@article{bfc5e116c3df4bc89cb9716c66eff071,
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.",
}