Intelligent attitude control of tunnel boring machine in synchronous operations of excavation and segment assembly

This study introduces a shield attitude control method aimed at improving the accuracy and stability of tunnel boring machine (TBM) in synchronous excavation and segment assembly (S-TBM) along the design tunnel axis. The proposed hybrid deep learning model integrates bidirectional long short-term memory (BiLSTM) and Kolmogorov–Arnold network (KAN) cells to accurately predict S-TBM attitude. This model is further combined with the multi-objective grey wolf optimizer (MOGWO) method for determining the optimal proportional-integral-derivative (PID) parameters, thereby minimizing attitude deviations. The experimental results indicate that: (1) The S-TBM reduces the construction time per single lining ring to 39 min, which is 34 min faster than the conventional TBM. (2) The improved BiLSTM model can effectively estimate shield attitude, with an RMSE of 1.030, an MAE of 0.695, and an R2 score of 0.998. (3) The proposed multi-objective optimization (MOO) framework is able to reliably control S-TBM attitude, achieving an overall improvement percentage of 25.66%. (4) Compared to other methods, the proposed approach enhances R2 score by 0.03 and the overall improvement percentage by 5.72%. The novelty of this research lies in the development of an intelligent control framework for shield attitude under the synchronous excavation and segment assembly construction mode, which achieves reliable prediction and precise regulation of S-TBM attitude by synergistically coupling a nonlinear attitude response model based on an improved BiLSTM with an MOGWO-driven PID parameter optimization strategy.