A Machine Learning-based Constitutive Model for Nonlinear Analysis via Finite Element Method
Palavras-chave:
Machine Learning, Neural Networks, Multilayer Perceptron, Constitutive Models, Nonlinear Analysis, Finite Element Method, Neural Network-Based Constitutive ModelsResumo
This paper addresses a machine learning technique in the context of constitutive modelling. Since it
has been proven that multilayer perceptrons with the backprogapation algorithm are capable of approximating any
class of functions, studies have been developed with the objective of using it as approximation functions for the
nonlinear behaviour of complex material media. This is only possible because neural networks have a powerful
adaptability, capability of learning and generalizability. In this context, a multilayer perceptron is trained with
stress-strain results from a nonlinear analysis via finite element method with Mazars material in order to develop a
neural network-based constitutive model. This implementation is carried out with the help of a recognized machine
learning package in order to obtain more accurate results. To validate the proposed constitutive model, the results
obtained through the multilayer perceptron are compared with the ones of the finite element numerical analysis.
