Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods

Abstract

Shear strength parameters such as friction angle and cohesion are of the most important soil's parameters which are used for design and practice in engineering works. The main aim of this paper is investigation of artificial neural networks (ANNs) and multivariate regression (MR) potential for estimation of soil shear strength parameters. For this reason, two types of ANNs including multilayer perceptron (MLP) and radial basis function (RBF), and MR including multivariate non-linear regression (MNR) as well as multivariate linear regression (MLR), have been used. Five different ANN and MR models comprising various combinations of soil's physical parameters, i.e.: percentages of passing the No. 200 (≠200), 40 (≠40) and 4 (≠4) sieves, plasticity index (PI), and density (ρ) have been developed to evaluate the effect degrees of these variables on shear strength parameters. In addition to correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and t-test have been also used for evaluation of prediction accuracy on both ANNs and ML methods. The results of this study indicated that MLP–ANN shows better performance rather than RBF–ANN. These results also indicated that the Levenberg–Marquardt learning rule and Sigmoid activation function were found to be appropriate for this problem. Furthermore, MLR shows better performance in prediction of shear strength parameters rather than MNR models.