Abstract
Reliability analysis of slope considering the spatial variability of soil properties may be subjected to the curse of high dimensionality, which leads to the traditional slope reliability analysis method cannot effectively carry out. This paper aims to propose a sliced inverse regression (SIR)-based multivariate adaptive regression spline (MARS) method for slope reliability analysis in spatially variable soils, which combines the advantages of both SIR and MARS. First, the Karhunen-Loève (K-L) expansion is adopted to simulate the spatial variability of soil properties. Second, the slope reliability analysis based on the SIR-MARS method is proposed. Thereafter, the implementation procedure for slope reliability evaluation in spatially variable soils using the proposed method is summarized. The validity of the proposed method is illustrated with a single-layered c-φ slope and a two-layered c-φ slope. The results indicate that, in the case of higher dimensions of random variables, the MARS model with the aid of SIR can effectively establish the relationship between soil shear strength parameters of slopes in spatially variable soils and safety factor (FS). Moreover, the proposed method can obtain sufficiently accurate reliability results for both single-layer and two-layer slopes in spatially variable soils with a low computational cost. The proposed method provides an effective and practical way to solve the reliability problem of high dimensional spatial variation slope.
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Funding
This work was supported by the National Natural Science Foundation of China (Project Nos. 52009054, 51969018, 51769017, 41867036), and the Science and Technology Project funded by the Department of Education of Jiangxi Province (Project No. GJJ201922).
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Deng, ZP., Pan, M., Niu, JT. et al. Slope reliability analysis in spatially variable soils using sliced inverse regression-based multivariate adaptive regression spline. Bull Eng Geol Environ 80, 7213–7226 (2021). https://doi.org/10.1007/s10064-021-02353-9
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DOI: https://doi.org/10.1007/s10064-021-02353-9