Prediction of unsaturated soils effective stress parameter using gene expression programming

Unsaturated soil shear strength can be determined using effective stress relation that depends on the effective stress parameter. Several models have been developed in past few years to estimate this parameter. In this research, the Gene Expression Programming (GEP) is used as an Artificial Intelligence (AI) method for developing a model to predict the effective stress parameter using efficient parameters. The principal advantage of the GEP approach is its ability to generate powerful prediction equations without any prior assumption on the possible form of the functional relationship.The input terminal set consistsof net confining pressure, suction, Soil Water Characteristic Curve (SWCC) fitting parameter, bubbling pressure, residual and saturated volumetric water content. The output terminal set has one member, which is the effective stress parameter. An experimental database obtained from the literature is employed to develop the model. Comparison of the model prediction with the actual data as well as other investigators indicates a very good performance and ability of model. Sensitivity and parametric analyses are conducted to verify the results. It is also shown that soil suction is the most influential parameter in the effective stress parameter of unsaturated soils.