A Neural Network Model for Estimation of Failure Stresses and Strains in Cohesive Soils

Abstract

In this article, a set of neural networks for the prediction of the stresses and the corresponding strains at failure of cohesive soils when subjected to a load of a shallow foundation are presented. The data are acquired via Monte Carlo analyses for different types of loadings and stochastic input material variabilities, and by adopting the clayey soil domain and modified Cam Clay material yield function. The mathematical functions for the estimation of the failure stresses and strains are computed with the feed forward neural network method (FNN). It is demonstrated that the accuracy of the derived relations is in the order of a maximum relative error of 10−5 in all monitored output variables. In addition, the number of training epochs required for convergence is relatively low and this means that the computational and data costs for the construction of the FNN are low. The critical input variable for the estimation of the most unfavorable situations is the Karhunen Loeve series expansion for porous analyses, while for non-porous analyses the constant distribution over depth is the one that provides more critical estimations for the monitored output variables of stresses and strains at failure. This set of functions can estimate the aforementioned variables of the footing settlement in clays with high accuracy; consequently, it can be an important tool for geotechnical engineering design, especially in providing the largest stress allowed from the foundation.