A neuro-fuzzy system for steel beams patch load prediction

This paper presents a neuro-fuzzy system developed to predict and classify the behaviour of steel beam subjected to concentrated loads. A good performance was obtained with a previously developed neural network system by Fonseca et al., (1999, 2001, 2003) when compared to available experimental data. The neural network accuracy was also significantly better than existing prediction formulae (Lyse and Godfrey, 1935; Bergfelt, 1979; Skaloud and Drdacky, 1975; Roberts and Newark, 1997). Despite this fact, the system architecture did not explicitly consider the different structural behaviour related to the beam collapse (web and flange yielding, web buckling and web crippling). Therefore this paper presents a neuro-fuzzy system that takes into account the ultimate limit state. The neuro-fuzzy system architecture is composed of one neuro-fuzzy model and one prediction neural network. The neuro-fuzzy model is used to classify the beams according to its pertinence to a specific structural response. Then, a neural network uses the pertinence established by the neuro-fuzzy classification model, to finally determine the beam patch load resistance.