Prediction of Large-Diameter Shield Tunneling Attitude: PCA-SWO-Stacking Machine Learning Algorithm Application in a Case Study of the Shanghai Beiheng Passageway

To address the limited cross-domain generalization of single-algorithm models for shield attitude prediction, this study proposes a heterogeneous algorithm-fusion framework based on Stacking. The framework integrates multiple machine learning algorithms and uses the Spider Wasp Optimizer (SWO) for hyperparameter optimization, thereby overcoming the limitations of individual learners and reducing the need for laborious algorithm selection. Principal Component Analysis (PCA) is further used to reduce dimensionality and reconstruct high-dimensional features, which lowers computational complexity and improves prediction accuracy. The proposed PCA-SWO-Stacking algorithm was applied to shield attitude prediction using data from the Shanghai Beiheng Passageway project. The results show strong predictive performance, with all R2 values exceeding 0.94 and all RMSE and MAE values remaining below 2. Comparative experiments with commonly used ensemble algorithms and ablation studies further confirm the effectiveness and robustness of the proposed method.