Prediction of TBM Roadway Surrounding Rock Grade Based on XGBoost Model

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.