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基于MCMC法的混凝土坝坝体坝基变形模量随机反演
程井1, 李培聪1, 李同春1, 袁平2
1.河海大学水利水电学院;2.中冶长天国际工程有限责任公司
摘要:
依据工程设计资料及监测数据建立大坝的位移统计模型及有限元分析模型;将坝体及坝基变形模量参数视为随机变量,基于Bayesian理论,利用无似然函数的马尔科夫链蒙特卡罗方法(Markov chain Monte Carlo without likelihoods)进行随机参数后验分布抽样,通过平稳后的马尔可夫链得到参数后验分布的随机样本,进而得到对应的期望值和标准差。以龙滩高混凝土重力坝为例,针对典型断面的二维平面模型,采用无似然函数的MCMC算法对坝体、坝基变形模量进行了随机反演,并研究了坝体、坝基变形模量分布的统计特性与观测值波动之间的关系。
关键词:  重力坝  变形模量  MCMC法  随机反演
DOI:
分类号:TV642.2
基金项目:
Stochastic inversion for deformation modulus ofa concrete dam based on
Cheng Jing1, Li Peicong1, Li Tongchun1, Yuan Ping2
1.College of Water Conservancy and Hydropower Engineering,Hohai University;2.Zhongye Changtian International Engineering Co,Ltd
Abstract:
According to engineering design and monitoring data, the statistic model of displacement and the model for finite element method analysis of dam are established. The deformation modulus of dam body and dam foundation are considered as random variables. Based on Bayesian theory, using the Markov Chain Monte Carlo without likelihoods, samples of the posterior distribution of random parameters are obtained by the stable Markov chain. The corresponding expectation and standard deviation are obtained. Then the Longtan Gravity Dam is studied as a demonstration. The deformation modulus of the dam is inversed using Markov chain Monte Carlo without likelihood method with a two-dimensional plane model. In the end, the relationship between the statistical characteristics of deformation modulus distribution of dam body and dam foundation and the fluctuation of observation value has been further studied.
Key words:  gravity dam  deformation modulus  Markov chain Monte Carlo method  stochastic inversion
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