SciELO - Scientific Electronic Library Online

 issue47Measured pressures on the basis of bottom slab with gaps in the flow direction in a channelTotal suspended particles interception by five urban tree species in Valle de Aburrá author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google


Revista Facultad de Ingeniería Universidad de Antioquia

Print version ISSN 0120-6230


DONIS DIAZ, Carlos Alberto; VALENCIA MORALES, Eduardo  and  MORELL PEREZ, Carlos. Support vector machine model for regression applied to the estimation of the creep ruptura stress in ferritic steels. Antioquia [online]. 2009, n.47, pp.53-58. ISSN 0120-6230.

Having as antecedent the use of artificial neural networks (ANN) in the estimation of the creep rupture stress in ferritic steels, new experiments have been developed using Support Vector Machine for Regression (SVMR), a recently method developed into the machine learning field. A comparative analysis between both methods established that SVMR have a better behavior in the problematic of creep. The results are explained theoretically and finally, the use of a model of SVMR that uses a polynomial kernel of third grade and a control capacity constant of 100, is proposed.

Keywords : Creep; ferritic steels; support vector machine; artificial neural network.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License