Abstract
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. Copyright © 2005 by ABCM.
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De Lima, L. R. O., Vellasco, P. C. G. D. S., De Andrade, S. A. L., Da Silva, J. G. S., & Vellasco, M. M. B. R. (2005). Neural networks assessment of beam-to-column joints. Journal of the Brazilian Society of Mechanical Sciences and Engineering. Brazilian Society of Mechanical Sciences and Engineering. https://doi.org/10.1590/s1678-58782005000300015
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