期刊
ENGINEERING GEOLOGY
卷 265, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.enggeo.2019.105430
关键词
Bayesian methods; Site characterization; Spatial variability; Sparse data
资金
- Research Grants Council of the Hong Kong Special Administrative Region, China
- CityU [11213117, T22-603/15N]
Identification of subsurface stratification and characterization of spatially varying soil properties profiles in multiple soil layers are indispensable in geotechnical site investigation. Subsurface soils are often stratified first through visual inspection of soil samples obtained from boreholes or soil behavior type index from cone penetration tests. Then, the soil property profile within each layer is characterized via interpolation of the data points measured within the corresponding layer. Although this stratification first procedure is commonly used in engineering practice, it is difficult to apply when measured data points within each layer are sparse and limited (e.g., only a few data points within each layer), a scenario often encountered in geotechnical site characterization. When the number of measurements within each layer is small, it is difficult to properly interpolate spatially varying soil property profiles in each layer. To address this difficulty and increase the number of data points for enabling proper interpolation, an interpolation first procedure is proposed which utilizes measurements from all layers as input for interpolation, followed by soil stratification using the interpolation results. The proposed procedure includes two key elements: (1) a Bayesian supervised learning method for interpolation of non-stationary data from multiple layers, and (2) an unsupervised machine learning method (e.g., clustering) for soil stratification. An index is also proposed to determine when the proposed method is beneficial and performs better than the stratification first procedure in engineering geology practice. Both numerical and real-life data are used to illustrate the proposed method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据