The Application of Metamodels Based on Soft Computing to Reproduce the Behaviour of Bolted Lap Joints in Steel Structures

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


A promising field of research in steel structures regarding their preliminary design and optimization is the replacement of expensive computational finite element models with more efficient techniques. Without a significant loss of accuracy, new proposals should be able to consider not only the ideal load-displacement response but also relevant failure mechanisms and imprecisions in material properties. The article proposes the use of metamodels based on soft computing as an overall approximation system for structures analysis. This approach has been applied in several fields but, till nowadays, its implementation on structural analysis in early esign seems quite limited to a few theoretical cases. Taking advantage of artificial neural network as global approximation technique, the parameters for more realistic and informative load-displacement curve including nonlinear effects (damage mechanics) are estimated for bolted steel lap joints. Our results demonstrate the accuracy of the metamodel implemented can be close to simulations and also real experimental tests.


Artificial Neural Network Metamodel Finite Element Analysis Steel Structure Bolted Lap Joint 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    ABAQUS v.6.11. Analysis User’s ManualGoogle Scholar
  2. 2.
    Ductility Requirements in Shear Bolted Connections. Master’s thesis, University of Coimbra (2007)Google Scholar
  3. 3.
    Fernández-Ceniceros, J., Sanz-García, A., Antoñanzas-Torres, F., Martínez-de-Pisón-Ascacibar, F.J.: Multilayer-perceptron network ensemble modeling with genetic algorithms for the capacity of bolted lap joint. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part III. LNCS, vol. 7208, pp. 545–556. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Rotation capacity of partial strength steel joints with three-dimensional finite element approach. Computers & Structures 116, 88–97 (2013)Google Scholar
  5. 5.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  6. 6.
    Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing (2010)Google Scholar
  7. 7.
    Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall (1999)Google Scholar
  8. 8.
    Hornik, K., Stinchcombe, M.B., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)CrossRefGoogle Scholar
  9. 9.
    Jones, M.T.: Artificial Intelligence: A Systems Approach. Infinity Science Press LLC (2008)Google Scholar
  10. 10.
    Lemaitre, J.A.: A course on damage mechanics. Springer (1992)Google Scholar
  11. 11.
    Lemaitre, J.A.: A continuous damage mechanics model for ductile fracture. J. Eng. Mater. Technol. 107, 83–90 (1985)CrossRefGoogle Scholar
  12. 12.
    Mckay, M., Beckman, R., Conover, W.: A comparison of three method for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979)MathSciNetzbMATHGoogle Scholar
  13. 13.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013)Google Scholar
  14. 14.
    Sacks, J., Welch, W., Mitchell, T., Wynn, H.: Design and analysis od computer experiments. Statistical Science 4, 409–423 (1989)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.EDMANS Research GroupUniversity of La RiojaLogroñoSpain

Personalised recommendations