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The soil water retention curve (SWRC) provides information for a wide range of geoenvironmental problems, such as analyses of transient two-phase flow, the bearing capacity and shear strength of unsaturated soils. Many past studies have shown experimentally the effects of stress on the SWRC. Unfortunately, direct stress-dependent water retention measurements are relatively time-consuming and generally require special equipment and a certain level of expertise. This study primarily aimed to develop a novel predictive framework within the context of soft computing to capture the dependency of the SWRC on several variables, with an emphasis on stress and soil type. To achieve this, the three shape parameters of van Genuchten's water retention model were estimated using a comprehensive database of 102 SWRC tests retrieved from the literature. In this study, 60% of the datasets were employed for model training, with an additional 20% being designated for validation, while the remaining 20% were set aside for testing the model's performance. The data were analyzed using two machine learning techniques: the group method of data handling and multi-layer perceptron approaches. Results showed excellent performance of the two methods. A sensitivity analysis was conducted to explore the relative significance of the different variables. Interestingly, net stress was found to be almost as significant as soil type. The introduced artificial intelligence based predictive framework provided a very effective method of integrating theory and practice. [ABSTRACT FROM AUTHOR]

As the world struggles with climate change and energy crises, understanding the role of soil in the food–water–energy nexus becomes increasingly critical. Accurately estimating the soil thermal conductivity drying curve is essential for assessing the impacts of temperature on soil biota and crop growth, environmental changes due to forest fires and global warming, and for designing geo‐energy extraction techniques such as geothermal energy piles. Existing empirical models often fail to accurately estimate the soil thermal conductivity (TC), particularly in pendular soil moisture regimes where they do not capture sharp changes in TC. This study introduces a novel approach using a pore unit cell network model to more accurately describe the dynamics of TC in variably saturated soils. A quadratic parallel scheme within each soil pore unit cell links the TCs of solid, water, and air to the overall effective conductivity. By modeling air invasion in the pore network model and employing the proposed equation, we determined the unsaturated soil TC based on varying local conductivities. The model effectively captures the significant decrease in conductivity in the pendular saturation regime, associated with the shrinkage of the spanning‐wetting cluster. Quantitative analyses showed a substantial improvement in prediction accuracy compared to existing models, especially under varying moisture conditions. Our findings have significant implications for better characterizing soil thermal and hydraulic properties, which are crucial for resource management in a changing climate and advancing geo‐energy technologies. Core Ideas: Spanning‐wetting cluster shrinkage in the pendular regime significantly decreases soil thermal conductivity.Soil thermal conductivity is influenced by both the distribution and specific values of local pore saturation.Pore unit cell network modeling leads to more accurate estimates of the unsaturated thermal conductivity. [ABSTRACT FROM AUTHOR]