Soft Computing Metamodels for the Failure Prediction of T-stub Bolted Connections

  • Julio Fernández-Ceniceros
  • Javier Antoñanzas Torres
  • Rubén Urraca-Valle
  • Enrique Sodupe-Ortega
  • Andrés Sanz-García
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)

Abstract

In structural and mechanical fields, there is a growing trend to replace expensive numerical simulations with more cost-effective approximations. In this context, the use of metamodels represents an attractive option. Without significant loss of accuracy, metamodelling techniques can drastically reduce the computational burden required by simulations. This paper proposes a method for developing soft computing metamodels to predict the failure of steel bolted connections. The setting parameters of the metamodels are tuned by an optimisation based on genetic algorithms during the training process. The method also includes the selection of the most relevant input features to reduce the models’ complexity. In total, two well-known metamodelling techniques are evaluated to compare their performances on accuracy and parsimony. This case studies the T-stub bolted connection, which allows us to validate the proposed models. The results show soft computing’s metamodelling capacity to accurately predict the T-stub response, while reducing the number of variables and with negligible computation cost.

Keywords

Metamodelling Multilayer Perceptron Support Vector Regression Genetic Algorithms Finite Element Method T-stub connection 

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References

  1. 1.
    ABAQUS v.6.11. Analysis User’s ManualGoogle Scholar
  2. 2.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  3. 3.
    Bonora, N.: On the effect of triaxial state of stress on ductility using nonlinear cdm model. International Journal of Fracture 88(4), 359–371 (1997)CrossRefGoogle Scholar
  4. 4.
    Calvo-Rolle, J.L., Corchado, E.: A bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)CrossRefGoogle Scholar
  5. 5.
    Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C., Snasel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)CrossRefGoogle Scholar
  6. 6.
    European Committee for Standardization: EN 1993-1-8 Eurocode 3. Design of steel structures part 1-8. Design of jointsGoogle Scholar
  7. 7.
    Faella, C., Piluso, V., Rizzano, G.: Structural Steel Semirigid Connections: Theory, Design, and Software. New Directions in Civil Engineering. Taylor & Francis (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.
    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)MATHMathSciNetGoogle Scholar
  10. 10.
    Meckesheimer, M., Booker, A.J., Barton, R.R., Simpson, T.W.: Computationally inexpensive metamodel assessment strategies. AIAA Journal 40, 2053–2060 (2002)CrossRefGoogle Scholar
  11. 11.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013)Google Scholar
  12. 12.
    Sacks, J., Welch, W., Mitchell, T., Wynn, H.: Design and analysis od computer experiments. Statistical Science 4, 409–423 (1989)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Sanz-García, A., Fernández-Ceniceros, J., Fernández-Martínez, R., Martínez-de Pisón, F.J.: Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on an annealing furnace. Ironmaking & Steelmaking, 1–12 (November 2012)Google Scholar
  14. 14.
    Sanz-García, A., Fernández-Ceniceros, J., Antoñanzas-Torres, F., Martínez-de-Pisón-Ascacibar, F.J.: Parsimonious support vector machines modelling for set points in industrial processes based on genetic algorithm optimization. In: Herrero, A., Baruque, B., Klett, F., Abraham, A., Snasel, V., de Carvalho, A.C.P.L.F., Bringas, P.G., Zelinka, I., Quintian, H., Corchado, E. (eds.) International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. AISC, vol. 239, pp. 1–10. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York (1995)Google Scholar
  16. 16.
    Villa-Vialaneix, N., Follador, M., Ratto, M., Leip, A.: A comparison of eight metamodeling techniques for the simulation of n2o fluxes and n leaching from corn crops. Environmental Modelling & Software 34, 51–66 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Julio Fernández-Ceniceros
    • 1
  • Javier Antoñanzas Torres
    • 1
  • Rubén Urraca-Valle
    • 1
  • Enrique Sodupe-Ortega
    • 1
  • Andrés Sanz-García
    • 2
  1. 1.EDMANS Research GroupUniversity of La RiojaLogroñoSpain
  2. 2.Division of BiosciencesUniversity of HelsinkiHelsinkiFinland

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