Abstract: Due to the challenges posed by varying factors such as environmental conditions, equipment performance, and operational procedures, achieving an optimal balance between safety, efficiency, and cost in tunnel excavation is complex. To address this problem, a case study of a section of the Wuhan rail transit is conducted, proposing a hybrid intelligent framework that integrates categorical boosting(CatBoost) and nondominated sorting genetic algorithm(NSGA)-Ⅲ. This framework aims to develop an intelligent control decision-making system for construction parameters, considering factors such as advance speed, tunneling specific energy, and tool wear. The approach begins with the use of the CatBoost model to predict advance speed, specific energy, and tool wear, which then informs the fitness function for control targets. Following this, the CatBoost-NSGA-Ⅲ algorithm is employed for multi-objective optimization of construction parameters based on the fitness function derived from the CatBoost model. Optimal parameter combinations are subsequently selected from the multiple Pareto optimal solutions using a fuzzy decision method. This approach supports intelligent prediction and optimization of construction parameters for shield tunneling. Key findings include: (1) The CatBoost model provides accurate predictions with a goodness of fit exceeding 0.9. (2) Multi-objective optimization using the CatBoost-NSGA-Ⅲ algorithm, combined with fuzzy decision-making, determines the optimal scheme. Compared to the average measured data, this scheme reduces specific driving energy and tool wear by 5.3% and 13.5%, respectively, while increasing advance speed by 6.3%, thereby enhancing intelligent management and decision-making for shield tunneling.

Key words:  shield tunneling, advance speed, tunneling specific energy, tool wear, construction parameters, multi-objective optimization, categorical boosting-nondominated sorting genetic algorithm-Ⅲ algorithm