Freezing in saturated saline soil is a dynamic, coupled hydraulic-thermal-salt-mechanical (HSTM) process. The aim of this paper is to establish a model to describe the coupling process. In the model, mass conservation law, Darcy’s law, and the energy conservation law are applied. The Clapeyron equation is used to illustrate the phase equilibrium in the multiphase system of soil, ice, and water, which contributes to the simplification of the soil-water potential. A freezing experiment using a 20-cm soil column with top-side freezing was performed, and the model was solved using transient finite elements. The numerical simulation fit well with the experiment data. The results show the temperature distribution, the water content distribution, the frost heave amount, and the salt distribution at different times along the one-dimensional column. In addition, it was found that the temperature in the frozen zone changes faster than that in the unfrozen zone, and the temperature distribution tends to be stable after freezing for 96 h, resulting in upward water migration toward the freezing front from the unfrozen zone and the formation of ice lenses. Because of the water migration, an expansion process is produced, and salt is transported upward constantly. Excess salt is precipitated if the solute concentration exceeds the solubility. These results provide a reference for soil salinization and frost heave behavior in saline soils in cold regions.

Saline soil in seasonally frozen soil areas has caused tremendous damage to engineering and the ecological environment. The unfrozen water is the main factor affecting the properties of saline soil in seasonally frozen soil area and therefore needs to be studied. However, due to the high cost of laboratory measurement of the unfrozen water content, this study focuses on using an adaptive network fuzzy inference system (ANFIS) and a back propagation neural network (BPNN) to predict the unfrozen water content of saline soil in the Zhenlai area, Western Jilin. The data for the constructed model is obtained by nuclear magnetic resonance (NMR) testing. The initial water content (W₀), salt content (S), and temperature (T) are used as input parameters for predicting the unfrozen water content (W_u). The results of the ANFIS and BPNN models are compared. The results show that although both methods are suitable for predicting the unfrozen water content of saline soil in the Zhenlai area, western Jilin, the prediction accuracy of the ANFIS model is higher.

Soil in western Jilin, China has the properties of a saline soil, seasonal frozen soil, and a dispersive soil, in which the freezing process can induce many engineering and environmental problems, including soil secondary salinization and frost heave. During the freezing process, water, heat, stress, and even solute interact with each other. Water migration plays an important role in this process. In this paper, a sampling point in Nong’an, western Jilin was selected for soil collection and experimental tests. The experiment included two tests: the capillary water test and a water migration test subjected to freezing. The results showed that the height of capillary water is low, while the water migrates obviously after the process of freezing in different conditions. The results indicate that the bound water film hinders the capillary pores, which can impair the matric potential. During the freezing process, the temperature potential and gravitational potential can drive the water to migrate through the bound water film. For the soil in western Jilin, temperature potential plays a decisive role in water migration, but gravitational potential and matric potential have a minor effects.

The development of agriculture and ecology, and the construction of water conservancy facilities are seriously hindered by the salinization of seasonal frozen soil. Unfrozen water exists in the freezing and thawing of frozen soil. This unfrozen water is the core and foundation for studying the process of seasonal frozen soil salinization. However, it is difficult to obtain the unfrozen water content (UW) in routine experiments, and it shows nonlinear characteristics under the action of the main factors contained: salt content, water content, and temperature. In this paper, a new model is proposed to predict the UW of saline soil based on the combined weighting method and the adaptive neuro-fuzzy inference system (ANFIS). Firstly, the distance function was used to combine the analytic hierarchy process (AHP) with the entropy weight method (the combined weighting method) to determine the importance of the influencing factors (temperature, initial water content, and salt content) on UW. On this basis, the AHP, entropy weight method, and adaptive neuro-fuzzy inference system (AHP-entropy-ANFIS) ensemble model was established. Secondly, the five-fold cross-validation method and statistical factors (coefficient of determination, mean squared error, mean absolute percent error, and mean absolute error) were applied to evaluate and compare the AHP-entropy-ANFIS ensemble model, the ANFIS model, the support vector machine (SVM) model, and the AHP, entropy weight method, and support vector machine (AHP-entropy-SVM) ensemble model. In addition, the prediction values of the four models and the experimental values were also compared. The results show that the AHP-entropy-ANFIS model had the strongest prediction capability and the best stability, and so is more suitable for predicting the UW of saline soil. This study provides useful guidance for preventing and mitigating salinization hazards in seasonally frozen areas.