Intelligent computing in inverse problems

  • Tadeusz Burczyński Cracow University of Technology
  • Witold Beluch Silesian University of Technology
  • Adam Długosz Silesian University of Technology
  • Piotr Orantek Silesian University of Technology
  • Antoni Skrobol Silesian University of Technology

Abstract

This paper presents a review of intelligent computing techniques in solving inverse mechanics problems. These techniques are based on Evolutionary Algorithms (EAs) and the coupling of Evolutionary Algorithms (EAs) and Artificial Neural Networks (ANNs) in the form of Computational Intelligence Systems (CISs). The main attention was focused on the identification of the defects such as voids or cracks in structures on the basis of the knowledge about displacements, temperature and eigenfrequencies. The identification of the unknown number, position, size and kind of defects in the elastic structures is shown. The paper contains a lot of tests and numerical examples.

Keywords

intelligent computing (IC), evolutionary algorithms (EAs), artificial neural networks (ANNs), fuzzy inference system (FIS), computational intelligent system (CIS), finite element method (FEM), boundary element method (BEM),

References

[1] J .T. Aleandcr. An Indexed Bibliography of Distributed Genetic Algorithms. University of Vaasa, Report 94-1- PARA, Vaasa, Finland, 2000.
[2] J. Arabas. Lectures in Evolutionary Algorithms (in Polish). WNT, Warszawa, 2001.
[3] M. Bonnet, T. Burczyński, M. Nowakowski. Sensitivity analysis for shape perturbation of cavity or internal crack using BIE and adjoint variable approach. International Journal of Solids and Structures, 39: 2365-2385, 2002.
[4] T . Burczyński. The Boundary Elemenl Method In Mechanics WNT, Warszawa, 1995.
[5] T . Burczyński (ed). Computational Sensitivity Analysis and Evolutionary Optimization of Systems with Geometrical Singularities. ZN KWMiMKM, Gliwice, 2002.
Published
Nov 21, 2022
How to Cite
BURCZYŃSKI, Tadeusz et al. Intelligent computing in inverse problems. Computer Assisted Methods in Engineering and Science, [S.l.], v. 13, n. 1, p. 161-206, nov. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/968>. Date accessed: 21 nov. 2024.
Section
Articles

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