A dynamic soil freezing characteristic curve model for frozen soil

The soil freezing characteristic curve(SFCC)plays a fundamental role in comprehending thermohydraulic behavior and numerical simulation of frozen soil.This study proposes a dynamic model to uniformly express SFCCs amidst varying total water contents throughout the freezing-thawing process.Firstly,a general model is proposed,wherein the unfrozen water content at arbitrary temperature is determined as the lesser of the current total water content and the reference value derived from saturated SFCC.The dynamic performance of this model is verified through test data.Subsequently,in accordance with electric double layer(EDL)theory,the theoretical residual and minimum temperatures in SFCC are calculated to be-14.5℃to-20℃for clay particles and-260℃,respectively.To ensure that the SFCC curve ends at minimum temperature,a correction function is introduced into the general model.Furthermore,a simplified dynamic model is proposed and investigated,necessitating only three parameters inherited from the general model.Additionally,both general and simplified models are evaluated based on a test database and proven to fit the test data exactly across the entire temperature range.Typical recommended parameter values for various types of soils are summarized.Overall,this study provides not only a theoretical basis for most empirical equations but also proposes a new and more general equation to describe the SFCC.

Towards an improved prediction of soil-freezing characteristic curve based on extreme gradient boosting model

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.