ISSN 1003-8035 CN 11-2852/P
    揭鸿鹄,蒋水华,常志璐,等. 融合多源信息和改进贝叶斯与自适应条件抽样耦合算法的降雨入渗边坡概率反分析及可靠度预测[J]. 中国地质灾害与防治学报,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202309029
    引用本文: 揭鸿鹄,蒋水华,常志璐,等. 融合多源信息和改进贝叶斯与自适应条件抽样耦合算法的降雨入渗边坡概率反分析及可靠度预测[J]. 中国地质灾害与防治学报,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202309029
    JIE Honghu,JIANG Shuihua,CHANG Zhilu,et al. Probabilistic back-analysis of rainfall-induced landslides with multi-source information and coupling algorithms of modified Bayesian and adaptive conditional sampling[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202309029
    Citation: JIE Honghu,JIANG Shuihua,CHANG Zhilu,et al. Probabilistic back-analysis of rainfall-induced landslides with multi-source information and coupling algorithms of modified Bayesian and adaptive conditional sampling[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202309029

    融合多源信息和改进贝叶斯与自适应条件抽样耦合算法的降雨入渗边坡概率反分析及可靠度预测

    Probabilistic back-analysis of rainfall-induced landslides with multi-source information and coupling algorithms of modified Bayesian and adaptive conditional sampling

    • 摘要: 概率反分析是推断不确定土体参数统计特征的重要手段,可以使边坡可靠度评估更接近工程实际。然而目前的概率反分析很少使用多源信息(包括监测数据、观测信息和边坡服役记录)。因为这通常涉及数千个随机变量和高维似然函数的评估。因此融合多源信息对空间变异土体参数进行概率反分析进而预测降雨条件下的边坡可靠度是一项具有挑战性的难题。文章将改进的基于子集模拟的贝叶斯更新(mBUS)方法与自适应条件抽样(aCS)算法相结合,构建了空间变异土体参数概率反分析和边坡可靠度预测的框架,并以某一公路边坡为例验证了该框架的有效性。在此框架下,可以充分利用多源信息解决高维概率反分析问题。研究结果表明:通过融合多源信息所获得的土体参数后验统计特征与现场观测结果基本吻合。此外,更新后的土体参数可用于预测2004年9月12日该边坡附近区域暴雨工况下的边坡失效概率。

       

      Abstract: Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters, making the slope reliability assessment closer to the engineering reality. However, multi-source information (including monitored data, field observations and slope survival records) are rarely used in current probabilistic back-analysis. Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating the multi-source information is a challenging issue since thousands of random variables and high-dimensional likelihood function are usually involved. In this paper, a framework by integrating a modified Bayesian updating with subset simulation (mBUS) method with adaptive conditional sampling (aCS) algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction. A highway slope case is investigated to illustrate the effectiveness of the established framework. Within this framework, the high-dimensional probabilistic back-analysis problem can be easily tackled, and the multi-source information can be fully used for the back-analysis. The results show that the obtained posterior knowledge of soil parameters is in good agreement with the field observations. Furthermore, the updated statistics of soil parameters can be utilized to predict the failure probability of a slope caused by the heavy rainfall event on September 12, 2004 in the district near the studied slope.

       

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