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Master's Dissertation
DOI
10.11606/D.18.2015.tde-08042015-162956
Document
Author
Full name
Henrique Machado Kroetz
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2015
Supervisor
Committee
Beck, André Teófilo (President)
Leonel, Edson Denner
Lopes, Rafael Holdorf
Title in Portuguese
Meta-modelagem em confiabilidade estrutural
Keywords in Portuguese
Confiabilidade estrutural
Krigagem
Meta-modelos
Polinômios de caos
Redes neurais
Abstract in Portuguese
A aplicação de simulações numéricas em problemas de confiabilidade estrutural costuma estar associada a grandes custos computacionais, dada a pequena probabilidade de falha inerente às estruturas. Ainda que diversos casos possam ser endereçados através de técnicas de redução da variância das amostras, a solução de problemas envolvendo grande número de graus de liberdade, respostas dinâmicas, não lineares e problemas de otimização na presença de incertezas são comumente ainda inviáveis de se resolver por esta abordagem. Tais problemas, porém, podem ser resolvidos através de representações analíticas que aproximam a resposta que seria obtida com a utilização de modelos computacionais mais complexos, chamadas chamados meta-modelos. O presente trabalho trata da compilação, assimilação, programação em computador e comparação de técnicas modernas de meta-modelagem no contexto da confiabilidade estrutural, utilizando representações construídas a partir de redes neurais artificiais, expansões em polinômios de caos e através de krigagem. Estas técnicas foram implementadas no programa computacional StRAnD - Structural Reliability Analysis and Design, desenvolvido junto ao Departamento de Engenharia de Estruturas, USP, resultando assim em um benefício permanente para a análise de confiabilidade estrutural junto à Universidade de São Paulo.
Title in English
Meta-modeling techniques in structural reliability
Keywords in English
Artificial neural networks
Kriging
Meta-models
Polynomial caos
Structural reliability
Abstract in English
The application of numerical simulations to structural reliability problems is often associated with high computational costs, given the small probability of failure inherent to the structures. Although many cases can be addressed using variance reduction techniques, solving problems involving large number of degrees of freedom, nonlinear and dynamic responses, and problems of optimization in the presence of uncertainties are sometimes still infeasible to solve by this approach. Such problems, however, can be solved by analytical representations that approximate the response that would be obtained with the use of more complex computational models, called meta-models. This work deals with the collection, assimilation, computer programming and comparison of modern meta-modeling techniques in the context of structural reliability, using representations constructed from artificial neural networks, polynomial chaos expansions and Kriging. These techniques are implemented in the computer program StRAnD - Structural Reliability Analysis and Design, developed at the Department of Structural Engineering, USP; thus resulting in a permanent benefit to structural reliability analysis at the University of São Paulo.
 
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Publishing Date
2015-04-15
 
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