A neuro-fuzzy evaluation of steel beams patch load behaviour

Abstract

This work presents a neuro-fuzzy system developed to predict and classify the behaviour of steel beam web panels subjected to concentrated loads. A good performance was obtained with a previously developed neural network system [Fonseca ET, Vellasco MMBR, Vellasco PCGdaS, de Andrade SAL, Pacheco MAC. A neural network system for patch load prediction. J Intell Robot Syst 2001;31(1/3):185–200; Fonseca ET, Vellasco PCGdaS, de Andrade SAL, Vellasco MMBR. A patch load parametric analysis using neural networks. J Constr Steel Res 2003;59(2):251–67; Fonseca ET, Vellasco PCGdaS, de Andrade SAL, Vellasco MMBR. Neural network evaluation of steel beam patch load capacity. Adv Eng Software 2003;34(11–12):763–72] when compared to available experimental data. The neural network accuracy was also significantly better than existing patch load prediction formulae [Lyse I, Godfrey HJ. Investigation of web buckling in steel beams. ASCE Trans 1935;100:675–95, paper 1907; Bergfelt A. Patch loading on slender web. Influence of horizontal and vertical web stiffeners on the load carrying capacity, S79:1. Goteborg: Chalmers University of Technology, Publication; 1979, p. 1–143; Skaloud M, Drdacky M. Ultimate load design of webs of steel plated structures – Part 3 webs under concentrated loads. Staveb Cas 1975;23(C3):140–60; Roberts TM, Newark ACB. Strength of webs subjected to compressive edge loading. J Struct Eng Am Soc Civil Eng 1997;123(2):176–83]. Despite this fact, the system architecture did not explicitly considered the fundamental 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 patch load ultimate limit state. The neuro-fuzzy system architecture is composed of one neuro-fuzzy classification model and one patch load 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.