Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components

  1. Pernía-Espinoza, A. 1
  2. Fernandez-Ceniceros, J. 1
  3. Antonanzas, J. 1
  4. Urraca, R. 1
  5. Martinez-de-Pison, F.J. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Applied Soft Computing Journal

ISSN: 1568-4946

Año de publicación: 2018

Volumen: 70

Páginas: 737-750

Tipo: Artículo

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DOI: 10.1016/J.ASOC.2018.06.005 SCOPUS: 2-s2.0-85049096999 GOOGLE SCHOLAR

Otras publicaciones en: Applied Soft Computing Journal

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Resumen

This study presents a new soft computing method to create an accurate and reliable model capable of determining three key points of the comprehensive force–displacement curve of bolted components in steel structures. To this end, a database with the results of a set of finite element (FE) simulations, which represent real responses of bolted components, is utilized to create a stacking ensemble model that combines the predictions of different parsimonious base models. The innovative proposal of this study is using GA-PARSIMONY, a previously published GA-method which searches parsimonious models by optimizing feature selection and hyperparameter optimization processes. Therefore, parsimonious solutions created with a variety of machine learning methods are combined by means of a nested cross-validation scheme in a unique meta-learner in order to increase diversity and minimize the generalization error rate. The results reveal that efficiently combining parsimonious models provides more accurate and reliable predictions as compared to other methods. Thus, the informational model is able to replace costly FE simulations without significantly comprising accuracy and could be implemented in structural analysis software. © 2018 Elsevier B.V.