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An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils

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  • Ding, Jiayi
  • Zhou, Jianfang
  • Cai, Wei

Abstract

The random finite element method (RFEM) is an efficient tool to demonstrate the spatial variability of soil properties during reliability analysis of slopes, but it requires remarkable model evaluations and computational efforts. In this paper, an efficient variable selection-based Kriging model method is proposed to approximate the finite element analysis model in reliability analysis of slopes. The variable selection technique successfully remedies the “curse of dimensionality†within Kriging model induced by the numerous random variables in random field discretization. The implementation procedure of this method for the reliability analysis of slopes is introduced in detail. Two typical examples of soil slopes, as well as a real complex slope are subsequently analyzed to illustrate the validity of the proposed method. The results show that the local loss of variability of random field due to the variable selection method has little impact on the safety factor of slopes. The proposed method can significantly reduce the number of finite element analysis and obtain accurate results in reliability analysis of slopes considering the spatial variability of soil properties.

Suggested Citation

  • Ding, Jiayi & Zhou, Jianfang & Cai, Wei, 2023. "An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001497
    DOI: 10.1016/j.ress.2023.109234
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    Cited by:

    1. Wu, Yongxin & Wang, Juncheng & Cheng, Jialiang & Yang, Shangchuan, 2024. "Dimension-Reduction Spectral Representation of Soil Spatial Variability and Its Application in the Efficient Reliability Analysis of Seismic Response in Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Nguyen, Phong T.T. & Manuel, Lance, 2024. "Uncertainty quantification in low-probability response estimation using sliced inverse regression and polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Jerez, Danko J. & Chwała, M. & Jensen, Hector A. & Beer, Michael, 2024. "Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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