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Fonseca, E. T.; Vellasco, Marley; Pacheco, Marco Aurélio Cavalcanti; Vellasco, P.C.G. da S.; Andrade, S.A.L. de

doi:10.1023/a:1012027726962

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…A third model was investigated [3], based on a single neural network containing all the 155 experimental results. In this particular model, the neural network output adopted a different ultimate load normalisation technique, shown in equation 9.…”

Section: Previous Investigationsmentioning

confidence: 99%

“…where k is a variable that takes into account the influence of the flanges in the patch load distribution being defined by a comparison of the best trained neural networks [3]. This normalization strategy was adopted due to the large range present among the minimum and maximum load output values (from 30 kN up to 4010 kN).…”

Section: Previous Investigationsmentioning

confidence: 99%

“…A previously developed neural network system [1][2][3] was compared to available experimental data, attaining good performance with an average error of 6.71%. The neural network accuracy was also significantly better than existing patch load prediction formulae [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A neuro-fuzzy system for steel beams patch load prediction

Fonseca

Vellasco

et al. 2005

Fifth International Conference on Hybrid Intelligent Systems (HIS'05)

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This work 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 [1][2][3] when compared to available experimental data. The neural network accuracy was also significantly better than existing prediction formulae [4][5][6][7]. Despite this fact, the system architecture did not explicitly considered 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.

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Section: Previous Investigationsmentioning

confidence: 99%

Section: Previous Investigationsmentioning

confidence: 99%

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A neuro-fuzzy system for steel beams patch load prediction

Fonseca

Vellasco

et al. 2005

Fifth International Conference on Hybrid Intelligent Systems (HIS'05)

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Prediction of the local buckling strength and load‐displacement behaviour of SHS and RHS members using Deep Neural Networks (DNN) – Introduction to the Deep Neural Network Direct Stiffness Method (DNN‐DSM)

Müller

Taras

2022

Steel Construction

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The traditional separation of analysis and verification during the structural design of steel structures is a known source of conservatism and inaccuracy, as the true deformation/rotation capacity of sections and the redistribution of internal forces in systems remains only vaguely known in many cases. This particularly affects structures made of high-strength steel, since often sections would need to be classified as slender, thus disallowing the possibility to account for plasticity and stress redistribution. Shell-element FEM-models with material nonlinearities and imperfections would be suitable to overcome this separation and increase the accuracy and economy of designs, yet are computationally intensive and impractical for design of whole structures. In this paper, a novel approach for carrying out a computationally economical beam-element analysis that accounts for the nonlinear load-displacement behaviour of sections of various local slenderness is presented: the "DNN-DSM", which makes use of machine learning techniques (deep neural networks -DNN) to predict the nonlinear stiffness matrix terms in a beam-element formulation for implementation in the Direct Stiffness Method. Based on trained DNN models from an extensive pool of nonlinear (GMNIA) shell element results. The motivation, general features, and first implementations of this method in the sense of a "proof-of-concept", for the case of hollow-section truss members, are presented in the paper, as well as an outlook on the method's on-going, full implementation.Keywords machine learning and deep learning; deep neural networks (DNN); advanced inelastic analysis; GMNIA simulations; Direct Stiffness Method; high strength steel; structural hollow sections This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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