Abstract: To solve the problem of inaccuracy or missing information of the c and φ values for various soil strata in the geotechnical report for the shielded tunnel structures, a cooperative optimization algorithm based on the particle swarm optimization (PSO) algorithm and the Gaussian process regression(GPR) machine learning is presented. In the optimization process, the algorithm uses the approximation model of GPR to establish the relationship among the decision variables and the fitness functions. This relationship can be used as the approximate model of GPR as an evaluation tool of to resolve the time consuming problem in the engineering and can be the partial optimization during the global optimization process of PSO. This method was verified in the evaluation of the tunnel construction under cross the Ningtong Railway of the No 1 Line of Nanjing MetroRail Transportation Project. The results indicate that, compared with the basic PSO algorithm, the proposed method reduces the iteration numbers of finite difference for the back analysis and increase the efficiency. 

Key words: Geotechnical； Parameter Back Analysis, Particle Swarm Optimization (PSO), Gaussian Process Regression (GPR)