Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock. The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps. First, a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes, where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress. Then, the GP regress model optimized by DE is trained by the data samples. Finally, the GP model is integrated into the DE algorithm, and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function, while the parameters of the jointed surrounding rock are used as variables and identified. The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel, which is located in Dalian, China. The obtained calculation and analysis results are as follows: CR = 0.9, F = 0.6, NP = 100, and the difference strategy DE/Best/1 is recommended. The results of the back analysis are compared with the field monitored values, and the relative error is 4.58%, which is satisfactory. The algorithm influencing factors are also discussed, and it is found that the local correlation coefficient sigma f and noise standard deviation sigma n affected the prediction accuracy of the GP model. The results show that the proposed method is feasible and can achieve high identification precision. The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.