Probabilitic Analysis of Reservoir Landslides Considering the Spatial Variation of Seepage Parameters under the Conditions of Rainstorm and Sudden Drop of Water Level
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摘要: 传统滑坡概率分析没有合理考虑滑坡体渗透系数空间变异性的影响.为有效表征滑坡体渗透系数空间变异性对滑坡概率的影响，提出基于反向传播神经网络（BPNN）的参数空间变异性边坡可靠度分析方法，其中采用Karhunen-Loève级数展开方法离散滑坡体饱和渗透系数非高斯随机场，基于BPNN构建边坡稳定系数代理模型.以白水河滑坡为例，分别进行暴雨和库水位骤降条件下滑坡概率分析，并与其他方法对比验证了提出方法的有效性.结果表明：提出方法不仅可有效考虑渗透系数空间变异性对滑坡概率的影响，而且具有较高的计算效率，可为实际复杂水库滑坡概率计算提供一种有效的工具.考虑滑坡体渗透系数空间变异性的作用，白水河滑坡在连续5 d暴雨作用下有19.5%的可能性发生局部失稳破坏，而在水位骤降条件下发生局部失稳破坏的可能性很小.Abstract: Traditional probabilitic analyses of landslides do not take into account the influence of the spatial variability of hydraulic conductivity of landslide mass. To characterize the effect of the spatial variability of the hydraulic conductivity of landslide mass, this paper proposes a back-propagation neural network-based method for slope reliability analysis involving spatially variable soil parameters. The Karhunen-Loève series expansion method is used to discretize the non-Gaussian random field of the saturated hydraulic conductivity of landslide mass. The back-propagation neural network is adopted to construct the surrogate model of the factor of safety of a spatially variable slope. The Baishuihe landslide is investigated as an example to estimate the landslide probability caused by the rainstorm and sudden drop of reservoir water level, respectively. The effectiveness of the proposed method is demonstrated through comparisons with other methods. The results indicate that the proposed method can not only effectively account for the influence of the spatial variability of the hydraulic conductivity of landslide mass on the landslide probability, but also achieve high computational efficiency for the probabilitic analysis of reservoir landslides. It can provide an effective and versatile tool for the landslide probability evaluation. In addition, when the spatial variability of soil hydraulic conductivity is considered, the Baishuihe landslide has a 19.5% probability of local failure under five consecutive days of rainstorm, while it has quite small occurrence possibility of local failure under the sudden drop of reservoir water level.
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图 5 滑坡体体积含水量和渗透系数函数
Fig. 5. Volumetric water content and hydraulic conductivity functions for the landslide mass
图 6 白水河滑坡有限元渗流分析模型
Fig. 6. Finite element seepage analysis model of Baishuihe landslide
图 7 第5 d边坡渗流稳定计算结果
Fig. 7. Slope seepage and stability analysis results on the fifth day
图 9 白水河滑坡渗透系数随机场的一次典型实现
Fig. 9. One typical realization of the hydraulic conductivity random field of Baishuihe landslide
图 10 模型测试均方误差随训练样本数目的变化
Fig. 10. Variations of the mean square error of model testing with the number of training samples
图 13 滑坡概率随LHS模拟次数的变化关系曲线
Fig. 13. Variations of the landslide probability with the number of LHS simulations
图 14 白水河滑坡有限元渗流分析模型
Fig. 14. Finite element seepage analysis model of Baishuihe landslide
图 15 第50 d边坡渗流稳定计算结果
Fig. 15. Slope seepage and stability analysis results on the 50th day
图 17 白水河滑坡渗透系数随机场的一次典型实现
Fig. 17. One typical realization of the hydraulic conductivity random field of Baishuihe landslide
图 18 模型测试均方误差随训练样本数目的变化
Fig. 18. Variations of the mean square error of model testing with the number of training samples
图 20 滑坡概率随LHS模拟次数的变化
Fig. 20. Variations of the landslide probability with the number of LHS simulations
表 1 边坡岩土体参数取值
Table 1. Values of slope soil parameters
区域 重度
γ（kN/m3）黏聚力
c（kPa）内摩擦角
φ（°）饱和渗透系数
ks（m/d）滑坡体 20.7 27 18 0.43 基岩 25.6 190 30.4 0.01 
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表 2 滑坡体Van Genuchten模型拟合参数取值
Table 2. Values of van Genuchten model fitting parameters for the landslide mass
拟合参数 a（kPa） n $ {\theta }_{s} $ $ {\theta }_{r} $ 取值 50 1.45 0.43 0.02
下载: 导出CSV
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