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Volume 42 Issue 5
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TAN Xiao-hui, DONG Xiao-le, FEI Suo-zhu, GONG Wen-ping, XIU Lin-tian, HOU Xiao-liang, MA Hai-chun. Reliability analysis method based on KL expansion and its application[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(5): 808-816. DOI: 10.11779/CJGE202005002
Citation: TAN Xiao-hui, DONG Xiao-le, FEI Suo-zhu, GONG Wen-ping, XIU Lin-tian, HOU Xiao-liang, MA Hai-chun. Reliability analysis method based on KL expansion and its application[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(5): 808-816. DOI: 10.11779/CJGE202005002
shu

Reliability analysis method based on KL expansion and its application

  • 1.

    School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China

  • 2.

    Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

  • 3.

    Anhui Construction Engineering Test and Research Institute Co., Ltd., Hefei 230051, China

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  • Received Date: August 15, 2019
  • Available Online: December 07, 2022
    Graphical Abstract
    • Abstract
  • The spatial variability is an inherent attribute of soil properties. To consider the influences of spatial variability of soils on the reliability of geotechnical engineering, a KL-FORM and a KL-RSM are proposed, in which the Karhunen-Loeve expansion method is used for the discretization of random field, and the first-order reliability method (FORM) or response surface method is adopted for the reliability analysis based on the results of KL expansion. By considering the coefficients of the KL series expansion as the basic variables in the reliability analysis, the KL expansion method and the FORM or RSM can be combined together. An iterative method is proposed for determining the number of KL expansion terms, and flow charts of the KL expansion and the KL-FORM are presented. The proposed KL-FORM and KL-RSM are demonstrated by two case studies: a shallow foundation in a spatially variable unsaturated soil, and an undrained saturated clay slope with spatial variability. The case studies show that the KL-FORM and the KL-RSM are computationally accurate and efficient. Under the requirements of a certain discrete error, the number of KL expansion items and the reliability index decrease with the increase of autocorrelation distance.
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    • References (32)
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