Neural networks forecasting of endplate steel connections capacity

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.