English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reliability-based design optimization (RBDO) is an active field of researchwith an ever increasing number of contributions. Numerous methods have beenproposed for the solution of RBDO, a complex problem that combines optimizationand reliability analysis. Classical approaches are based on approximationmethods and have been classified in review papers. In this paper, we firstreview classical approaches based on approximation methods such as FORM, andalso more recent methods that rely upon surrogate modelling and Monte Carlosimulation. We then propose a general framework for the solution of RBDOproblems that includes three independent blocks, namely adaptive surrogatemodelling, reliability analysis and optimization. These blocks arenon-intrusive with respect to each other and can be plugged independently inthe framework. After a discussion on numerical considerations that requireattention for the framework to yield robust solutions to various types ofproblems, the latter is applied to three examples (using two analyticalfunctions and a finite element model). Kriging and support vector machinestogether with their own active learning schemes are considered in the surrogatemodel block. In terms of reliability analysis, the proposed framework isillustrated using both crude Monte Carlo and subset simulation. Finally, thecovariance-matrix adaptation - evolution scheme (CMA-ES), a global searchalgorithm, or sequential quadratic programming (SQP), a local gradient-basedmethod, are used in the optimization block. The comparison of the results tobenchmark studies show the effectiveness and efficiency of the proposedframework.

Surrogate-assisted reliability-based design optimization: a survey and a new general framework