Back analysis of microplane model parameters using soft computing methods

  • Anna Kucerova Czech Technical University in Prague
  • Matej Leps Czech Technical University in Prague
  • Jan Zeman Czech Technical University in Prague

Abstract

A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.

Keywords

References

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Published
Aug 24, 2022
How to Cite
KUCEROVA, Anna; LEPS, Matej; ZEMAN, Jan. Back analysis of microplane model parameters using soft computing methods. Computer Assisted Methods in Engineering and Science, [S.l.], v. 14, n. 2, p. 219-242, aug. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/828>. Date accessed: 23 dec. 2024.
Section
Articles