Polynomial chaos expansions (PCEs) have been extensively used to perform reliability analyses of slopes. The accuracy of a PCE metamodel is highly dependent on the experimental design samples, which are commonly selected according to their uniformity. However, the method of uniform sampling fails to put additional weight on the regions with high nonlinearity, in which more samples are required to give a good approximation. To address this issue, the Voronoi-based exploration and the local linear approximation-based exploitation (Voronoi-LOLA) are combined to determine experimental design samples for PCE constructions. A sequential sampling scheme that employs the sparse polynomial chaos expansion (SPCE) output information is further proposed to choose the most informative samples, which are crucial for building a PCE. This method not only improves computational efficiency but also enhances the accuracy of the PCE metamodel. The performance of the proposed Voronoi-LOLA-SPCE method is illustrated with four representative examples, and the results show that the proposed Voronoi-LOLA-SPCE is an effective and accurate method for slope reliability assessment.

多项式混沌扩展（PCE）已被广泛用于进行边坡的可靠性分析。PCE元模型的准确性高度依赖于实验设计样本，这些样本通常是根据其均匀性来选择的。但是，均匀采样的方法无法在非线性度较高的区域上施加额外的权重，在该区域中，需要更多的采样才能给出良好的近似值。为了解决这个问题，将基于Voronoi的勘探和基于局部线性近似的勘探（Voronoi-LOLA）结合起来，以确定PCE施工的实验设计样本。进一步提出了采用稀疏多项式混沌扩展（SPCE）输出信息的顺序采样方案，以选择信息量最大的样本，这对于构建PCE至关重要。该方法不仅提高了计算效率，而且提高了PCE元模型的准确性。通过四个有代表性的例子说明了所提出的Voronoi-LOLA-SPCE方法的性能，结果表明，所提出的Voronoi-LOLA-SPCE方法是一种有效，准确的边坡可靠性评估方法。