Structural Analysis with Fuzzy Data and Neural Network Based Material Description

In the article, a new approach is presented utilizing artificial neural networks for uncertain time-dependent structural behavior. Recurrent neural networks (RNNs) for fuzzy data can be trained by uncertain experimental data to describe arbitrary stress–strain–time dependencies. The benefit is a generalized formulation, which can be applied to describe the behavior of several materials without definition of a specific material model. Model-free material descriptions can be used as numerical efficient material formulations within the finite element method. To perform fuzzy or fuzzy stochastic finite element analyses, a new approach is introduced. An -level optimization is utilized for signal computation and training of RNNs for fuzzy data. The applicability is demonstrated by means of examples.