A unified neural network approach for steel beams patch load capacity

  • Authors:

  • E. T. Fonseca;M. M. B. R. Vellasco;S. A. L. de Andrade;P. C. G. da S. Vellasco

  • Affiliations:

  • Civil Engineering Department, Pontifical Catholic University of Rio de Janeiro, Brazil;Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Brazil and Systems Engineering Department, State University of Rio de Janeiro, Brazil;Civil Engineering Department, Pontifical Catholic University of Rio de Janeiro, Brazil and Structural Engineering Department, State University of Rio de Janeiro, Brazil;Structural Engineering Department, State University of Rio de Janeiro, Brazil

  • Venue:
  • ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

This work presents a neural networks modelling to forecast steel beam's patch load resistance. In preceding studies, neural network results have been compared and calibrated with experimental data and existing design formulae, showing a good agreement. Despite these results, the adopted method did not properly consider the differences in behaviour of slender, intermediate and compact beams. This paper introduces a new strategy based on a single neural network containing all the 155 experimental results. The Neural Network presented a maximum error value lower than 30%, while the existing formulas presented errors greater than 40%.