Formulation of soil angle of shearing resistance using a hybrid GP and OLS method

Abstract

In the present study, a prediction model was derived for the effective angle of shearing resistance (ϕ′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates ϕ′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation coefficient, root mean squared error, and mean absolute percent error were used to evaluate the performance of the models. Sensitivity and parametric analyses were conducted and discussed. The GP/OLS-based formula precisely estimates the ϕ′ values for a number of soil samples. The proposed model provides a better prediction performance than the traditional GP and regression models.