S.A.L. de Andrade

Experimental and mechanical model for predicting the behaviour of minor axis beam-to-column semi-rigid joints

This paper describes a series of experimental tests followed by 4nite element simulations produced to enable the prediction of moment resistance and rotation capacity of minor axis beam-to-column semi-rigid connections. These investigations motivated the development of a mechanical model to assess the connection’s structural response. The mechanical model is based on the component method of design, in accordance with the Eurocode 3 speci4cation. This philosophy implies that each joint component is represented by a spring possessing a non-linear force versus displacement (F–� ) curve. The model was subsequently calibrated against experimental and4nite element results previously performed . ? 2002 Elsevier Science Ltd. All rights reserved.

An evaluation of the dynamical performance of composite slabs

Abstract The competitive trends of the world market have long been forcing structural engineers to develop minimum weight and labour cost solutions. A direct consequence of this new design trend is a considerable increase in problems related to unwanted floor vibrations. This phenomenon is very frequent in a wide range of structures subjected to rhythmic dynamical load actions. These load actions are generally caused by human rhythmic activities such as: musical and or sporting events, dance or even gymnastics. The main objective of this paper is to investigate the structural behaviour of commonly used composite floors subjected to rhythmic dynamical load actions identifying the occurrence of unwanted vibrations that could cause human discomfort or, in extreme cases, structural failure.

A neuro-fuzzy evaluation of steel beams patch load 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 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.

Neural network evaluation of steel beam patch load capacity

This work presents a neural network modelling to forecast steel beam patch load resistance. In preceding studies, the results of a neural network system composed of four neural networks, have been compared and calibrated with experimental data and existing design formulae, showing a good agreement. Despite these results, the adopted system did not properly consider the differences in behaviour of slender, intermediate and compact beams. This paper introduces a new strategy based on a single neural network, which is trained with a different normalisation parameter. The neural network presented a maximum error value lower than 30%, while existing formulas presented errors greater than 40%.

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.