak particle velocity is established based on LightGBM, and SHAP is introduced to interpret the variable importance of the model. Regarding the root mean squared error RMSE and goodness of fitting R2 on the test set, the established LightGBM model outperforms the support vector machine and neural network models as its RMSE decreases by 25.9% and 28.9%, while the R2 improves by 12.7% and 9.9%. Compared to the Sadaovsk empirical formula, which is widely applied to the blasting design and safety evaluation, the RMSE of LightGBM declines by 63.4%, 39.5% and 68.3%, while the R2 increases by 18.9%, 27.7%and 42.4% in the longitudinal axis X, transverse axis Y and vertical axis Z, respectively. The SHAP values computed from the model inform that apart from the axis variable, the distance between blasting source and monitoring point, total charge, the minimum row distance, the average charge length, hole diameter and the maximum hole distance are the six most influencing variables that affect the predicted value of peak particle velocity. The distance between blasting source and monitoring point is negatively correlated with the predicted peak particle velocity, while the other five variables are positively correlated with the predicted value.
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图 2 经验公式测试集RMSE与R2对比
Fig. 2. Test set RMSE and R2 of the empirical formulas
a.RMSE; b.R2
图 3 训练集与测试集模型总体RMSE与R2对比
a.训练集；b.测试集
Fig. 3. Training set and test set RMSE and R2 of the models
图 4 测试集模型真实值‒预测值散点图
a.LightGBM；b.SVM；c.NN；d.萨氏公式
Fig. 4. Test set actual PPV versus predicted PPV
图 5 测试集模型三轴RMSE与R2对比
Fig. 5. Test set triaxial RMSE and R2 of the models
a. RMSE; b. R2
表 1 爆破质点峰值振速记录例表
Table 1. Example sheet of recorded peak particle velocity
监测点位置 监测点距离(m) 径向X峰值振速(cm/s) 切向Y峰值振速(cm/s) 垂向Z峰值振速(cm/s) 宿舍楼 1 758 0.15 0.20 0.19 办公楼 1 635 0.16 0.16 0.16 锅炉房 1 567 0.21 0.23 0.19 变电站 1 545 0.24 0.25 0.30 现场1 73.7 4.11 4.17 9.65 现场2 155.5 2.92 2.14 2.89 
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表 2 爆破参数记录例表
Table 2. Example sheet of recorded blasting parameters
孔号 孔径(mm) 台阶高度(m) 孔深(m) 孔距/排距(m) 药量(kg) 装药高度(m) 填塞长度(m) 1 250 15.7 18.2 17/9.5 687 10.9 7.5 2 250 15.6 18.1 17/9.5 680 10.8 7.5 3 250 15.8 18.3 17/9.5 693 11 7.5 4 250 15.5 18.0 17/9.5 674 10.7 7.5 5 250 15.7 18.2 17/9.5 693 11 7.5 ······ 50 250 15.6 18.1 17/9.5 680 10.8 7.5
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表 3 模型变量
Table 3. Modeling variables
原始变量 模型变量 质点峰值振速 对数质点峰值振速 监测点距离 监测点距离 孔径 孔径 孔数 孔数 坐标轴 径向X独热编码 切向Y独热编码 垂向Z独热编码 单孔装药量 最大单孔装药量 平均单孔装药量 最小单孔装药量 总药量 台阶高度 最大台阶高度 平均台阶高度 最小台阶高度 孔深 最大孔深 平均孔深 最小孔深 孔距 最大孔距 平均孔距 最小孔距 排距 最大排距 平均排距 最小排距 装药高度 最大装药高度 平均装药高度 最小装药高度 填塞长度 最大填塞长度 平均填塞长度 最小填塞长度
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表 4 LightGBM超参数
Table 4. Hyperparameters of LightGBM
超参数 参数取值 迭代次数 90 学习率 0.06 约束系数$ \gamma $ 0.05 约束系数$ \lambda $ 0.1 最大叶节点数 50 最大深度 6 每次迭代选用变量比例 1 直方图离散区间最大数目 255 直方图离散区间最小样本数 3 叶节点最小Hessian值之和 1e-3 数据重抽样步长 5 数据重抽样比例 0.5
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表 5 SVM超参数
Table 5. Hyperparameters of SVM
超参数 参数取值 核函数形式 径向基核函数RBF RBF核函数gamma值 0.05 惩罚系数 4 容忍度 1e-3
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表 6 NN超参数
Table 6. Hyperparameters of NN
超参数 参数取值 激活函数形式 tanh 隐含层神经元个数 30 隐含层层数 4 L2正则项系数 1e-4 学习率 1e-3 容忍度 1e-4
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