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Proceedings ArticleDOI

Reliability-Based Design Optimization Using Efficient Global Reliability Analysis

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TLDR
The Efficient Global Reliability Analysis method was recently introduced to improve both the accuracy and efficiency of reliability analysis for this type of performance function and this paper explores how this new reliability analysis method can be used in a design optimization context to enable the use of reliability-based design optimization as a practical design tool.
Abstract
Finding the optimal (lightest, least expensive, etc.) design for an engineered component that meets or exceeds a specified level of reliability is a problem of obvious interest across a wide spectrum of engineering fields. Various methods for this reliability-based design optimization problem have been proposed. Unfortunately, this problem is rarely solved in practice because, regardless of the method used, solving the problem is too expensive or the final solution is too inaccurate to ensure that the reliability constraint is actually satisfied. This is especially true for engineering applications involving expensive, implicit, and possibly nonlinear performance functions (such as large finite element models). The Efficient Global Reliability Analysis method was recently introduced to improve both the accuracy and efficiency of reliability analysis for this type of performance function. This paper explores how this new reliability analysis method can be used in a design optimization context to create a method of sufficient accuracy and efficiency to enable the use of reliability-based design optimization as a practical design tool.

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Citations
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Journal ArticleDOI

A survey on approaches for reliability-based optimization

TL;DR: This contribution provides a survey on approaches for performing Reliability-based Optimization, with emphasis on the theoretical foundations and the main assumptions involved.
Dissertation

Adaptive surrogate models for reliability analysis and reliability-based design optimization

TL;DR: This manuscript proposes a surrogate-based strategy where the limit-state function is progressively replaced by a Kriging meta-model, a probabilistic design approach aimed at considering the uncertainty attached to the system of interest in order to provide optimal and safe solutions.
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Constrained efficient global optimization with support vector machines

TL;DR: This paper introduces an SVM-based “probability of feasibility” using a new Probabilistic SVM model and results indicate that the constrained formulation of the optimization problem is more robust and efficient.
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An improved adaptive sampling scheme for the construction of explicit boundaries

TL;DR: An improved adaptive sampling scheme for the construction of explicit decision functions (constraints or limit state functions) using Support Vector Machines (SVMs) using substantial modifications to an earlier version of the scheme.
Journal ArticleDOI

An adaptive hybrid surrogate model

TL;DR: The Adaptive Hybrid Functions (AHF) formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adaptively combines the favorable characteristics of different surrogate models to capture the global trend of the function as well as the local deviations.
References
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Journal ArticleDOI

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Noel A Cressie
- 01 Mar 1994 - 
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Journal ArticleDOI

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