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• W3133676333 endingPage "103265" @default.
• W3133676333 startingPage "103265" @default.
• W3133676333 abstract "Deformation of high-speed railway subgrades, due to low temperatures, is a common phenomenon in cold regions. In winter, the uneven frost heave of subgrade soil would cause hazards to train safety. It is therefore necessary to estimate and predict the subgrade properties. Since the variation of frost heave is non-stationary over time, traditional time series analyses have difficulties where complex physical parameters are not available. In this study, we introduce two models based on deep learning technology to predict frost heave deformation of railway subgrade. These include the artificial neural network (ANN) and long-short term memory (LSTM) network, where we used data of four sections to build the ANN and LSTM. The experimental results of the LSTM model provided lower MAE and RMSE with different datasets. The prediction of three deep deformations for the K1959 + 580 and K1962 + 618 section with slight fluctuation in the data and the performance of the ANN with MAE is 0.0090‐–0.0660 and 0.0069‐–0.0201 of the LSTM models. In the K2005 + 948 and K2029 + 829 section, ANN and LSTM estimated the frost heave deformation with MAE of 0.0061‐–0.0681 and 0.0054‐–0.0309 for a more intense fluctuation on the deformation. Our findings suggest that the network topology of the LSTM model with 12 hidden neurons performs best with fewer parameters, with an average RMSE of 0.0210 mm and MAE of 0.0138 for all the training samples, indicating that the deep learning model has high precision in this scenario." @default.
• W3133676333 created "2021-03-15" @default.
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• W3133676333 date "2021-05-01" @default.
• W3133676333 modified "2023-10-17" @default.
• W3133676333 title "A deep learning forecasting method for frost heave deformation of high-speed railway subgrade" @default.
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• W3133676333 doi "https://doi.org/10.1016/j.coldregions.2021.103265" @default.
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