期刊
COMPUTERS AND GEOTECHNICS
卷 95, 期 -, 页码 147-161出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compgeo.2017.09.019
关键词
Data assimilation; Ensemble Kalman filter; Finite elements; Random fields; Slope reliability; Spatial variability
类别
资金
- Marie Curie Career Integration Grant [333177]
- Geo-Engineering Section of Delft University of Technology
- China Scholarship Council (CSC)
A data assimilation framework, utilising measurements of pore water pressure to sequentially improve the estimation of soil hydraulic parameters and, in turn, the prediction of slope stability, is proposed. Its effectiveness is demonstrated for an idealised numerical example involving the spatial variability of saturated hydraulic conductivity,. It is shown that the estimation of generally improves with more measurement points. The degree of spatial correlation of influences the improvement,in the predicted performance, as does the selection of initial input statistics. However, the results are robust with respect to moderate uncertainty in the spatial and point statistics.
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