Modeling Structural Elements Subjected to Buckling Using Data Mining and the Finite Element Method

  • Roberto Fernández-Martínez
  • Rubén Lostado-Lorza
  • Marcos Illera-Cueva
  • Bryan J. Mac Donald
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

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.

Keywords

Finite Element Method ANN Buckling Geometric Imperfections 

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References

  1. 1.
    Akesson, B.: Plate Buckling in Bridges and Other Structures. Taylor & Francis (2007)Google Scholar
  2. 2.
    Johnston, B.: Column Buckling Theory: Historic Highlights. Journal of Structural Engineering 109(9), 2086–2096 (1983)CrossRefGoogle Scholar
  3. 3.
    EN 1993 - Eurocode 3: Design of steel structures (1993)Google Scholar
  4. 4.
    Corchado, E., Herrero, Á.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing 11(2), 2042–2056 (2011)CrossRefGoogle Scholar
  5. 5.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)Google Scholar
  6. 6.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)MATHGoogle Scholar
  7. 7.
    Timoshenko, S.P., Gere, J.M.: Theory of Elastic Stability, 2nd edn. McGraw-Hill (1961)Google Scholar
  8. 8.
    Crisfield, M.A.: Large-deflection elasto-plastic buckling analysis of eccentrically stiffened plates using finite elements (1976)Google Scholar
  9. 9.
    El-Sawy, K.M., Elshafei, A.L.: Neural network for the estimation of the inelastic buckling pressure of loosely fitted liners used for rigid pipe rehabilitation. Thin-walled Structures 41(8), 785–800 (2003)CrossRefGoogle Scholar
  10. 10.
    Waszczyszyn, Z., Bartczak, M.: Neural prediction of buckling loads of cylindrical shells with geometrical imperfections. International Journal of Non-linear Mechanics 37(4), 763–775 (2002)MATHCrossRefGoogle Scholar
  11. 11.
    Sadovský, Z., Guedes Soares, C.: Artificial neural network model of the strength of thin rectangular plates with weld induced initial imperfections. Reliability Engineering & System Safety 96(6), 713–717 (2011)CrossRefGoogle Scholar
  12. 12.
    Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer (2002)Google Scholar
  13. 13.
    Team RC: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2012) ISBN 3-900051-07-0, http://www.R-project.org/
  14. 14.
    Fernández, R., Lostado, R., Fernandez, J., Martinez-de-Pison, F.J.: Comparative analysis of learning and meta-learning algorithms for creating models for predicting the probable alcohol level during the ripening of grape berries. Computers and Electronics in Agriculture 80, 54–62 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Fernández-Martínez
    • 1
  • Rubén Lostado-Lorza
    • 2
  • Marcos Illera-Cueva
    • 2
  • Bryan J. Mac Donald
    • 3
  1. 1.Department of Material ScienceUniversity of Basque Country UPV/EHUBilbaoSpain
  2. 2.Department of Mechanical EngineeringUniversity of La RiojaLogroñoSpain
  3. 3.School of Mechanical & Manufacturing EngineeringDublin City UniversityDublin 9Ireland

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