Prediction of TBM Roadway Surrounding Rock Grade Based on XGBoost Model

Zhang Hu; Zhanbiao Yang

doi:10.54097/mrsqak79

Authors

Zhang Hu
Zhanbiao Yang

DOI:

https://doi.org/10.54097/mrsqak79

Keywords:

Excavation parameters, XGBoost model.

Abstract

The classification of roadway surrounding rock is a very important link in the process of TBM construction. Its main purpose is to determine the drilling parameters such as bit type and cutterhead speed used in the process of shield tunneling according to different surrounding rock characteristics, so as to ensure the safety and efficiency of construction. With the development of machine learning and artificial intelligence technology, these technologies are gradually applied to the classification of tunnel surrounding rock. This method can establish a model through a large number of data analysis, so as to quickly and accurately classify and predict different types of surrounding rock. Based on Shoushan No.1 Mine, the XGBoost model was used to classify the surrounding rock types of the selected sample tunnel sections, and the tunneling parameters affecting the classification of shield surrounding rock were analyzed. The classification model of shield tunnel surrounding rock was established and the model was preliminarily tested. The results show that thrust, rotational speed, torque and propulsion speed are the main factors affecting the grade of surrounding rock. The XGBoost model is used to identify the surrounding rock grade in real time. The samples of grade III and grade V surrounding rock are all predicted, with an accuracy rate of 100 %. The accuracy rate of grade II and grade IV surrounding rock is 90 %, and the total accuracy rate reaches 95 %. The prediction effect is good.

Downloads

Download data is not yet available.

References

[1] Li Hongbo, Zhang Dongyue, Ge Xueyuan． Tunnel boring machine tunneling parameters prediction method based on PSO-LSSVM algorithm [J]. Science Technology and Engineering, 2023, 23( 14) : 6230-6237.

[2] LIU Ruilin, MAN Ke, LIU Xiaoli, et al. Prediction of ground conditioning effect of water-rich sandy stratum based on genetic algorithm-back propagation neural network[J]. Tunnel Construction, 2023, 43(S1): 222.

[3] LI Taibin, ZHANG Chong, YAN tiancheng, et al. Intelligent EarlyWarning System for Uneven Blade Opening Fault of Hydraulic Turbines based on Big Data. [J]. Technology Innovation and Application, 2020, (11): 120-121.

[4] Gholami R. Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms[J]. Rock Mechanics & Rock Engineering, 2013, 46 (5): 1199-1209.

[5] Jian XU, Wang J, Yan MA. Rock Mass Quality Assessment Based on Bp Artificial Neural Network (ANN)——a Case Study of Borehole Bs03 inJiujing Segment of Beishan, Gansu[J]. Uranium Geology, 2007, 23 (4): 249-455.

[6] Sanio H P. Prediction of the performance of disc cutters in aniso tropic rock[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1985, 22(3): 153-161.

[7] Rosutami J. A new model for performance prediction of hard rock TBMs[C]//Proceedings/1993 rapid excavation and tunneling conference. 1993.

[8] Yang Yaohong, Han Xingzhong, Zhang Zhixiao, et al. The interval prediction of tunnel boring machine penetration rate under condition of mixed face ground and small diameter[J]. Science Technology and Engineering, 2023, 23(34): 14638-14650.

[9] WANG Jian, WANG Ruirui, ZHANG Xinxin, et al. Estimation of TBM Performance Parameters Based on Rock Mass Rating (RMR) System[J].Tunnel Construction, 2017, 37(6): 59–66.

[10] Hassanpour J, Rostami J, Zhao J. A new hard rock TBM performance prediction model for project planning[J]. Tunnelling and Underground Space Technology, 2011, 26(5): 595-603.

[11] MAN Ke, CAO Zixiang, LIU Xiaoli, et al. The prediction of TBM tunnel boring parameters based on GRU-RF model[J].Jouranal of Basic Science and Engineering, 2023, 31(06):1519-1539.