台阶爆破岩石破碎平均粒径预测的支持向量机方法(英文)

针对传统的岩石台阶爆破破碎评估问题,运用统计学理论,建立预测不同矿山岩石爆破破碎后的平均粒径(X50)的支持向量机(SVMs)回归模型。爆破参数包括爆破设计参数、炸药参数、弹性模量及现场块度大小。SVMs模型选用7个参量作为预测岩石爆破破碎的平均粒径X50输入自变量:台阶高度与钻孔荷载比(H/B),间距与荷载比(S/B),荷载与孔径比(B/D),炮泥与荷载比(T/B),粉因数(PF),弹性模量(E)和现场块度大小(XB)。利用世界各地不同矿山和岩层测量的90组数据来训练和测试SVMs模型,其他12组爆破数据来验证该模型的有效性,并将SVR的预测结果与人工神经网络(ANN)、多元回归分析(MVRA)、传统的Kuznetsov方法及X50实测值进行比较。该方法显现出很好的效果,其预测精度是可以接受的。

Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction

Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting,a support vector machines(SVMs) regression methodology was used to predict the mean particle size(X50) resulting from rock blast fragmentation in various mines based on the statistical learning theory.The data base consisted of blast design parameters,explosive parameters,modulus of elasticity and in-situ block size.The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden(H/B),ratio of spacing to burden(S/B),ratio of burden to hole diameter(B/D),ratio of stemming to burden(T/B),powder factor(Pf),modulus of elasticity(E) and in-situ block size(XB).After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing,the model was applied to 12 another blast data for validation of the trained support vector regression(SVR) model.The prediction results of SVR were compared with those of artificial neural network(ANN),multivariate regression analysis(MVRA) models,conventional Kuznetsov method and the measured X50 values.The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.