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Adaptive Virtual Support Vector Machine for the Reliability Analysis of High-Dimensional Problems

[+] Author Affiliations
Hyeongjin Song, K. K. Choi, Ikjin Lee, Liang Zhao

The University of Iowa, Iowa City, IA

David Lamb

US Army RDECOM/TARDEC, Warren, MI

Paper No. DETC2011-47538, pp. 1139-1149; 11 pages
doi:10.1115/DETC2011-47538
From:
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 37th Design Automation Conference, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5482-2
  • Copyright © 2011 by ASME

abstract

In this study, an efficient classification methodology is developed for reliability analysis while maintaining the accuracy level similar to or better than existing response surface methods. The sampling-based reliability analysis requires only the classification information — a success or a failure – but the response surface methods provide real function values as their output, which requires more computational effort. The problem is even more challenging to deal with high-dimensional problems due to the curse of dimensionality. In the newly proposed virtual support vector machine (VSVM), virtual samples are generated near the limit state function by using linear or Kriging-based approximations. The exact function values are used for approximations of virtual samples to improve accuracy of the resulting VSVM decision function. By introducing the virtual samples, VSVM can overcome the deficiency in existing classification methods where only classified function values are used as their input. The universal Kriging method is used to obtain virtual samples to improve the accuracy of the decision function for highly nonlinear problems. A sequential sampling strategy that chooses a new sample near the true limit state function is integrated with VSVM to maximize the accuracy. Examples show the proposed adaptive VSVM yields better efficiency in terms of the modeling time and the number of required samples while maintaining similar level or better accuracy especially for high-dimensional problems.

Copyright © 2011 by ASME

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