摘要
In cold regions,the frost-heave of soil can cause uneven railway subgrades,affecting the safety and efficiency of high-speed railways.This study proposes a novel PCW-iTransformer model for predicting frost heave,which integrates PCHIP data interpolation,CEEMDAN signal decomposition,and WPT denoising to extract sequential features.Compared to existing models like Autoformer,Crossformer,and DLinear,PCW-iTransformer achieves a reduction of 19.1%-34.5%in error metrics and an improvement of 2.8%-4.6%in the coefficient of determination.Additionally,a fused parameter model based on normalized moisture and temperature improves prediction accuracy,reducing MSE,MAE,and RMSE by up to 7.6%.The model also demonstrates robustness under data scarcity,maintaining stable performance with 40%continuous or 60%random missing data.Overall,PCW-iTransformer provides a reliable approach for predicting frost heave,offering valuable insights for the maintaining and long-term stability of high-speed railway subgrades in cold regions.
基金
supported by National Key R&D Program of China(Grant No.2022YFB2603301)
the National Natural Science Foundation of China(Grant No.52178376)
Science and Technology Research and Development Program of China Railway Group Limited(Grant No.2023-Major Project-04).
