露天矿山台阶爆破矿岩平均块度的FOA-SVM预测

露天矿山台阶爆破后矿岩的平均块度是衡量爆破质量的重要指标,对后续的铲装和运输也具有重要的意义。为了对台阶爆破后的矿岩平均块度进行预测,使用果蝇优化算法(FOA)对支持向量机回归模型(SVM)进行参数优化,通过建立基于果蝇优化算法的支持向量机回归模型(FOA-SVM)对矿岩爆破平均块度进行预测,避免传统的SVM参数选取对预测结果准确性产生的影响。结果表明,FOA-SVM可实现对矿岩爆破平均块度的较好预测。

Prediction of average block size of rock in open-pit mine bench blasting based on FOA-SVM

The average block size of rock after bench blasting in open-pit mine is an important measurement for blasting quality.It is also significant for subsequent shoveling and transportation.In order to predict the average block size after bench blasting,the parameters of Support Vector Machine(SVM)were optimized using Fruit Fly Optimization Algorithm(FOA).By establishing FOA-SVM,the average block size of rock after blasting is predicted.The impact of traditional SVM s parameters selection on the accuracy of the prediction results is avoided.The results show that the FOA-SVM can achieve a better prediction of the average block size of rock blasting.