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.
Underground stratification based on the cone penetration test(CPT)data relies on empirical charts and subjective judgments.Uncertainties in soil profile identified from CPT data are unavoidable.In this paper it proposes a fast underground stratigraphy identification method based on soil classification index Ic,which also quantifies the identification uncertainty.Under the hierarchical Bayesian learning framework,the proposed method uses full Gaussian random field model to characterize the spatial variability of soils,and efficiently calculates the likelihood function of soil layer thicknesses given a soil layer number by introducing the Normal-Inverse Wishart conjugate distribution.By this means,the model evidence can be solved efficiently,and the most possible profile is identified.The proposed method automatically identifies the most probable soil layer profile considering the statistical characteristics of Ic,and improves the reliability of the identification results.
作者
曹子君
胡超
苗聪
王轩毫
郑硕
Cao Zijun;Hu Chao;Miao Cong;Wang Xuanhao;Zheng Shuo(State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China;Power China Huadong Engineering Co.Ltd.,Hangzhou 311122,China)
出处
《地球科学》
EI
CAS
CSCD
北大核心
2023年第5期1730-1741,共12页
Earth Science
基金
国家自然科学基金项目(Nos.52278368,52025094).
关键词
静力触探试验
分层贝叶斯学习
土层划分
不确定性
岩土工程.
cone penetration test
hierarchical Bayesian learning
underground stratification
uncertainty
geotechnical engineering.
