<div class="section abstract"><div class="htmlview paragraph">When the drill arm reaches the specified position, the rubber top disk of the propelling beam is pressed against the rock surface by the hydraulic cylinder force and the rock drill starts drilling. Because of the reaction force and the deformation of the drill arm, the propelling beam will be offset from its target position and vibrate, which will affect the drilling accuracy. To analyze the vibration of the propelling beam, the rigid-flexible coupled model is established. The minimum displacement offset of the propelling beam from the initial position is used as the optimization function and the parameters of the rubber top disk are used as optimization variables. The amplitude of the propelling beam at a steady state is used as the constraint. From the simulation results, the rigid-flexible coupled model can describe the vibration of the propelling beam better than the rigid model, especially during the rock drill working stage. After optimization, the offset value of the propelling beam is reduced, and the vibration amplitude at a steady state is also in a small range.</div></div>

Surrounding rock classification represents distinguishing the different grades of surrounding rock according to the hardness and integrity of surrounding rock. Accurately obtaining the surrounding rock grade of drill jumbo working face is not only the basis for selecting the tunnel position and support type, but also the key to ensure the safety of the drill jumbo's construction site. As the traditional classification methods, engineering drilling and geological mapping are time-consuming and labor-intensive. Aiming at this situation, this paper proposes an intelligent identification method of surrounding rock grade combine drilling parameters with machine learning algorithm XGBoost. Firstly, adequately analyse the correlation between drilling parameters and rock label, and select six drilling parameters as feature vectors for surrounding rock grade recognition. Then outlier processing and data screening are carried out on the data recorded by the drill jumbo. Next, we construct a model based on XGBoost to realize the rapid and accurate identification of surrounding rock grade. Finally, the effectiveness and superiority of the proposed method are demonstrated by the actual data collected by the drill jumbo in Gao Jiaping tunnel, and mix the partial data of Alianqiu tunnel together to construct 5 datasets to compare the identification performance of other classical algorithms. The results show that the recognition capability of the proposed method is superior to those of other algorithms, and the recognition accuracy of surrounding rock along the tunnel can reach 99.68%.