Reliability-based optimization using bridge importance sampling

P. Beaurepaire; H. A. Jensen; G. I. Schuëller; M. A. Valdebenito

doi:10.1016/j.probengmech.2013.04.001

Reliability-based optimization using bridge importance sampling

P. Beaurepaire, H. A. Jensen, G. I. Schuëller, M. A. Valdebenito

Faculty of Engineering and Science

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

Abstract

This paper introduces an efficient reliability estimation approach to be used in the framework of reliability-based optimization. The key feature of the procedure consists of reusing during the optimization procedure the results of the previous iterations. The reliability analysis is performed subsequently with a reduced number of samples which greatly decreases the computational efforts associated with the reliability-based optimization process. The validity and the advantages of the procedure are demonstrated by means of two reliability-based optimization problems.

Original language	English
Pages (from-to)	48-57
Number of pages	10
Journal	Probabilistic Engineering Mechanics
Volume	34
DOIs	https://doi.org/10.1016/j.probengmech.2013.04.001
State	Published - 2013

Keywords

Advanced simulation techniques
Carlo
Importance sampling
Markov chain
Monte
Reliability-based optimization

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10.1016/j.probengmech.2013.04.001

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title = "Reliability-based optimization using bridge importance sampling",

abstract = "This paper introduces an efficient reliability estimation approach to be used in the framework of reliability-based optimization. The key feature of the procedure consists of reusing during the optimization procedure the results of the previous iterations. The reliability analysis is performed subsequently with a reduced number of samples which greatly decreases the computational efforts associated with the reliability-based optimization process. The validity and the advantages of the procedure are demonstrated by means of two reliability-based optimization problems.",

keywords = "Advanced simulation techniques, Carlo, Importance sampling, Markov chain, Monte, Reliability-based optimization",

author = "P. Beaurepaire and Jensen, {H. A.} and Schu{\"e}ller, {G. I.} and Valdebenito, {M. A.}",

note = "Funding Information: This research was partially supported by the Austrian Science Foundation (FWF) under Contract no. P20251-N13, the North West Development Agency and European Regional Development Fund located at the Daresbury Laboratory of the Science and Technology Facilities Council, and CONICYT (National Commission for Scientific and Technological Research) under Grant nos. 1110061 and 11121250, which is gratefully acknowledged by the authors.",

year = "2013",

doi = "10.1016/j.probengmech.2013.04.001",

language = "English",

volume = "34",

pages = "48--57",

journal = "Probabilistic Engineering Mechanics",

issn = "0266-8920",

publisher = "Elsevier Ltd.",

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TY - JOUR

T1 - Reliability-based optimization using bridge importance sampling

AU - Beaurepaire, P.

AU - Jensen, H. A.

AU - Schuëller, G. I.

AU - Valdebenito, M. A.

N1 - Funding Information: This research was partially supported by the Austrian Science Foundation (FWF) under Contract no. P20251-N13, the North West Development Agency and European Regional Development Fund located at the Daresbury Laboratory of the Science and Technology Facilities Council, and CONICYT (National Commission for Scientific and Technological Research) under Grant nos. 1110061 and 11121250, which is gratefully acknowledged by the authors.

PY - 2013

Y1 - 2013

N2 - This paper introduces an efficient reliability estimation approach to be used in the framework of reliability-based optimization. The key feature of the procedure consists of reusing during the optimization procedure the results of the previous iterations. The reliability analysis is performed subsequently with a reduced number of samples which greatly decreases the computational efforts associated with the reliability-based optimization process. The validity and the advantages of the procedure are demonstrated by means of two reliability-based optimization problems.

AB - This paper introduces an efficient reliability estimation approach to be used in the framework of reliability-based optimization. The key feature of the procedure consists of reusing during the optimization procedure the results of the previous iterations. The reliability analysis is performed subsequently with a reduced number of samples which greatly decreases the computational efforts associated with the reliability-based optimization process. The validity and the advantages of the procedure are demonstrated by means of two reliability-based optimization problems.

KW - Advanced simulation techniques

KW - Carlo

KW - Importance sampling

KW - Markov chain

KW - Monte

KW - Reliability-based optimization

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U2 - 10.1016/j.probengmech.2013.04.001

DO - 10.1016/j.probengmech.2013.04.001

M3 - Article

AN - SCOPUS:84879525897

SN - 0266-8920

VL - 34

SP - 48

EP - 57

JO - Probabilistic Engineering Mechanics

JF - Probabilistic Engineering Mechanics

ER -

Reliability-based optimization using bridge importance sampling

Abstract

Keywords

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