Neural network constitutive model for rate-dependent materials

Neural network (NN) constitutive model adjusts itself to describe given stress and strain relationship. It is capable of capturing complex material behavior, using stress and strain sets from experiments. This paper presents a rate-dependent NN constitutive model formulation and its implementation in finite element analysis. The proposed NN model is verified for a standard solid viscoelasticity model. The model is then applied to analysis of time-dependent behavior of concrete. The proposed model has potential of capturing any rate-dependent material models, provided enough data sets are given. The issue of what constitutes a sufficient data set to train a neural network constitutive model must be addressed in future research.