An equivalent reliability index approach for surrogate model-based rbdo

Mingyang Li, Zequn Wang, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In reliability-based design optimizations, surrogate model has been widely used to replace expensive physical-based simulations in computationally intensive engineering applications. Due to the lack of data, surrogate model uncertainty is involved since surrogate models cannot perfectly represent actual simulations or experiments. To ensure a reliable optimal design, surrogate model uncertainty needs to be considered in design under uncertainties when the training data is limited. In this work, we presents an equivalent reliability index (ERI) approach to handle both parameter variations and surrogate model uncertainty in RBDO. The Gaussian process (GP) modeling technique is first employed for building surrogate models and Monte Carlo simulations are used for reliability assessment with the consideration of input randomness. In detail, a Gaussian mixture model (GMM) is constructed based on the GP model, and the reliability is estimated by an equivalent reliability index that calculated based on the first and second statistical moment of the GMM. To facilitate the RBDO process, the sensitivity of the ERI is analytically derived without incurring extra computational costs. Two case studies are used to demonstrate the effectiveness and robustness of the proposed approach.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
StatePublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
CountryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Aerospace Engineering

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