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EE - Escola de Engenharia

URI permanente desta comunidadehttps://rihomolog.furg.br/handle/1/512

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2010 - 20112

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Autor: search.filters.author.Awruch, Armando MiguelAssunto: search.filters.subject.Artificial neural networks

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Reliability analysis of laminated composite structures using finite elements and neural networks
    (2010) Lopes, Paulo André Menezes; Gomes, Herbert Martins; Awruch, Armando Miguel
    Saving of computer processing time on the reliability analysis of laminated composite structures using artificial neural networks is the main objective of this work. This subject is particularly important when the reliability index is a constraint in the optimization of structural performance, because the task of looking for an optimum structural design demands also a very high processing time. Reliability methods, such as Standard Monte Carlo (SMC), Monte Carlo with Importance Sampling (MC–IS), First Order Reliability Method (FORM) and FORM with Multiple Check Points (FORM–MCPs) are used to compare the solution and the processing time when the Finite Element Method (FEM) is employed and when the finite element analysis (FEA) is substituted by trained artificial neural networks (ANNs). Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN). Several examples are presented, including a shell with geometrically non-linear behavior, which shows the advantages using this methodology.
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    Reliability based optimization of laminated composite structures using genetic algorithms and artificial neural networks
    (2011) Gomes, Herbert Martins; Awruch, Armando Miguel; Lopes, Paulo André Menezes
    The design of anisotropic laminated composite structures is very susceptible to changes in loading, angle of fiber orientation and ply thickness. Thus, optimization of such structures, using a reliability index as a constraint, is an important problem to be dealt. This paper addresses the problem of structural optimization of laminated composite materials with reliability constraint using a genetic algorithm and two types of neural networks. The reliability analysis is performed using one of the following methods: FORM, modified FORM (FORM with multiple checkpoints), the Standard or Direct Monte Carlo and Monte Carlo with Importance Sampling. The optimization process is performed using a genetic algorithm. To overcome high computational cost it is used Multilayer Perceptron or Radial Basis Artificial Neural Networks. It is shown, presenting two examples, that this methodology can be used without loss of accuracy and large computational time savings, even when dealing with non-linear behavior.