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    中国百强科技报刊

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    Volume 48 Issue 5
    May  2023
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    Article Contents
    Liu Leilei, Liang Changqi, Xu Meng, Zhu Wenqing, Zhang Shaohe, Ding Xingyu, 2023. Probabilistic Analysis of Large Slope Deformation Considering Soil Spatial Variability with Rotated Anisotropy. Earth Science, 48(5): 1836-1852. doi: 10.3799/dqkx.2022.372
    Citation: Liu Leilei, Liang Changqi, Xu Meng, Zhu Wenqing, Zhang Shaohe, Ding Xingyu, 2023. Probabilistic Analysis of Large Slope Deformation Considering Soil Spatial Variability with Rotated Anisotropy. Earth Science, 48(5): 1836-1852. doi: 10.3799/dqkx.2022.372

    Probabilistic Analysis of Large Slope Deformation Considering Soil Spatial Variability with Rotated Anisotropy

    doi: 10.3799/dqkx.2022.372
    • Received Date: 2022-09-08
      Available Online: 2023-06-06
    • Publish Date: 2023-05-25
    • To explore the influence of spatial variability of soil parameters with rotated anisotropy on probabilistic large slope deformation characteristics, a multiple response surface-based random material point method is proposed in this study. First, random field theory is employed to simulate the spatial variability of soil parameters with rotated anisotropy. Then, the multiple response surface method is used to evaluate the factor of safety (FS) of each random field sample, based on which the FSs for all random field samples are sorted efficiently in an ascending order. Finally, the random material point method is used to sequentially simulate the large deformation features of a slope for the failed samples with the FS at less than 1. An undrained clay slope is taken as the illustrative example, where the undrained shear strength of the soil is simulated as a rotated anisotropic random field. The effect of the rotational angle β and the autocorrelation length θ2 in the minor principal direction of the rotated anisotropic random field on the large deformation features and failure modes of the slope are systematically studied. The results show that the proposed method can be efficiently conducted for probabilistic analysis of large slope deformation. Both β and θ2 have significant effects on large slope deformation features and failure modes. In terms of large deformation features, the mean and standard deviation of the influence distance, sliding distance, and sliding volume increase with the increase of θ2. There might be four failure modes when considering the rotated anisotropic spatial variability of the undrained strength of the slope, among which the deep sliding mechanism and progressive failure are the two main probabilistic failure modes. Therefore, the proposed method provides an effective way for the probabilistic large slope deformation analysis as well as a good theoretical reference for an accurate risk assessment of slope stability.

       

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