Key points are not available for this paper at this time.
As an essential property of frozen soils, change of unfrozen water content (UWC) with temperature, namely soil-freezing characteristic curve (SFCC), plays significant roles in numerous physical, hydraulic and mechanical processes in cold regions, including the heat and water transfer within soils and at the land–atmosphere interface, frost heave and thaw settlement, as well as the simulation of coupled thermo-hydro-mechanical interactions. Although various models have been proposed to estimate SFCC, their applicability remains limited due to their derivation from specific soil types, soil treatments, and test devices. Accordingly, this study proposes a novel data-driven model to predict the SFCC using an extreme Gradient Boosting (XGBoost) model. A systematic database for SFCC of frozen soils compiled from extensive experimental investigations via various testing methods was utilized to train the XGBoost model. The predicted soil freezing characteristic curves (SFCC, UWC as a function of temperature) from the well-trained XGBoost model were compared with original experimental data and three conventional models. The results demonstrate the superior performance of the proposed XGBoost model over the traditional models in predicting SFCC. This study provides valuable insights for future investigations regarding the SFCC of frozen soils.
Li et al. (Wed,) studied this question.