Abstract

The stability of slope is usually characterized by many parameters which are mostly uncertain in nature. Deterministic approach is usually followed to calculate the factor of safety of a slope, but it does not depict the true state of the slope. Hence, a probabilistic approach is a better alternative, which can quantify the probability of failure of a slope under uncertain input parameters. In the present study, slope stability assessment of Pakhi landside is carried out using finite element modelling (FEM) to ascertain the stability conditions of the multilayer slope under both deterministic and probabilistic framework. The multilayer configurations of the profiles are established from the electrical resistivity tomography (ERT). The shear strength reduction (SSR) method is employed to determine the critical strength reduction factor of the slope considering four random variables, namely, cohesion (c), angle of internal friction (ϕ), Poisson’s ratio (ν) and elastic modulus (E) of each individual layers. The deterministic factor of safety values along two considered profiles namely, section X–X′ and Y–Y′ are calculated as 1.41 and 1.25, respectively. A data driven machine learning algorithm is used to build a computationally efficient surrogate model to perform Monte Carlo Simulations (MCS). MCS are performed for two different values of coefficient of variation, i.e., 5% and 15% for all the four random variables of all the layers. The proposed method has no idealization regarding the layering configuration and the failure surface. Probabilistic analysis has been made exhaustive and computationally efficient. The probabilistic analysis indicates good adherence with the recent landslide incident in the field. Further, the analysis indicates that the proposed methodology is favourable and useful tool for the system reliability analysis of landslide slopes.

Research highlights

• Electrical resistivity tomography (ERT) interpretations could delineate sub-surface geometry of lithological layers of the landslide slope.

• Numerical simulation is performed using the finite element modelling.

• Sampling of random variables is done using the Latin Hypercube sampling technique.

• Probabilistic analysis is performed using the MARS-based surrogate model for Monte-Carlo simulation (MCS) with two values of CoV, viz., 5% and 10%.

• Efficiency of machine learning algorithm to incorporate with the stand alone FE code is demonstrated to build a surrogate model for an efficient probabilistic slope stability analysis.

中文翻译：

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Garhwal喜马拉雅山Pakhi滑坡概率边坡稳定性分析的数据驱动有效框架

摘要

边坡稳定性通常由许多参数表征，而这些参数大多是不确定的。通常采用确定性方法来计算边坡的安全系数，但它并不描绘边坡的真实状态。因此，概率方法是一种更好的替代方法，它可以量化不确定输入参数下斜坡失效的概率。在本研究中，Pakhi 陆侧边坡稳定性评估是使用有限元模型 (FEM) 进行的，以确定在确定性和概率框架下多层边坡的稳定性条件。轮廓的多层配置是根据电阻率断层扫描 (ERT) 建立的。c )、内摩擦角 ( ϕ )、泊松比 ( ν ) 和每个单独层的弹性模量 ( E )。沿两个考虑的剖面的安全值的确定性因素，即截面X–X ′ 和Y–Y' 分别计算为 1.41 和 1.25。数据驱动的机器学习算法用于构建计算效率高的替代模型，以执行蒙特卡罗模拟 (MCS)。MCS 是针对变异系数的两个不同值执行的，即所有层的所有四个随机变量的 5% 和 15%。所提出的方法在分层配置和失效表面方面没有理想化。概率分析已经变得详尽且计算效率高。概率分析表明现场最近发生的滑坡事件符合良好。此外，分析表明，所提出的方法是滑坡边坡系统可靠性分析的有利和有用的工具。

研究亮点

• 电阻率层析成像 (ERT) 解释可以描绘滑坡斜坡岩性层的地下几何形状。

• 使用有限元建模进行数值模拟。

• 随机变量的抽样是使用拉丁超立方抽样技术完成的。

• 使用基于 MARS 的蒙特卡罗模拟 (MCS) 代理模型进行概率分析，其中包含两个CoV值，即 5% 和 10%。

• 证明了机器学习算法与独立 FE 代码结合的效率，以构建用于有效概率边坡稳定性分析的替代模型。