Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels

To tackle the challenge of discrete and complex monitoring data generated during high-speed rail tunnel construction, this study proposes a hybrid deep learning model for deformation forecasting. Using 300-hour continuous deformation records from multiple cross-sections of the G Tunnel (March 2023), a novel WOA-CNN-GRU model is developed, integrating data preprocessing, feature extraction, and prediction. The methodology incorporates quadratic exponential smoothing for outlier mitigation, followed by sequential feature extraction using convolutional neural networks (CNNs) and bidirectional gated recurrent units (GRUs). Comparative experiments demonstrate the model's superiority over conventional architectures including RNN, LSTM, GRU, and CNN-GRU. The WOA-CNN-GRU model achieves an RMSE of 0.1257 mm and a MAPE of 0.51%, significantly outperforming baseline models, with RMSE reductions of 0.9814 mm (RNN), 0.7629 mm (LSTM), 0.4188 mm (GRU), and 0.2292 mm (CNN-GRU), and MAPE improvements of 2.64%, 1.37%, 1.05%, and 0.86%, respectively. Moreover, the model exhibits robust generalization, with average absolute errors below 1 mm and relative errors under 1% across various construction methods and tunnel segments. These results provide compelling evidence for the effectiveness of hybrid intelligent models in capturing nonlinear, spatiotemporal deformation patterns in tunnel engineering. The WOA-CNN-GRU model offers practical guidance for real-time monitoring system development and risk mitigation in civil infrastructure projects.