Recurrent and Constructive‐Algorithm Networks For Sand Behavior Modeling

Abstract

ABSTRACT This article presents and discusses various aspects regarding the modeling of the behavior of a coarse granular material using Recurrent Neural Networks (RNNs) and Constructive Algorithms (CAs). A series of undrained triaxial tests following compression stress paths was performed to develop the database for neural network training and testing, where the relative density (Dr) and the confining effective stress (σ3) were varied. The range of Dr and σ3 values was selected to have both dilatant and compressive sand behaviors. Modeling of sand behavior is done using Cascade and Jordan's network architectures. Several input functions, learning rules, and transfer functions are utilized to evaluate their effects on the accuracy achieved by both algorithms during the training and predicting stages as well as on the time employed to perform these tasks. It is also shown that for the case of cascade networks, when the full-size network having two outputs (pore water pressure and deviatoric stress) is divid...