Prediction of yield shear strength of saturated sandy soils using artificial neural networks

Abstract

ABSTRACT The undrained shear strength of sandy soils during flow failures and liquefaction is a critical metric in the analysis of undrained stability. In our study, the numerical technique known as Artificial Neural Network (ANN) is used for simulating the triaxial stress–strain relationship of sandy soils. This paper aims to predict the undrained shear strength of saturated sandy soil. The proposed program requires simple laboratory soil data to proceed. They are median grain diameter (D50), fines content percentage (FC%), void ratios and relative density. In addition, the stress data of undrained loading such as effective vertical stress (σ’1) and effective horizontal stress (σ’3) are needed. Then, using the program, the deviator stress at yield (qu yield) and, consequently, the yield shear strength of sandy soil (Su (yield)) can be determined. A database of experimental undrained triaxial of saturated sandy soils was collected from the literature and prepared to be the inputs of the network. Two artificial neural networks have been built. By comparing the effectiveness of the two networks, the Back Propagation Neural Network (PBNN) approved higher results and more accuracy than the General Regression Neural Network (GRNN). The computer program, yield shear strength prediction application, written in visual basic, has been developed by the author. For model validation, seven case studies (seven patterns of the production set), which were not seen by the network previously, were presented to the application and the results are compared to the actual output. The produced yield shear strength is very close to the actual strength.