Shield Tunneling Parameter Matching Model and UI Interface

In order to improve the accuracy of shield tunneling parameter matching under the limited data, the matching model based on support vector machine (SVM) and exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) is proposed. The nonlinear relationship model between the tunneling parameters and the ground settlement is constructed by SVM and trained with the actual engineering sample data. Based on the trained model, EAIW-IPSO is used to optimize the tunneling parameters. At the same time, UI interface was developed based on the tunneling parameter matching model. The matching model based on BP neural network and PSO algorithm is compared in simulation experiments and engineering case. It is verified that the matching model based on SVM and EAIW-IPSO still maintains great accuracy and stability as the number of samples continues to decrease. The paper provides a better solution for the matching of tunneling parameters in actual engineering.