Effective Material Modeling of Parameterizable Viscoelastic Shell Structures with Artificial Neural Networks

In this contribution, we seek to reduce the computational cost of structural simulations by taking advantage of a consistent homogenization scheme for shell representative volume elements to train an artificial neural network (ANN) material model for approximating the effective strain–stress relation. As ANNs can capture inelastic material behavior, this work focuses on the application to high-dimensional strain–stress relations for shell structures with underlying viscoelastic microstructures. We demonstrate that a small database comprising uniaxial synthetic material tests on a representative microstructure, in combination with derivative information, is sufficient to train a feasible ANN material model. Furthermore, we explore the limits of the approximation capabilities of the ANN by including non-strain-related parameters of the microstructure—such as volume fractions—as inputs to the material model. Studies include comparisons with full-scale and multiscale models, highlighting computational efficiency and practical feasibility in application to real-world engineering problems involving complex microstructures.