An active weight learning method for efficient reliability assessment with small failure probability-Reference-Cited by-同舟云学术

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An active weight learning method for efficient reliability assessment with small failure probability

Published:2019-11-16 Issue:3 Volume:61 Page:1157-1170
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Meng Zeng,Zhang Zhuohui,Li Gang,Zhang Dequan^ORCID

Funder

National Natural Science Foundation of China

Foundation of State Key Laboratory of Structural Analysis for Industrial Equipment from Dalian University of Technology

Fundamental Research Funds for Central Universities of the Central South University

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Graphics and Computer-Aided Design,Computer Science Applications,Control and Systems Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s00158-019-02419-z.pdf

Reference55 articles.

1. Balesdent M, Morio J, Marzat J (2013) Kriging-based adaptive importance sampling algorithms for rare event estimation. Struct Saf 44:1–10. https://doi.org/10.1016/j.strusafe.2013.04.001

2. Basudhar A, Missoum S (2008) Adaptive explicit decision functions for probabilistic design and optimization using support vector machines. Comput Struct 86:1904–1917. https://doi.org/10.1016/j.compstruc.2008.02.008

3. Bichon BJ, McFarland JM, Mahadevan S (2011) Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf 96:1386–1395. https://doi.org/10.1016/j.ress.2011.05.008

4. Bichon BJ, Eldred MS, Mahadevan S, McFarland JM (2012) Efficient global surrogate modeling for reliability-based design optimization. J Mech Des 135:011009. https://doi.org/10.1115/1.4022999

5. Birge B (2003) PSOt-a particle swarm optimization toolbox for use with Matlab. In: Swarm Intelligence Symposium, 2003. SIS’03. Proceedings of the 2003 IEEE. IEEE, pp 182-186

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