A Modular Configuration Method for Tunnel Boring Machine Based on Improved Bayesian Inference and Multi-objective Optimization

To address the problems of strong dependence on expert experience, low scheme generation efficiency, and difficulty in balancing multiple objectives in tunnel boring machine (TBM) configuration under complex geological conditions, this paper proposes a modular configuration method integrating improved Bayesian inference and multi-objective optimization. First, a generic bill of materials (GBOM) model for the TBM product family is established to describe module hierarchies, instance attributes, and configuration constraints. Second, the naive Bayes model is improved by combining mutual-information-based weighting and expert prior correction, so as to realize module-instance inference for each module family. Third, a three-objective optimization model is formulated with configuration cost, delivery time, and adaptability risk as the objectives and solved using an improved NSGA-II algorithm. Finally, the proposed method is validated using 27 historical engineering cases through leave-one-out cross-validation, supplementary sensitivity analysis, retrospective validation, and a synthetic scale-expansion experiment. The results show that the proposed framework achieves a mean LOOCV accuracy of 72.75% at the module-family level, exhibits robustness to moderate subjective-weight perturbations. A retrospective validation based on case indicates that the historical scheme is feasible but not globally balanced in the three-objective sense, while the proposed method can provide a clearer trade-off boundary for engineering decision-making. The study provides a feasible methodological reference for rapid TBM modular configuration under complex working conditions.