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Volume 42 Issue 3
Mar.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(3): 421-425.
MENG Guangwei, FENG Xinyu, LI Feng, et al. Structural reliability analysis based on dimensionality reduction and Edgeworth series[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 421-425. doi: 10.13700/j.bh.1001-5965.2015.0181(in Chinese)
Citation: MENG Guangwei, FENG Xinyu, LI Feng, et al. Structural reliability analysis based on dimensionality reduction and Edgeworth series[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 421-425. doi: 10.13700/j.bh.1001-5965.2015.0181(in Chinese)
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Structural reliability analysis based on dimensionality reduction and Edgeworth series

doi: 10.13700/j.bh.1001-5965.2015.0181

  • School of Mechanical Science and Engineering, Jilin University, Changchun 130025, China

Funds:  Foundation of Jilin Provincial Science & Technology Department (201205001, 201215048);National Key Scientific Instrument and Equipment Development Projects of China (2012YQ030075)
  • Received Date: 30 Mar 2015
  • Publish Date: 20 Mar 2016
    Abstract
  • A reliability analysis method based on the dimension reduction algorithm and the Edgeworth series was proposed to treat the complicate structures with implicit and high dimensional nonlinear limit state functions in practical engineering. By utilizing the dimension reduction method, the n-dimensional function was expanded to n unidimensional functions and the random variable were made to subject to the independent normal distribution with mean value being zero and variance deviation being 0.5 by means of the variable transformation. The origin moments of the unidimensional functions were obtained after the Gauss-Hermite integration. In this case, the central moments of the limit state function of the structure were achieved successfully and applied to the Edgeworth series expanding expressions, from which the cumulative distribution function of the limit state function could be generated and finally the probability of failure could be obtained. Avoiding gradient computation, the proposed method requires less definite reanalysis and is proved to be effective and correct via numerical examples.

     

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