PREDICTION OF SOIL-WATER CHARACTERISTIC CURVE USING GENE EXPRESSION PROGRAMMING

Soil–Water Characteristic Curve (SWCC) is one of the most important parts of any model that describes unsaturated soil behavior as it explains the variation of soil suction with changes in water content. In this research, Gene Expression Programming (GEP) is employed as an artificial intelligence method for modelling of this curve. The principal advantage of the GEP approach is its ability to generate powerful predictive equations without any prior assumption on the possible form of the functional relationship. GEP can operate on large quantities of data in order to capture nonlinear and complex relationships between variables of the system. The selected inputs for modelling are the initial void ratio, initial gravimetric water content, logarithm of suction normalized with respect to atmospheric air pressure, clay content, and silt content. The model output is the gravimetric water content corresponding to the assigned input suction. Sensitivity and parametric analyses are conducted to verify the results. It is also shown that clay content is the most influential parameter in the soil–water characteristic curve. The results illustrate that the advantages of the proposed approach are highlighted.