PCA-GWO-SVR机器学习用于边坡爆破振动速度峰值预测研究

PCA-GWO-SVR machine learning applied to prediction of peak vibration velocity of slope blasting

针对复杂场地环境下传统经验公式预测精度不高的问题,提出了一种主成分分析(PCA)特征选取下基于灰狼优化支持向量回归机算法(PCA‐GWO‐SVR)的爆破振动速度峰值预测模型。以白鹤滩水电站右岸坝肩槽爆破开挖监测数据为依据,选取爆心距、单响药量、高程差、纵波波速、炮孔间距、炮孔排距作为输入参数,通过PCA的数据降维对特征值进行选取,将选取的6种特征降维后化为4种相关性更高的特征;使用灰狼优化算法(GWO)改进支持向量回归机(SVR)以获取最优参数;将参数输入到SVR模型中进行计算评估。研究结果表明:PCA‐GWO‐SVR算法对比萨道夫斯基公式,改进的萨道夫斯基公式,SVR,PCA‐SVR和GWO‐SVR的预测值和实测值的吻合效果更好,预测结果的准确度更高,更能有效地预测边坡爆破振动峰值,为边坡爆破施工安全控制提供帮助。

Aiming at the low accuracy of traditional empirical formulas in complex site environment,a predictive model for peak blasting vibration velocity based on grey wolf optimization support vector regression(PCA-GWO-SVR)with principal component analysis(PCA)feature selection is proposed.Based on the monitoring data of blasting excavation of dam abutment trough on the right bank of Baihetan Hydropower Station,the blasting center distance,maximum single-shot charge quantity,elevation difference,longitudinal wave velocity,bore spacing and bore row distance are selected as input parameters,and the characteristic values are selected by data dimension reduction of PCA,and the six selected features are dimensionally reduced to four characteristics with higher correlation.Support vector regression(SVR)is improved by grey wolf optimization algorithm(GWO)to obtain the optimal parameters.Parameters are input into the SVR model for evaluation.The research results show that the PCA-GWO-SVR algorithm has better agreement with the predicted values and the measured values of Sadowski formula,improved Sadowski formula,SVR,PCA-SVR,GWO-SVR.The predicted results are more accurate and can predict the peak value of blasting vibration of slope more effectively,which provides help for safety control of blasting construction of slope.