Developing constitutive models from EPR-based self-learning finite element analysis

Abstract

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self-learning simulation (SelfSim). Self-learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self-learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim-based finite element analysis. These include a total stress-strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress-strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR-based self-learning finite element method. The EPR-based self-learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.