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蒋水华, 朱明明, 曾绍慧, 黄劲松, 杨志刚, 周创兵. 基于贝叶斯更新方法的尾矿坝材料参数随机反演[J]. 岩土工程学报, 2020, 42(S2): 77-82. DOI: 10.11779/CJGE2020S2014
引用本文: 蒋水华, 朱明明, 曾绍慧, 黄劲松, 杨志刚, 周创兵. 基于贝叶斯更新方法的尾矿坝材料参数随机反演[J]. 岩土工程学报, 2020, 42(S2): 77-82. DOI: 10.11779/CJGE2020S2014
JIANG Shui-hua, ZHU Ming-ming, ZENG Shao-hui, HUANG Jin-song, YANG Zhi-gang, ZHOU Chuang-bing. Stochastic back analysis of material parameters of tailings dams using Bayesian updating approach[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(S2): 77-82. DOI: 10.11779/CJGE2020S2014
Citation: JIANG Shui-hua, ZHU Ming-ming, ZENG Shao-hui, HUANG Jin-song, YANG Zhi-gang, ZHOU Chuang-bing. Stochastic back analysis of material parameters of tailings dams using Bayesian updating approach[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(S2): 77-82. DOI: 10.11779/CJGE2020S2014

基于贝叶斯更新方法的尾矿坝材料参数随机反演

Stochastic back analysis of material parameters of tailings dams using Bayesian updating approach

  • 摘要: 为准确获得有限数据条件下的尾矿坝材料参数取值,在贝叶斯更新理论和有限元分析框架下,提出了考虑不确定性的尾矿坝材料参数随机反演方法。为提高参数随机反演分析的计算效率,利用随机多项式展开建立尾矿坝代表性监测点位移与输入参数之间的隐式函数关系。以某实际尾矿坝为例,基于有限的位移监测数据进行多层尾矿材料参数随机反演分析,说明了提出方法的有效性。结果表明,提出方法可以有效缩减尾矿坝材料参数的不确定性,准确推断尾矿坝材料参数的概率分布,并识别不同材料参数(弹性模量和泊松比等)对尾矿坝变形的影响程度。

     

    Abstract: To obtain accurate values of the material parameters of tailings dams based on the limited data, a stochastic back analysis approach considering the uncertainties of the material parameters of tailings dams is proposed under the framework of Bayesian updating and finite element analysis. To improve the computational efficiency of back analysis, a polynomial chaos expansion is adopted to replace the implicit function between the displacements of tailing dams at the representative monitoring points and uncertain input parameters. A real tailings dam is taken as an example to demonstrate the effectiveness of the proposed approach for stochastic back analysis of parameters of multi-layered tailings materials based on the monitoring data of displacements. The results indicate that the proposed approach can effectively reduce the estimation in the uncertainties of the material parameters of tailings dams, accurately infer the probability distributions of the material parameters, and identify the influence degree of different material parameters (e.g., elastic modulus, Poisson’s ratio) on the deformation of the tailings dams.

     

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