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The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.
Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.
The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.
By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Dietrich, R., Opper, M., & Sompolinsky, H. (1999). Statistical mechanics of support vector networks. Physical Review Letters, 82(14), 2975.
Honer, P. C., & Sherrell, F. W. (1977). The application of air-flush rotary percussion drilling techniques in site investigation. Quarterly Journal of Engineering Geology and Hydrogeology, 10(3), 207–220.
National Railway Administration of the People’s Republic of China (2017). Railway & train standard of the People’s Republic of China: TB 10003—2016 Code for design of railway tunnel. Beijing: China Railway Publishing House. (in Chinese).
Nishitsuji, Y., & Exley, R. (2019). Elastic impedance based facies classification using support vector machine and deep learning. Geophysical Prospecting, 67(4), 1040–1054.
Niu, G. G., Zang, K., Yu, B. S., Chen, Y. L., Wu, Y., & Liu, J. F. (2019). Experimental study on comprehensive real-time methods to determine geological condition of rock mass along the boreholes while drilling in underground coal mines. Shock and Vibration, 2019, 1–17.
Qin, M., Wang, K., Pan, K., Sun, T., & Liu, Z. (2018). Analysis of signal characteristics from rock drilling based on vibration and acoustic sensor approaches. Applied Acoustics, 140, 275–282.
Tan, Z., Cai, M., Yue, Z., Tan, G., & Li, C. (2006). Theory and approach of identification of Ground interfaces based on rock drillability index. Journal of University of Science and Technology Beijing, 28(9), 803–807, (in Chinese).
Tan, Z., Yue, Z., & Cai, M. (2007). Analysis of energy for rotary drilling in weathered granite formation. Chinese Journal of Rock Mechanics and Engineering, 26(3), 478–483, (in Chinese).
Tan, Z., Cai, M., Yue, Z., Tan, G., & Li, C. (2007). Interface identification in weathered granite strata based on a instrumented drilling system. Journal of University of Science and Technology Beijing, 29(7), 665–669, (in Chinese).
Tan, Z., Yue, Z., Tan, G., & Li, C. (2008). Relationship between diamond penetrating energy and weathered degree in granite formation. Journal of University of Science and Technology Beijing, 30(4), 339–343, (in Chinese).
Tan, Z., Li, W., Yue, P., Wang, L., Li, J., Qi, K., & Zhou, D. (2015). Techniques and approaches for identification of geo-formation structure based on diamond drilling parameters. Chinese Journal of Geotechnical Engineering, 37(7), 1328–1333, (in Chinese).
Valentín, M. B., Bom, C. R., Coelho, J. M., Correia, M. D., De Albuquerque, M. P., & Faria, E. L. (2019). A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs. Journal of Petroleum Science and Engineering, 179, 474–503.
Wedge, D., Hartley, O., Mcmickan, A., Green, T., & Holden, E. J. (2019). Machine learning assisted geological interpretation of drillhole data: Examples from the pilbara region, western Australia. Ore Geology Reviews, 114, 103118.
Yue, Z. (2014). Drilling process monitoring for refining and upgrading rock mass quality classification methods. Chinese Journal of Rock Mechanics and Engineering, 33(10), 1977–1996, (in Chinese).
Zhao, H. (2005). Predicting the surrounding deformations of tunnel using support vector machine. Chinese Journal of Rock Mechanics and Engineering, 24(4), 649–652, (in Chinese).
Zhu, X., Pan, Y., Zhang, J., Wang, S., Gu, X., & Xu, C. (2007). The effects of training samples on the wheat planting area measure accuracy in TM scale (Ⅰ): The accuracy response of different classifiers to training samples. Journal of Remote Sensing, 11(6), 826–837, (in Chinese).
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