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FE Analysis of Structures With Uncertain Model-Free Material Descriptions

[+] Author Affiliations
Steffen Freitag

Georgia Institute of Technology, Savannah, GA

Wolfgang Graf

Technische Universität Dresden, Dresden, Germany

Paper No. IMECE2011-63357, pp. 831-839; 9 pages
doi:10.1115/IMECE2011-63357
From:
  • ASME 2011 International Mechanical Engineering Congress and Exposition
  • Volume 9: Transportation Systems; Safety Engineering, Risk Analysis and Reliability Methods; Applied Stochastic Optimization, Uncertainty and Probability
  • Denver, Colorado, USA, November 11–17, 2011
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5495-2
  • Copyright © 2011 by ASME

abstract

An artificial neural network concept is presented, which can be used to identify uncertain time-dependent material behavior. Dependencies between strain and stress processes obtained from uncertain results of experimental investigations are described by recurrent neural networks for fuzzy data. Direct and indirect approaches are presented for the computation of fuzzy stress and strain processes from experimental results. The identification of uncertain stress-strain-time dependencies with recurrent neural networks for fuzzy data is realized by a network training utilizing α-cuts and an α-level-optimization. After identification, the network can be applied instead of material models within the finite element method. An incremental finite element formulation is developed under consideration of different time scales for the material level and for the structural level. This enables the finite element analysis of structures with an uncertain model-free material description. An example is presented to demonstrated the applicability of the new approach.

Copyright © 2011 by ASME

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