Engineering computation under uncertainty - Capabilities of non-traditional models
Computers and Structures
Neural networks as material models within a multiscale approach
Computers and Structures
Neural network constitutive model for rate-dependent materials
Computers and Structures
Finite Elements in Analysis and Design
Recurrent neural networks for fuzzy data
Integrated Computer-Aided Engineering - Data Mining in Engineering
Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data
Computers and Structures
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A new soft computing approach is presented for structural analysis. Instead of material models, an artificial neural network concept is applied to describe time-dependent material behaviour within the finite element method. In order to consider imprecise data for the identification of dependencies between strain and stress processes from uncertain results of experimental investigations, recurrent neural networks for fuzzy data are used. An algorithm for the signal computation of recurrent neural networks is developed utilizing an @a-level optimization. The approach is verified by a model based solution. Application capabilities are demonstrated by means of numerical examples.