Probabilistic analysis of soil-water characteristic curve based on machine learning algorithms

Abstract

The soil-water characteristic curve (SWCC) is the constitutive relationship curve of soil suction and water content, which has important engineering significance for studying the soil strength, permeability coefficient and volume change of unsaturated soils. Considering the experimental measurement of the SWCC is time-consuming, many empirical methods have been suggested to estimate the SWCC. This paper proposed a machine learning algorithm to predict SWCC from limited sets of soil properties. By predicting the parameters of the Fredlund and Xing model (which is called FX model), the most probable SWCC can be obtained. Since SWCC can't be accurately determined, the residual probability distribution functions of parameters of FX model are also derived in this paper. Finally, this paper predicted the 90% confidence interval of the SWCC. The result shows that the machine learning algorithm has high accuracy and generalization ability. The suggested method provides a practical means to estimate the SWCC and the variability of it, so that the error associated with the prediction model can be explicitly considered.