A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves

Soil water retention curves are a key constitutive law used to describe the physical behavior of an unsaturated soil. Various computational modeling techniques, that formulate retention curve models, are mostly based on existing soil databases, which rarely consider any effect of stress on the soil water retention. Such effects are crucial in the case of swelling soils. This study illustrates and explores the ability of computational intelligence-based genetic programming to formulate the mathematical relationship between the water content, in terms of degree of saturation, and two input variables, i.e., net stress and suction for three different soils (sand-kaolin mixture, Gaduk Silt and Firouzkouh clay). The predictions obtained from the proposed models are in good agreement with the experimental data. The parametric and sensitivity analysis conducted validates the robustness of our proposed model by unveiling important parameters and hidden non-linear relationships.