Prediction of unsaturated soils e ective stress parameter using gene expression programming

Unsaturated soil shear strength can be determined using eective stress relation that depends on the eective stress parameter. Several models have been developed in the past few years to estimate this parameter. In this research, the Gene Expression Programming (GEP) is used as an Articial Intelligence (AI) method for developing a model to predict the eective stress parameter, using ecient 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 consists of net conning pressure, suction, Soil Water Characteristic Curve (SWCC) tting parameter, bubbling pressure, residual and saturated volumetric water content. The output terminal set has one member, which is the eective 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 in uential parameter in the eective stress parameter of unsaturated soils.