An ensemble machine learning framework for predicting the soil-water characteristic curve of compacted soils

Fine-grained compacted soils that are usually in an unsaturated state are widely used in the construction of geo-infrastructures that include embankments, pavements, and retaining walls. The hydro-mechanical behavior of unsaturated soils is typically predicted using the saturated soil properties and the soil-water characteristic curve (SWCC). However, elaborate testing equipment is required for reliable determination of the SWCC, which is not only cumbersome but also time-consuming and expensive. To address this limitation, a hybrid computational framework is developed in this study, combining machine learning (ML) techniques and principles of thermodynamics to efficiently predict the SWCC considering the influence of soil structure due to initial compaction water content. Specifically, Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) models are used to approximate the Fredlund and Xing SWCC equation, and their fitting parameters are correlated with basic soil properties through ML-based regression. To improve the physical interpretability of the ML predictions, a new parameter, termed the free energy deviation index, is introduced and calibrated. Sensitivity analyses further highlight the influence of soil structure on the shape of the SWCC. The proposed physics-guided ML framework provides a reliable approach for predicting the SWCC, offering a tool for assessing the hydro-mechanical properties of fine-grained compacted soils.