Soft computing metamodels for the failure prediction of T-stub bolted connections

  1. Fernández-Ceniceros, J. 2
  2. Torres, J.A. 2
  3. Urraca-Valle, R. 2
  4. Sodupe-Ortega, E. 2
  5. Sanz-García, A. 1
  1. 1 University of Helsinki
    info

    University of Helsinki

    Helsinki, Finlandia

    ROR https://ror.org/040af2s02

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Libro:
Advances in Intelligent Systems and Computing

ISBN: 9783319079943

Año de publicación: 2014

Volumen: 299

Páginas: 41-51

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-319-07995-0_5 SCOPUS: 2-s2.0-84927740999 WoS: WOS:000343754200005 GOOGLE SCHOLAR

Resumen

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. © Springer International Publishing Switzerland 2014.