An intelligent model that considers lithology in classifying surrounding rock is established based on drilling parameters so as to further improve the accuracy of predicting the surrounding rock.First,the drilling parameters collected by a drilling trolley in the Yichang-Xingshan and Zhengzhou-Wanzhou high-speed railway tunnels are divided into the five data subsets of dolomite,granodiorite,limestone,sandstone,and shale.Next,data cleaning and feature extraction are conducted,and each data subset and original data set are divided into a training set and test set in a 4∶1 ratio.Furthermore,the extracted features and the original features are used as inputs to establish five intelligent classification models for surrounding rock based on drilling parameters and lithology and an intelligent classification model for surrounding rock based on drilling parameters using a machine learning random forest algorithm.Finally,the generalization performance of each model is evaluated.The results demonstrate the following.(1)The models that are based on dolomite,granodiorite,limestone,sandstone,and shale achieve an accuracy on the test set of 85.48%,92.16%,88.62%,85.00%,and 89.47%,respectively,and the accuracy of the model that does not consider lithology is 84.91%.(2)Compared with the improvement in the model that considers lithology,the improvement in the accuracy on the test set of the surrounding rock intelligent classification model based on drilling parameters and lithology is 0.09%-7.25%,and the improvement in model accuracy is 1.61%-13.82%compared with no feature extraction.This shows that extracting the features of the drilling parameters effectively improves the accuracy of the model.The intelligent model that considers lithology in surrounding rock classification is more stable and achieves better generalization performance than the model that does not consider lithology.
tunnel engineeringintelligent surrounding rock classificationrandom forestdrilling parametersfeature extractionlithology
NETL
NSTL
万方数据
