首页|暴雨及水位骤降条件下渗流参数空间变异的水库滑坡概率分析

暴雨及水位骤降条件下渗流参数空间变异的水库滑坡概率分析

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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.
Probabilitic Analysis of Reservoir Landslides Considering the Spatial Variation of Seepage Parameters under the Conditions of Rainstorm and Sudden Drop of Water Level
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.

landslideshazardslandslide probabilityspatial variabilityhydraulic conductivityback-propagation neural networkrainstormsudden drop of water level

蒋水华、熊威、朱光源、黄卓涛、林列、黄发明

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江西省水利科学院江西省水工安全工程技术研究中心,江西南昌 330029

南昌大学工程建设学院,江西南昌 330031

南昌大学设计研究院,江西南昌 330029

滑坡 灾害 滑坡概率 空间变异性 渗透系数 反向传播神经网络 暴雨 水位骤降

江西省水利科学院开放研究基金国家自然科学基金国家自然科学基金江西省自然科学基金江西省自然科学基金

2021SKSG02522229055217910320232ACB20403120224ACB204019

2024

10.3799/dqkx.2022.361

地球科学
中国地质大学

地球科学

CSTPCD北大核心
影响因子：1.447
ISSN：1000-2383
年,卷(期)：2024.49(5)
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