期刊论文

Study on prediction of blasting vibration velocity basedon Grey Wolf Algorithm improved Random Forest Algorithm

中文

黄金

1001-1277

2024

45

1

12-16

10.11792/hj20240103

湖南省研究生科研创新项目(QL20210225)。

本校为第一机构

为改善露天矿爆破作业过程中的峰值质点速度在采用传统经验公式和单一仿生算法时预测精度不够的问题,引入灰狼算法(GWO)优化了随机森林算法(RF)中决策树的棵数和层数2个超参数,成功构建了基于灰狼算法改进的GWO-RF爆破振...展开更多 为改善露天矿爆破作业过程中的峰值质点速度在采用传统经验公式和单一仿生算法时预测精度不够的问题,引入灰狼算法(GWO)优化了随机森林算法(RF)中决策树的棵数和层数2个超参数,成功构建了基于灰狼算法改进的GWO-RF爆破振动速度预测模型。结合某爆破工程69组爆破监测数据,以爆心距、最大段药量、总装药量、微差时间、炮孔数、孔距、孔深及排距为输入参数,运用GWO-RF...

In order to improve the prediction accuracy of peak particle velocity during open-pit blasting operations,which is insufficient when using traditional empirical formulas and single bionic algorithms,the Grey Wolf Algo-rithm(GWO)is introduced to optimize 2 hyperparameter...MORE In order to improve the prediction accuracy of peak particle velocity during open-pit blasting operations,which is insufficient when using traditional empirical formulas and single bionic algorithms,the Grey Wolf Algo-rithm(GWO)is introduced to optimize 2 hyperparameters,the number and depth of decision trees in the Rand...