Neural Network-Based Material Modeling

Abstract

A neural network - based material modeling methodology for engineering materials is developed in this study. With this approach, the complex stress - strain behavior of an engineering material can be captured within the weight structure of a multilayer feedforward neural network trained directly on the stress- strain data obtained from experiments. The feasibility of this approach is verified through constructing neural network-based constitutive models of plain concrete in biaxial stress states and in uniaxial cyclic compression. A composite material model simulating the stress-strain behavior of reinforced concrete as a generic composite material in a biaxial stress state is built with experimental data from Vecchio and Collins' tests on reinforced concrete panels in both pure shear and combined shear with normal stresses. An adaptive neural network simulator is developed by implementing a dynamic node creation scheme and a higher order learning algorithm. Representation schemes, network architectures. training and testing methods, stress- and strain -based approaches for material modeling are investigated. An elastic unloading mechanism is studied with a concrete material model in biaxial compression. Main issues concerning the implementation of neural network material models in finite element solution procedures arc discussed. The results on the stress-strain relations of a material predicted by a neural network-based model are compared with experimental data. All neural network material models developed in this study match well with experimental results and the network testing results are reasonable. The developed approach shows promise in the constitutive modeling of composite materials