Comparisons of Random Forest and Support Vector Machine for Predicting Blasting Vibration Characteristic Parameters
The prediction of blasting vibration characteristic parameters is very important to evaluate the situations of blasting vibration damage. Blasting vibration of rock mass is affected by lots of characteristics, such as charging parameter, rock type and geological topography. The characteristics should be comprehensively considered in order to accurately predict the blasting vibration. Based on training and testing 93 sets of measured data in an open-pit mine, Support Vector Machine (SVM) and Random Forest (RF) methods are applied to predict the peak particle velocity (PPV), first dominant frequency and duration time of first dominant frequency of blasting vibration. The other 15 groups of measured data are tested as forecast samples, of which the predicted results are consistent with the measured ones. Results show that the prediction accuracies of SVM and RF models were acceptable. The average error rate of SVM is lower than results using RF, and the weight of factors is determined using RF. It is a new approach to predict destructive effect on housing under blasting vibration using SVM and RF, which can be applied to practical engineering.
blasting engineering SVM RF PPV first dominant frequency duration time first dominant frequency
Dong Longjun Li Xibing Xu Ming Li Qiyue
School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, China
国际会议
The First International Symposium on Mine Safety Science and Engineering (首届矿山安全科学与工程学术会议)
北京
英文
305-314
2011-10-27（万方平台首次上网日期，不代表论文的发表时间）