Modeling structural elements subjected to buckling using data mining and the finite element method

  1. Fernández-Martinez, R. 1
  2. Lostado-Lorza, R. 2
  3. Illera-Cueva, M. 2
  4. Donald, B.J.M. 3
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  2. 2 Universidad de La Rioja
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    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 Dublin City University
    info

    Dublin City University

    Dublín, Irlanda

    ROR https://ror.org/04a1a1e81

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Libro:
International Joint Conference SOCO’13-CISIS’13-ICEUTE’13 : Advances in Intelligent Systems and Computing

Editorial: Springer

ISBN: 9783319018539

Año de publicación: 2014

Volumen: 239

Páginas: 269-278

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-319-01854-6_28 SCOPUS: 2-s2.0-84927613114 GOOGLE SCHOLAR

Objetivos de desarrollo sostenible

Resumen

Buckling of thin walled welded structures is one of the most common failure modes experienced by these structures in-service. The study of such buckling, to date, has been concentrated on experimental tests, empirical models and the use of numerical methods such as the Finite Element Method (FEM). Some researchers have combined the FEM with Artificial Neural Networks (ANN) to study both open and closed section structures but these studies have not considered imperfections such as holes, weld seams and residual stresses. In this paper, we have used a combination of FEM and ANN to obtain predictive models for the critical buckling load and lateral displacement of the center of the profile under compressive loading. The study was focused on ordinary Rectangular Hollow Sections (RHS) and on the influence of geometric imperfections while taking residual stresses into consideration. © Springer International Publishing Switzerland 2014.