融合多源信息的降雨入渗边坡概率反分析及可靠度预测

P642.22; 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.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...

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Published in中国地质灾害与防治学报 Vol. 35; no. 1; pp. 28 - 36
Main Authors 揭鸿鹄, 蒋水华, 常志璐, 黄劲松, 黄发明
Format Journal Article
LanguageChinese
Published 南昌大学工程建设学院,江西南昌 330031%南昌大学工程建设学院,江西南昌 330031 2024
南昌大学资源与环境学院,江西南昌 330031
Subjects
probabilistic inverse-analysis
Bayesian updating
reliability analysis
概率反分析
spatial variability
空间变异性
降雨诱发滑坡
rainfall-induced landslide
可靠度分析
贝叶斯更新
Online AccessGet full text
ISSN1003-8035
DOI10.16031/j.cnki.issn.1003-8035.202309029

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Abstract P642.22; 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.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;用更新后的土体参数预测得到2004年9月12日该边坡在暴雨工况下的失效概率为23.1％,符合实际边坡失稳情况,说明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.
AbstractList P642.22; 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.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;用更新后的土体参数预测得到2004年9月12日该边坡在暴雨工况下的失效概率为23.1％,符合实际边坡失稳情况,说明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.
Abstract_FL Probabilistic inverse-analysis is an essential approach to infer statistical characteristics of uncertain soil parameters,making the slope reliability assessment closer to engineering reality.However,current probabilistic inverse analysis rarely integrates multi-source information,including monitored data,field observation information,and slope survival records.Conducting the probabilistic inverse-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating the multi-source information is a challenging issue due to the involvement of thousands of random variables and the evaluation of high-dimensional likelihood functions.In this paper,a modified Bayesian updating with subset simulation(mBUS)method is combined with adaptive conditional sampling(aCS)algorithm to establish a framework for probabilistic inverse analysis of spatially variable soil parameters and reliability prediction of slopes.The effectiveness of this framework is validated using a highway slope as a case study.The research results show that the posterior statistical characteristics of soil parameters obtained by integrating multi-source information are in good agreement with field observation results.Additionally,the probability of slope failure under heavy rainfall on September 12,2004 with the updated soil parameters is 23.1％,which is in line with the actual slope instability.Within this framework,multi-source information can be fully utilized to address high-dimensional probabilistic inverse analysis problems.
Author 揭鸿鹄
蒋水华
常志璐
黄劲松
黄发明
AuthorAffiliation 南昌大学工程建设学院,江西南昌 330031%南昌大学工程建设学院,江西南昌 330031;南昌大学资源与环境学院,江西南昌 330031
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Author_FL CHANG Zhilu
JIE Honghu
HUANG Faming
HUANG Jinsong
JIANG Shuihua
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Keywords probabilistic inverse-analysis
Bayesian updating
reliability analysis
概率反分析
spatial variability
空间变异性
降雨诱发滑坡
rainfall-induced landslide
可靠度分析
贝叶斯更新
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PublicationTitle 中国地质灾害与防治学报
PublicationTitle_FL The Chinese Journal of Geological Hazard and Control
PublicationYear 2024
Publisher 南昌大学工程建设学院,江西南昌 330031%南昌大学工程建设学院,江西南昌 330031
南昌大学资源与环境学院,江西南昌 330031
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Title 融合多源信息的降雨入渗边坡概率反分析及可靠度预测
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