Geologic material properties of hillslopes are inherently heterogeneous, with complex layering structures due to geological deposition processes. Lacking detailed sampling of the properties’ spatial distribution has led to the stochastic representation of the properties to address uncertainty in the hillslope stability evaluation. This study treats the spatial distributions of the shear strength parameters, the cohesion (c), and the internal friction angle (φ), in a synthetic two-dimensional slope as stochastic random fields characterized by their means, variances, and correlation scales. This study then evaluates the cross-correlation between these parameters and the factor of safety (FS) using unconditional Monte Carlo simulation (MCS). Different from classical sensitivity analyses, the cross-correlation analysis of FS and the stochastic parameter fields stresses the importance of the orientation of the large-scale geological layering, the correlation between the geological media’s cohesion, and the internal friction angle at the local scale on the probability and uncertainty of failure of the heterogeneous hillslope. The analysis further guides the field sampling strategy to reduce uncertainty in the slope stability analysis due to unknown heterogeneity. More importantly, it suggests the location of stability reinforcement measures. The results of this study provide cost-effective tools for geoengineers to deal with field slope stability analysis under uncertainty.

The Fangshan landslide was a rainfall-induced landslide that occurred in a volcanic area in the Fangshan scenic spot, Nanjing, Jiangsu, China. On 25 October 2016, after approximately 10 days of continuous rainfall, a shallow landslide rapidly developed, which triggered slow movement of deep mudstone rock. According to the characteristics of the landslide body, measures such as anti-slide piles, anchor cables and drainage were used to reinforce the landslide. Active drainage measures included arranging plant growth zones at the trailing edge of the landslide, and passive drainage measures included arranging pumping wells at the trailing edge of the landslide. It is worth emphasizing that the Fangshan landslide was the first example of a landslide in Jiangsu Province, China that was treated by actively lowering the water pressure. After landslide treatment from 16 May 2017 to 21 January 2018, the Fangshan landslide tended to be stable. However, the stable landslide was reactivated by the rise in groundwater level caused by rainfall and pumping well damage and underwent accelerated downward sliding in July 2020. The Fangshan landslide has caused great damage to the roads and buildings of Fangshan scenic spot, with a direct loss of RMB 6 million and an indirect loss of RMB 95 million. This article discusses the development process of the shallow soil landslide and the underlying deep mudstone rock landslide. The influence of groundwater level variation on the deformation of the shallow soil landslide and deep mudstone rock landslide of the Fangshan landslide are also discussed.

Due to large-scale geological deposition processes, slope structures are often stratified, which means that the spatial distribution of the parameters involved in slope reliability evaluation is statistically anisotropic. This paper studies the effect of the statistical anisotropy of undrained shear strength on the probability of slope failure (p_f) based on the Monte Carlo simulation. The results show that for the horizontally layered slope, the larger the horizontal correlation scale of undrained shear strength (λ_x) is, the larger p_f is, especially when λ_x is smaller than the slope length; for the vertically layered slope, the larger the vertical correlation scale (λ_y) is, the smaller p_f is, especially when λ_y is smaller than the slope height. Additionally, the mechanism of the above results is discussed by analyzing the displacement distribution at different correlation scales. The findings indicate that in the reliability evaluation of undrained slopes in stratified structures, either underestimation of λ_x or overestimation of λ_y leads to an unconservative estimate of p_f, resulting in an overestimation of the slope stability.