Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/53589
Título: | An innovative adaptive sparse response surface method for structural reliability analysis |
Autor(es): | Guimarães, Hugo Matos, José C. Henriques, António A. |
Palavras-chave: | Structural reliability Response surface Metamodel Small failure probability Confidence interval |
Data: | Jul-2018 |
Editora: | Elsevier 1 |
Revista: | Structural Safety |
Citação: | Guimarães, H., Matos, J. C., & Henriques, A. A. (2018). An innovative adaptive sparse response surface method for structural reliability analysis. Structural Safety, 73, 12-28 |
Resumo(s): | In the scope of infrastructure risk assessment, structural reliability analysis leads to a challenging problem in order to deal with conflicting objectives: accurate estimation of failure probabilities and computational efficiency. Since the application of classical reliability methods is limited and often leads to a prohibitive computational cost, metamodeling techniques (e.g. polynomial chaos, kriging, response surface methods (RSM), etc.) have been widely used. Nevertheless, existing RSM present limitations handling with highly non-linear limit states, large-scale problems and approximation error. To overcome these problems, this paper describes a cutting-edge response surface algorithm covering the following issues: (i) dimensionality reduction by a variable screening procedure; (ii) definition of a promising search domain; (iii) initial experimental design based on an optimized space-filling scheme; (iv) model selection according to a stepwise regression procedure; (v) model validation by a cross-validation approach; (vi) model fitting using a double weighted regression technique; (vii) sequential sampling scheme by exploring a defined region of interest; (viii) confidence interval of reliability estimates based on a bootstrapping technique. With the aim of proving its efficiency, a wide collection of six illustration examples, concerning both analytical and FE-based problems, was selected. By benchmarking obtained results with literature findings, proposed method not only outperforms existing RSM, but also provides a powerful alternative to the use of other metamodeling techniques |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/53589 |
DOI: | 10.1016/j.strusafe.2018.02.001 |
ISSN: | 0167-4730 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S0167473017301108 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito autor |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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1-s2.0-S0167473017301108-main.pdf Acesso restrito! | 1,57 MB | Adobe PDF | Ver/Abrir |