Probability of failure analysis and design using an efficient sequential sampling approach

Zequn Wang, Pingfeng Wang

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents a new efficient sequential sampling approach, referred to as maximum confidence enhancement (MCE) based sequential sampling, for failure probability analysis and design optimization using surrogate models. In the proposed approach, the ordinary Kriging method is adopted to construct surrogate models for all constraints and thus Monte Carlo simulation (MCS) is used to estimate reliability and its sensitivity with respect to design variables. A cumulative confidence level is defined to quantify the accuracy of reliability estimation using MCS based on the Kriging models. To improve the efficiency of proposed approach, a maximum confidence enhancement based sequential sampling scheme is developed to update the Kriging models based on the maximum improvement of the defined cumulative confidence level, in which a sample that produces the largest improvement of the cumulative confidence level is selected to update the surrogate model. A case study is used to demonstrate the efficacy of the proposed sequential sampling methodology.

Original languageEnglish (US)
DOIs
StatePublished - 2014
Externally publishedYes
Event16th AIAA Non-Deterministic Approaches Conference - SciTech Forum and Exposition 2014 - National Harbor, MD, United States
Duration: Jan 13 2014Jan 17 2014

Other

Other16th AIAA Non-Deterministic Approaches Conference - SciTech Forum and Exposition 2014
Country/TerritoryUnited States
CityNational Harbor, MD
Period1/13/141/17/14

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanics of Materials
  • Building and Construction
  • Architecture

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