A CNN-PatchTST Hybrid Deep Learning Model for Multi-Target Multi-Step Attitude Prediction of Shield Machines in Small-Radius Curves

Small-radius curved tunneling makes shield machine attitude control especially difficult. Intensified soil-machine interaction under these conditions creates a high risk of snakelike motion, which can compromise both construction safety and segment assembly quality. Accurate advance prediction of attitude parameters is therefore critical for timely course correction. We propose CNN-PatchTST, a hybrid deep learning model that integrates four complementary components. A convolutional neural network (CNN) extracts local temporal features. A PatchTST-based Transformer encoder applies global self-attention over long sequences. A direct mapping branch produces short-range inertial estimates, and a gated fusion layer performs adaptive signal integration. Operating as a unified architecture, the model simultaneously predicts all 12 key attitude parameters five steps ahead, providing operators with roughly five minutes of advance warning. Validated on construction data from the Fangbai Intercity Railway (Guangzhou Metro, minimum curve radius 350 m), CNN-PatchTST achieves a mean coefficient of determination (R2) of 0.992 across all 12 attitude parameters under three independent random seeds. Mean absolute errors (MAE) for the front and rear shield azimuths reach 0.578° and 0.430°, respectively. A pure-inertia baseline (using only historical attitude values) attains R2 = 0.988, yet its azimuth MAE is 4.3 times higher than that of the full model. This result confirms that modeling control parameters is essential for accurate angular prediction. SHAP-based sensitivity analysis yields three further insights. First, historical attitude parameters account for approximately 97% of total feature importance. Second, the four most recent steps contribute over 50% of predictive power. Third, a lagged predictive association of roughly 3–4 min is observed between thrust jack pressure differentials and shield tail deviation response, suggesting a potential lagged association that warrants further causal validation. Collectively, these findings demonstrate that CNN-PatchTST delivers accurate and interpretable multi-step attitude predictions, establishing it as a practical tool for on-site guidance during small-radius shield tunneling.