Intelligent decision‐making for parameters of slurry shield tunneling based on multi‐objective particle swarm and genetic hybrid algorithm

In slurry shield tunneling, relying solely on human experience to adjust active control parameters manually often has limitations and can lead to safety hazards. Therefore, the intelligent decision‐making for parameters of slurry shield tunneling based on machine learning algorithms is of great significance. This paper established a hybrid algorithm that combines the Bayesian method with the long short‐term memory neural network to predict passive response parameters of slurry shield tunneling, achieving accurate predictions of slurry pressure and specific energy. Additionally, a hybrid algorithm combining particle swarm optimization with genetic algorithms is studied for the multi‐objective optimization control of active control parameters in slurry shield tunneling. The objective is to minimize both in the specific energy and the error of slurry pressure during excavation. To facilitate practical engineering applications, an intelligent decision‐making software for shield machine active control parameters is designed. This software provides a visualization interface for parameter optimization of slurry shield tunneling.