Neural network aided stochastic computations and earthquake engineering

  • Nikos D. Lagaros National Technical University Zografou Campus
  • Manolis Papadrakakis National Technical University Zografou Campus
  • Michalis Fragiadakis National Technical University Zografou Campus
  • George Stefanou National Technical University Zografou Campus
  • Yiannis Tsompanakis Technical University of Crete

Abstract

This article presents recent developments in the field of stochastic finite element analysis of structures and earthquake engineering aided by neural computations. The incorporation of Neural Networks (NN) in this type of problems is crucial since it leads to substantial reduction of the excessive computational cost. In particular, a hybrid method is presented for the simulation of homogeneous non-Gaussian stochastic fields with prescribed target marginal distribution and spectral density function. The presented method constitutes an efficient blending of the Deodatis- Micaletti method with a NN based function approximation. Earthquake-resistant design of structures using Probabilistic Safety Analysis (PSA) is an emerging field in structural engineering. It is investigated the efficiency of soft computing methods when incorporated into the solution of computationally intensive earthquake engineering problems.

Keywords

References

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Published
Aug 24, 2022
How to Cite
LAGAROS, Nikos D. et al. Neural network aided stochastic computations and earthquake engineering. Computer Assisted Methods in Engineering and Science, [S.l.], v. 14, n. 2, p. 251-275, aug. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/830>. Date accessed: 13 nov. 2024.
Section
Articles