Research on freeze-thaw displacement prediction model of sandy soil based on attention mechanism CNN-BiGRU - Zecheng Wang; Dongwei Li; Zhengbin Dong; Zhiwen Jia; Chaochao Zhang (Case Studies in Thermal Engineering)
Accurate prediction of freeze-thaw displacement in sandy soils is crucial for artificial ground freezing (AGF) projects like subway tunnels. Traditional methods struggle with nonlinear complexities in freeze-thaw dynamics. This study develops an attention-based CNN-BiGRU model that synergizes convolutional neural networks for spatial feature extraction, bidirectional gated recurrent units for temporal dependency modeling, and attention mechanisms for critical time-step weighting. Laboratory tests analyzed temperature fields, moisture migration, and displacement under varied water contents (14 %–20 %) and freezing temperatures (−5 °C to −20 °C), generating 5715 experimental samples. Results show the proposed model with a mean RMSE of 0.00129 and R2 of 0.992, outperforming conventional approaches by 17.8 % in accuracy. The framework enhances coupled modeling of temperature gradients and moisture transfer through multimodal feature fusion, offering precise frost heave/thaw settlement control for engineering applications. This advancement supports optimized AGF parameter selection (e.g., critical freezing temperatures and moisture thresholds) and risk mitigation in underground construction, demonstrating practical value for improving structural safety in geotechnical projects.