Reseach On TBM Tunneling Efficiency Evaluation And Surrounding Rock Classification Based On Rock-Machine Parameters Fusion

The development and application of the full-section rock tunneling machine(TBM)has played a huge role in promoting the construction of highway and railway tunnels,mine roadways and water conservancy and hydropower engineering tunnels.However,compared with the traditional drilling and blasting method,the disadvantage of the TBM method is poor adaptability to geological conditions.For long tunnels,under normal circumstances,the geological conditions are relatively complex,and the development probability of unfavorable geological conditions is large.TBM encounters unfavorable geological problems and is prone to problems of driving efficiency and low equipment utilization rate.High quality and other advantages have caused unnecessary losses such as delay in construction period and increased costs.Therefore,how to accurately and efficiently predict the TBM tunneling efficiency has a crucial role in the feasibility demonstration and economic cost evaluation of actual projects.At the same time,because the driving speed is not directly related to the surrounding rock classification of traditional underground engineering,most of the current rock classification standards focus on the analysis and classification of surrounding rock stability,which is more suitable for the traditional drilling and blasting tunnel construction.The classification system does not take into account the TBM’s driving efficiency.Therefore,the surrounding rock classification method of the TBM construction tunnel should not only be able to evaluate the stability of the rock mass,but also reflect the TBM’s driving efficiency.It is necessary to establish new TBM surrounding rock classification rules from the perspective of TBM driving speed to distinguish the excavability of surrounding rock of TBM construction tunnels.Considering the TBM’s driving efficiency,the surrounding rock classification of the tunnel based on the driving performance of TBM is very important for efficient,fast and safe driving.In order to solve all the above problems,this article uses the principal engineering analysis method to optimize the index parameter system based on the actual engineering data,and then introduces the support vector machine regression model to predict and research the TBM tunneling efficiency.TBM construction tunnel surrounding rock’s excavability and machine’s adaptability to the stratum,taking TBM construction speed as the classification standard,a new tunnel surrounding rock classification method based on TBM’s driving performance was built;on this basis,based on a large number of engineering site Based on the measured data,an improved BP neural network pattern recognition model was used to establish a tunnel surrounding rock grade prediction model based on TBM driving performance.Through actual engineering verification,the results show that the predicted results and the actual matching degree is good.This prediction model has important construction guidance significance for the identification of the surrounding rock excavation performance grade in TBM construction,and helps reduce the negative impact of human experience on construction.