Prediction of the undrained shear strength of remolded soil with non-linear regression, fuzzy logic, and artificial neural network

This study aims to predict the undrained shear strength of remolded soil samples using nonlinear regression analyses, fuzzy logic, and artificial neural network modeling. A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected, utilizing six different measurement devices. Although water content, plastic limit, and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling, liquidity index or water content ratio was considered as an input parameter for nonlinear regression analyses. In non-linear regression analyses, 12 different regression equations were derived for the prediction of undrained shear strength of remolded soil. Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling, while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling. The experimental results of 914 tests were used for training of the artificial neural network models, 196 for validation and 196 for testing. It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses. Furthermore, a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.