Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/53589

TitleAn innovative adaptive sparse response surface method for structural reliability analysis
Author(s)Guimarães, Hugo
Matos, José C.
Henriques, António A.
KeywordsStructural reliability
Response surface
Metamodel
Small failure probability
Confidence interval
Issue dateJul-2018
PublisherElsevier
JournalStructural Safety
CitationGuimarães, H., Matos, J. C., & Henriques, A. A. (2018). An innovative adaptive sparse response surface method for structural reliability analysis. Structural Safety, 73, 12-28
Abstract(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
TypeArticle
URIhttp://hdl.handle.net/1822/53589
DOI10.1016/j.strusafe.2018.02.001
ISSN0167-4730
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0167473017301108
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:ISISE - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
1-s2.0-S0167473017301108-main.pdf
  Restricted access
1,57 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID