岩石巷道爆破效果预测及应用效果实践研究

Forecast model of rock roadway blasting effect and its application result

为更准确地预测岩石巷道的爆破效果,以提高爆破效率和降低生产成本,基于随机森林方法确定了影响爆破效果的6个关键因素:总装药量、断面面积、炮眼深度、掏槽眼装药量、辅助眼装药量、周边眼装药量,构建基于网格搜索法-支持向量机回归预测模型,以平均绝对误差和相关系数为评价指标,预测炸药单耗。建立了径向基核函数、多项式核函数和线性核函数三种核函数的支持向量机模型,并采用随机森林回归算法作为对照组。结果表明,SVR-Rbf组表现最好,在数据库和顾北煤矿实际案例的预测中相关系数均达到0.95左右,平均绝对误差也至少比其他组小一倍左右,并将最优模型应用于顾北矿岩石巷道爆破炸药单耗预测,效果良好,表明建立的Grid Search CV-SVM预测模型是预测岩石巷道爆破效果有效方法。

In order to more accurately predict the blasting effect of rock roadway, improve blasting efficiency and reduce production costs, 6 factors of total charge, roadway cross-sectional area, blast hole depth, cuthole charge, auxiliary cuthole charge, and peripheral cuthole charge are selected as input, and a grid search Cross Validation-Support Vector Machine(Grid Search CV-SVM) regression prediction model is established. The average absolute error and correlation coefficient are taken as evaluation indicators to predict the unit consumption of explosives. SVM uses radial basis kernel function(Rbf), polynomial kernel function, and linear kernel function to compare three kernel functions with each other, and introduces random forest regression algorithm as the control group. The results show that the SVR-Rbf group performed the best. The correlation coefficients in the database and the actual case prediction of the Gubei coal mine were both about 0.95, and the average absolute error was about half of the other groups’ at most. The performance of the Random Forest prediction group is not as good as the three SVM prediction groups, and the optimal model is applied to the prediction of the unit consumption of explosives for rock tunnel blasting in Gubei Mine, and the result is favorable. The established Grid Search CV-SVM prediction model is a valid method to predict the effect of rock roadway blasting.