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.

考虑土性空间变异性的边坡可靠度分析可能会受到高维数的诅咒，导致传统的边坡可靠度分析方法无法有效开展。本文旨在提出一种基于切片逆回归（SIR）的多元自适应回归样条（MARS）方法，用于空间可变土壤的边坡可靠性分析，它结合了SIR和MARS的优点。首先，采用 Karhunen-Loève (KL) 扩展来模拟土壤性质的空间变异性。其次，提出了基于SIR-MAR​​S方法的边坡可靠度分析。此后，总结了使用所提出的方法在空间可变土壤中进行边坡可靠性评估的实施过程。c - φ斜率和两层c - φ斜率。结果表明，在随机变量维数较高的情况下，借助SIR的MARS模型可以有效地建立空间变量土中边坡土抗剪强度参数与安全系数（FS）之间的关系。此外，所提出的方法可以以较低的计算成本获得空间可变土壤中单层和双层边坡的足够准确的可靠性结果。该方法为解决高维空间变化斜率的可靠性问题提供了一种有效而实用的方法。