Shield Tunneling Parameters Matching Based on Support Vector Machine and Improved Particle Swarm Optimization

Abstract

Of late, emerging algorithms such as machine learning have been increasingly used in shield tunneling construction management and control. This research article proposes a shield tunneling parameter matching model based on a support vector machine (SVM) and an improved particle swarm algorithm (PSO) to enhance the accuracy and reliability of shield tunneling parameter selection. First, the optimization performance of the algorithm is augmented by adjusting particle diversity. Simulation-based experiments are conducted to test the performance of the improved algorithm. The experimental results indicate that the improved algorithm has better accuracy. Meanwhile, the particle diversity adjustment strategy is given. Then, the SVM model to express the relationship between shield tunneling parameters and ground settlement is established and trained. Based on the obtained SVM model, the improved PSO is used to optimize the tunneling parameters. The results show that the shield tunneling parameter matching model based on SVM and improved PSO can obtain more accurate shield tunneling parameters and provides a more precise reference for selecting shield tunneling parameters.