Science Journal of Applied Mathematics and Statistics

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Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment

Received: 28 November 2015    Accepted: 05 December 2015    Published: 22 December 2015
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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.

DOI 10.11648/j.sjams.20150306.16
Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 6, December 2015)
Page(s) 263-274
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Latin Hypercube Sampling, Kriging Models, Reliability Assessment

References
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Author Information
  • Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France

  • Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France

  • Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France

  • Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, Rouen, France

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  • APA Style

    Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. (2015). Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment. Science Journal of Applied Mathematics and Statistics, 3(6), 263-274. https://doi.org/10.11648/j.sjams.20150306.16

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    ACS Style

    Liu Chu; Eduardo Souza De Cursi; Abdelkhalak El Hami; Mohamed Eid. Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment. Sci. J. Appl. Math. Stat. 2015, 3(6), 263-274. doi: 10.11648/j.sjams.20150306.16

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    AMA Style

    Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment. Sci J Appl Math Stat. 2015;3(6):263-274. doi: 10.11648/j.sjams.20150306.16

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  • @article{10.11648/j.sjams.20150306.16,
      author = {Liu Chu and Eduardo Souza De Cursi and Abdelkhalak El Hami and Mohamed Eid},
      title = {Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {3},
      number = {6},
      pages = {263-274},
      doi = {10.11648/j.sjams.20150306.16},
      url = {https://doi.org/10.11648/j.sjams.20150306.16},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjams.20150306.16},
      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.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Application of Latin Hypercube Sampling Based Kriging Surrogate Models in Reliability Assessment
    AU  - Liu Chu
    AU  - Eduardo Souza De Cursi
    AU  - Abdelkhalak El Hami
    AU  - Mohamed Eid
    Y1  - 2015/12/22
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    DO  - 10.11648/j.sjams.20150306.16
    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
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    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20150306.16
    AB  - 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.
    VL  - 3
    IS  - 6
    ER  - 

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