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    基于分层贝叶斯学习的滨海软土地层高效识别方法

    曹子君 胡超 苗聪 王轩毫 郑硕

    曹子君, 胡超, 苗聪, 王轩毫, 郑硕, 2023. 基于分层贝叶斯学习的滨海软土地层高效识别方法. 地球科学, 48(5): 1730-1741. doi: 10.3799/dqkx.2022.503
    引用本文: 曹子君, 胡超, 苗聪, 王轩毫, 郑硕, 2023. 基于分层贝叶斯学习的滨海软土地层高效识别方法. 地球科学, 48(5): 1730-1741. doi: 10.3799/dqkx.2022.503
    Cao Zijun, Hu Chao, Miao Cong, Wang Xuanhao, Zheng Shuo, 2023. Efficient Identification Method of Coastal Soft Soil Stratum Based on Hierarchical Bayesian Learning. Earth Science, 48(5): 1730-1741. doi: 10.3799/dqkx.2022.503
    Citation: Cao Zijun, Hu Chao, Miao Cong, Wang Xuanhao, Zheng Shuo, 2023. Efficient Identification Method of Coastal Soft Soil Stratum Based on Hierarchical Bayesian Learning. Earth Science, 48(5): 1730-1741. doi: 10.3799/dqkx.2022.503

    基于分层贝叶斯学习的滨海软土地层高效识别方法

    doi: 10.3799/dqkx.2022.503
    基金项目: 

    国家自然科学基金项目 52278368

    国家自然科学基金项目 52025094

    详细信息
      作者简介:

      曹子君(1987—),男,教授,研究方向为岩土工程不确定性表征、可靠度设计与风险评估. ORCID:0000⁃0002⁃4712⁃1542. E⁃mail:zijuncao@whu.edu.cn

    • 中图分类号: P642

    Efficient Identification Method of Coastal Soft Soil Stratum Based on Hierarchical Bayesian Learning

    • 摘要: 基于静力触探试验数据划分土层依赖于经验图表和主观判断,划分的土层剖面不可避免地存在不确定性.提出了一种基于土体分类指数Ic的土层界面快速识别和不确定性量化方法.在分层贝叶斯学习框架下,所提方法采用全高斯概率模型表征土体空间变异性,通过引入正态‒逆威沙特共轭分布实现似然函数的快速计算,高效求解模型证据,识别最可能土层数目和厚度.所提方法基于Ic的统计特性自动划分土层,提高了识别结果的可靠性.

       

    • 图  1  土层划分示意及全高斯随机场模型

      Fig.  1.  Illustration of full Gaussian random field modelling of underground stratigraphy

      图  2  所提土层划分方法对应的图模型

      Fig.  2.  Graphical model for the proposed soil stratification method

      图  3  温州仙湖试验区静力触探试验数据

      Fig.  3.  A set of CPT data in Wenzhou Xianhu test area

      图  4  不同土层数目对应的模型证据对数值

      Fig.  4.  Logarithm of the evidence for different model classes with different numbers of soil layers

      图  5  土层划分结果及界面深度标准差

      Fig.  5.  Underground stratigraphy identified from proposed approach and its uncertainty

      图  6  模拟场地土层剖面与模拟数据

      Fig.  6.  Virtual site and simulated data

      图  7  不同土层数目对应的模型证据对数值

      Fig.  7.  Logarithm of the evidence of different model classes with different numbers of soil layers

      图  8  模拟数据的土层划分结果

      Fig.  8.  Soil stratification results based on simulated data

      图  9  不同$ \alpha $对应的土层数目识别精度

      Fig.  9.  Accuracy of soil layer number using different $ \alpha $ values

      表  1  基于土类指数Ic的土体分类(刘松玉等,2013

      Table  1.   Soil classification based on the soil behavior type index $ {I}_{c} $ (Liu et al., 2013)

      土类指数$ {I}_{c} $范围 土体类型 分区
      < 1.87 中砂 7
      1.87~2.10 细砂 6
      2.10~2.32 粉砂 5
      2.32~2.65 粉土 4
      2.65~2.90 粉质黏土 3
      2.9~3.45且$ {Q}_{tn} > 11.8\mathrm{e}\mathrm{x}\mathrm{p}(-{F}_{r}/1.15)-0.36 $ 黏土 2
      > 3.45或$ {Q}_{tn} < 11.8\mathrm{e}\mathrm{x}\mathrm{p}\left(-{F}_{r}/1.15\right)-0.36 $ 淤泥或淤泥质土 1
      下载: 导出CSV
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    出版历程
    • 收稿日期:  2022-10-30
    • 网络出版日期:  2023-06-06
    • 刊出日期:  2023-05-25

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