A maximum confidence enhancement based sequential sampling scheme for simulation-based design

Zequn Wang, Pingfeng Wang

Research output: Contribution to journalArticlepeer-review


A maximum confidence enhancement (MCE)-based sequential sampling approach is developed for reliability-based design optimization (RBDO) using surrogate models. The developed approach employs the ordinary Kriging method for surrogate model development and defines a cumulative confidence level (CCL) measure to quantify the accuracy of reliability estimation when Monte Carlo simulation is used based on the developed surrogate model. To improve the computational efficiency, an MCE-based sequential sampling scheme is developed to successively select sample points for surrogate model updating based on the defined CCL measure, in which a sample point that produces the largest CCL improvement will be selected. To integrate the MCE-based sequential sampling approach with RBDO, a new sensitivity analysis approach is developed, enabling smooth design sensitivity information to be accurately estimated based upon the constructed surrogate model without incurring any extra computational costs, thus greatly enhancing the efficiency and robustness of the design process. Two case studies are used to demonstrate the efficacy of the developed approach.

Original languageEnglish (US)
Article number021006
JournalJournal of Mechanical Design, Transactions of the ASME
Issue number2
StatePublished - Feb 2014
Externally publishedYes

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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