Relationship between TBM tunneling parameters and rock mass grades based on GA-LightGBM algorithm

Abstract:

In order to establish an identification model of rock mass grade reflecting the relationship between TBM tunneling parameters and rock mass categories, improve model building efficiency and recognition rate, a research was carried out on background of a tunnel project. The rock mass characteristics were surveyed and graded based on BQ method and RQD, the TBM working data was collected and the main excavation parameters related to the change of rock mass characteristics were screened. The relationship between TBM tunneling parameters and rock mass grade was fitted based on Light Gradient Boosting Machine (LightGBM) algorithm, and the hyperparameters of LightGBM were optimized using genetic algorithm (GA), then a GA-LightGBM model of rock mass grades identification was established. Results: The accuracy of the GA-LightGBM recognition model reached 93.5%, which was higher than that of the support vector machine model and the random forest model. The model training speed is 8 times faster than the gradient boosting decision tree algorithm. Five TBM tunneling parameters were related to rock strength and rock mass integrity, and the total propulsion force could be used as the main criterion for sensing rock mass characteristics. The study provides an efficient method for analyzing TBM excavation parameters and accurately identifying rock mass grades, providing support for rapid on-site perception of rock mass grades and real-time adjustment of operating parameters.