Neural networks forecasting of endplate steel connections capacity

Authors:
L. R. O. de Lima;P. C. G. da S. Vellasco;S. A. L. de Andrade;M. M. B. R. Vellasco
Affiliations:
Civil Engineering Department, Pontifical Catholic University of Rio de Janeiro, Brazil;Structural Engineering Department, State University of Rio de Janeiro, Brazil;Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Brazil;Systems 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

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Abstract

This paper proposes the use of artificial neural networks to predict the flexural resistance and initial stiffness of beam-to-column end plate connections. In this work, the Back Propagation supervised learning algorithm has been used. The results of 26 experimental tests (21 for training and 5 for testing) were used, producing satisfactory results. The mean errors obtained were 8.5% and 26% for the prediction of the flexural resistance and the initial stiffness of beam-to-column connections, respectively.