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Intelligent Identification Method of Surrounding Rock Grades of Tunnel Face Based on Drilling Parameters
(1. Guizhou Road & Bridge Group Co., Ltd., Guiyang 550000；2. School of Civil Engineering, Central South University,Changsha 410075；3.Chongqing Geological Exploration and Mineral Resources Development Group Inspection and Testing Co., Ltd.,Chongqing 400700)
Abstract To address the complexities and subjectivity in traditional rock mass classification methods for drill-andblast tunnels, which have limitations when assessing complex rock masses, this study proposes an intelligent method for identifying tunnel surrounding rock grades. The method involves fitting the distribution of drilling parameters using the kernel density estimation method and employing a Naive Bayes classification algorithm for rock classification.The performance of the classification model was enhanced through cross-validation, and the method was validated using literature data. The results demonstrate that the Naive Bayes classification algorithm based on kernel density estimation can accurately classify the quality of the tunnel face surrounding rock using drilling parameters, achieving a classification accuracy of 94.0% in the test set. Furthermore, the cross-validation method improved the model′s performance, reaching a classification accuracy of 98.7% on a test set of 299 samples.
Abstract：
To address the complexities and subjectivity in traditional rock mass classification methods for drill-andblast tunnels, which have limitations when assessing complex rock masses, this study proposes an intelligent method for identifying tunnel surrounding rock grades. The method involves fitting the distribution of drilling parameters using the kernel density estimation method and employing a Naive Bayes classification algorithm for rock classification.The performance of the classification model was enhanced through cross-validation, and the method was validated using literature data. The results demonstrate that the Naive Bayes classification algorithm based on kernel density estimation can accurately classify the quality of the tunnel face surrounding rock using drilling parameters, achieving a classification accuracy of 94.0% in the test set. Furthermore, the cross-validation method improved the model′s performance, reaching a classification accuracy of 98.7% on a test set of 299 samples.
XU Jianfeng1 ZHANG Xiangchuan1 QIN Guifang1 KUANG Huajiang1 LIU Guanghui1 DENG Xingxing1
.Intelligent Identification Method of Surrounding Rock Grades of Tunnel Face Based on Drilling Parameters[J] MODERN TUNNELLING TECHNOLOGY, 2024,V61(5): 79-87
2024, Vol. 61 