Neural networks assessment of beam-to-column joints

AUTOR(ES)

Lima, L. R. O. de, Vellasco, P. C. G. da S., Andrade, S. A. L. de, Silva, J. G. S. da, Vellasco, M. M. B. R.

FONTE

Journal of the Brazilian Society of Mechanical Sciences and Engineering

DATA DE PUBLICAÇÃO

2005-09

RESUMO

This paper proposes the use of artificial neural networks to predict the flexural resistance and initial stiffness of beam-to-column steel joints using the back propagation supervised learning algorithm. Three types of steel beam-to-column joints were investigated: welded, endplate and bolted with top, seat and double web angles, respectively. The neural networks results proved to be consistent with experimental and design code reference values.

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