Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment
Science Journal of Applied Mathematics and Statistics
Volume 3, Issue 6, December 2015, Pages: 263-274
Received: Nov. 28, 2015; Accepted: Dec. 5, 2015; Published: Dec. 22, 2015
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Authors
Liu Chu, Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France
Eduardo Souza De Cursi, Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France
Abdelkhalak El Hami, Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France
Mohamed Eid, Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France
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Abstract
Reliability assessment is one of the necessary and critical parts in structural design under uncertainties. The methods for structural reliability assessment aim at evaluating the probability of limit state by considering the fluctuation of acting loads, variation of structural component or system, and complexity of operating environment. Latin Hypercube sampling (LHS) method as advanced Monte Carlo simulation (MCS) has higher efficiency in sampling. It will be chosen and applied in this paper in order to obtain an effective database for building Kriging surrogate models. In this paper, we propose an effective method to have reliability assessment by Latin Hypercube sampling based Kriging surrogate models. This method keeps the certain level of accuracy in prediction of the response of a structural finite element model or other explicit mathematical functions.
Keywords
Latin Hypercube Sampling, Kriging Models, Reliability Assessment
To cite this article
Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid, Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment, Science Journal of Applied Mathematics and Statistics. Vol. 3, No. 6, 2015, pp. 263-274. doi: 10.11648/j.sjams.20150306.16
Copyright
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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