A new sampling strategy for SVM-based response surface for structural reliability analysis. (July 2015)

• Record Type: Journal Article
• Authors: Alibrandi, Umberto; Alani, Amir M.; Ricciardi, Giuseppe
• Is Part Of: Probabilistic engineering mechanics. Volume 41(2015:Jul.)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-07
• Subjects: Response Surface -- SVM -- Structural Reliability Analysis -- Uncertainty -- Design Point -- FORM -- Sampling Directions; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2015.04.001
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: To evaluate failure probability of structures in the most general case is computationally demanding. The cost can be reduced by using the Response Surface Methodology, which builds a surrogate model of the target limit state function. In this paper authors consider a specific type of response surface, based on the Support Vector Method (SVM). Using the SVM the reliability problem is treated as a classification approach and extensive numerical experimentation has shown that each type of limit state can be adequately represented; however it could require a high number of sampling points. This work demonstrates that, by using a novel sampling strategy based on sampling directions, it is possible to obtain a good approximation of the limit state without high computational complexity. A second-order polynomial SVM model has been adopted, so the need of determining free parameters has been avoided. However, if needed, higher-order polynomial or Gaussian kernel can be adopted to approximate any kind of limit state. Some representative numerical examples show the accuracy and effectiveness of the presented procedure. Highlights: We evaluate the failure probability using a novel second-order Response Surface. The proposed Response Surface is based on Support Vector Method (SVM). It is adopted a novel sampling plan based on sampling directions, instead of sampling points. The effectiveness of the procedure is shown through several numerical examples.