Abstract This paper investigates the performances of three conditional methods in slope reliability evaluation with spatially variable soils, considering various sampling strategies and rotated transverse anisotropy. The first method is based on conditional random field (RF) (i.e., conditional RF model 1), where the fluctuation component of the RF model is simulated using Kriging interpolation. The second method is also based on conditional RF (i.e., conditional RF model 2), where matrix decomposition is implemented on the conditional autocorrelation matrix. The third method originates from Sobol’ index formulation, which is extended to enable the use of correlated input random variables. When sample points are distributed sparsely or near strata orientation, conditional RF model 1 would produce significantly smaller magnitude of uncertainty reduction and reliability index (β) than those by other conditional methods. Also, in these situations, the standard deviation of factor of safety (FS) after conditioning by conditional RF model 1 may be larger than the unconditional standard deviation of FS. This is unexpected in slope reliability evaluation. Besides, β by conditional RF simulation methods for reverse slopes may be smaller than that for dip slopes, which is against engineering experiences, while this issue cannot be found when using unconditional RF.

中文翻译：

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边坡可靠度评价条件法比较研究

摘要 本文研究了三种条件方法在空间可变土壤边坡可靠性评估中的性能，考虑了各种采样策略和旋转横向各向异性。第一种方法基于条件随机场（RF）（即条件RF模型1），其中RF模型的波动分量使用克里金插值法进行模拟。第二种方法也是基于条件RF（即条件RF模型2），其中矩阵分解是在条件自相关矩阵上实现的。第三种方法源自 Sobol 的指数公式，它被扩展为能够使用相关的输入随机变量。当样本点分布稀疏或接近地层取向时，与其他条件方法相比，条件 RF 模型 1 产生的不确定性降低和可靠性指数 (β) 的幅度要小得多。此外，在这些情况下，通过条件 RF 模型 1 调节后的安全系数 (FS) 的标准偏差可能大于 FS 的无条件标准偏差。这在边坡可靠性评估中是出乎意料的。此外，条件RF仿真方法对反坡的β可能小于倾角的β，这与工程经验相悖，而使用无条件RF时不会发现这个问题。通过条件RF模型1调节后的安全系数（FS）的标准偏差可能大于FS的无条件标准偏差。这在边坡可靠性评估中是出乎意料的。此外，条件RF仿真方法对反坡的β可能小于倾角的β，这与工程经验相悖，而使用无条件RF时不会发现这个问题。通过条件RF模型1调节后的安全系数（FS）的标准偏差可能大于FS的无条件标准偏差。这在边坡可靠性评估中是出乎意料的。此外，条件RF仿真方法对反坡的β可能小于倾角的β，这与工程经验相悖，而使用无条件RF时不会发现这个问题。