NDLI: Sampling-Based RBDO Using Probabilistic Sensitivity Analysis and Virtual Support Vector Machine

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Song, Hyeongjin Choi, K. K. Lee, Ikjin Zhao, Liang Lamb, David
Copyright Year	2012
Abstract	In this paper, a sampling-based RBDO method using a classification method is presented. The probabilistic sensitivity analysis is used to compute sensitivities of probabilistic constraints with respect to random variables. Since the probabilistic sensitivity analysis requires only the limit state function, and not the response surface or sensitivity of the response, an efficient classification method can be used for a sampling-based RBDO. The proposed virtual support vector machine (VSVM), which is a classification method, is a support vector machine (SVM) with virtual samples. By introducing virtual samples, VSVM overcomes the deficiency in existing SVM that uses only classification information as their input. In this paper, the universal Kriging method is used to obtain locations of virtual samples to improve the accuracy of the limit state function for highly nonlinear problems. A sequential sampling strategy effectively inserts new samples near the limit state function. In sampling-based RBDO, Monte Carlo simulation (MCS) is used for the reliability analysis and probabilistic sensitivity analysis. Since SVM is an explicit classification method, unlike implicit methods, computational cost for evaluating a large number of MCS samples can be significantly reduced. Several efficiency strategies, such as the hyper-spherical local window for generation of the limit state function and the Transformations/Gibbs sampling method to generate uniform samples in the hyper-sphere, are also applied. Examples show that the proposed sampling-based RBDO using VSVM yields better efficiency in terms of the number of required samples and the computational cost for evaluating MCS samples while maintaining accuracy similar to that of sampling-based RBDO using the implicit dynamic Kriging (D-Kriging) method.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
Starting Page	1213
Ending Page	1225
Page Count	13
File Format	PDF
ISBN	9780791845028
DOI	10.1115/DETC2012-70715
Volume Number	Volume 3: 38th Design Automation Conference, Parts A and B
Conference Proceedings	ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2012-08-12
Publisher Place	Chicago, Illinois, USA
Access Restriction	Subscribed
Subject Keyword	Support vector machine (svm) Virtual support vector machine (vsvm) Surrogate model Sequential sampling Probabilistic sensitivity analysis Hyper-spherical local window Sampling-based rbdo Virtual samples Support vector machines Sensitivity analysis
Content Type	Text
Resource Type	Article

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