摘要
为解决露天矿山爆破复杂场地地质条件的爆破振动预测问题,提出了一种基于马氏距离判别(MD)和主成分分析(PCA)的改进BP神经网络预测模型,即MD-PCA-BP模型。结合内蒙古长滩露天矿爆破振动监测数结果,利用马氏距离判别法剔除监测数据的离群值,并采用主成分分析法对爆破振动影响因素进行降维处理得到3个主成分因子,计算各主成分因子的得分,最终通过BP神经网络构建爆破振动与主成分得分的非线性关系,建立了基于MD-PCA-BP的爆破振动预测模型。结果表明:基于MD-PCA-BP模型建立的爆破振动速度预测模型预测结果与实测值的拟合度达到0.94,预测模型具有较高的预测精度;将预测结果与萨道夫斯基经验公式、2个改进的高程经验公式、MD-BP模型、PCA-BP模型以及BP模型进行比较,MD-PCA-BP模型的预测误差大部分在10%以内,相较于经验公式和未改进的BP预测模型具有更高的可靠度和准确度。基于MD-PCA-BP的爆破振动预测模型在复杂地形的爆破振动速度预测方面表现出了良好的预测效果,对复杂地形的爆破振动预测具有一定的参考作用。
In order to address the problem of predicting blasting vibration in complex geological conditions at open-pit mines,an improved BP neural network prediction model based on Mahalanobis distance discrimination(MD)and principal component analysis(PCA),namely MD-PCA-BP model,is proposed.By combining the monitoring data of blasting vibration at Changtan open-pit mine in Inner Mongolia,outliers in the monitoring data are eliminated using the Mahalanobis distance discrimination method.Then,the principal component analysis method is employed to reduce the dimensionality of factors affecting blasting vibration and obtain three principal component factors.The scores of each principal component factor are calculated,and finally a nonlinear relationship between blasting vibration and principal component scores is constructed through BP neural network to establish the prediction model based on MD-PCA-BP.The results show that the fitting degree between predicted values and measured values of blasting vibration velocity prediction model established based on MD-PCA-BP reaches 0.94,indicating high prediction accuracy of this model.When compared with Sadovsky empirical formula,two improved elevation empirical formulas,MD-BP model,PCA-BP model,and BP model,most of the prediction errors of MD-PCA-BP model are within 10%,demonstrating higher reliability and accuracy compared to empirical formulas and unimproved BP prediction models.The blast vibration prediction model based on MD-PCA-BP exhibits good predictive performance for blast vibration velocity in complex terrains.
作者
赵茉溪
杨玉民
周传波
张升
陈文忠
杨茂森
张玉琦
ZHAO Mo-xi;YANG Yu-min;ZHOU Chuan-bo;ZHANG Sheng;CHEN Wen-zhong;YANG Mao-sen;ZHANG Yu-qi(College of Engineering,China University of Geosciences(Wuhan),Wuhan 430074,China;Inner Mongolia Shengli Zhongwei Blasting Co.,Ltd.,Ordos 010300,China;Inner Mongolia Autonomous Region Public Security Department Security Management Corps,Hohhot 010051,China)
出处
《爆破》
CSCD
北大核心
2024年第2期203-211,共9页
Blasting
基金
国家自然科学基金资助项目(41972286)。
关键词
露天矿山
爆破振动
马氏距离
主成分分析
BP神经网络模型
open-pit mines
blasting vibration
Mahalanobis distance
principal component analysis
BP neural network model
