期刊
ADVANCES IN ENGINEERING SOFTWARE
卷 39, 期 7, 页码 558-572出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2007.07.005
关键词
patch load; steel structures; neural networks; fuzzy inference system; web buckling; web crippling and yielding; structural design and behaviour
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 Intel] 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 FT, 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 1, 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):176-831. 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. (c) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据