Conditional random field reliability analysis of a cohesion-frictional slope

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• 作者：Lei-Lei Liu^a ; Yung-Ming Cheng^a ; ^{ceymchen@polyu.edu.hk} ; Shao-He Zhang^b ; ^c
• 关键词：Reliability analysis ; Probability of failure ; Spatial variability ; Conditional random field ; Subset simulation
• 刊名：Computers and Geotechnics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：82
• 期：Complete
• 页码：173-186
• 全文大小：3316 K
• 卷排序：82

文摘

Discarding known data from cored samples in the reliability analysis of a slope in spatially variable soils is a waste of site investigation effort. The traditional unconditional random field simulation, which neglects these known data, may overestimate the simulation variance of the underlying random fields of the soil properties. This paper attempts to evaluate the reliability of a slope in spatially variable soils while considering the known data at particular locations. Conditional random fields are simulated based on the Kriging method and the Cholesky decomposition technique to match the known data at measured locations. Subset simulation (SS) is then performed to calculate the probability of slope failure. A hypothetical homogeneous cohesion-frictional slope is taken as an example to investigate its reliability conditioned on several virtual samples. Various parametric studies are performed to explore the effect of different layouts of the virtual samples on the factor of safety (FS), the spatial variation of the critical slip surface and the probability of slope failure. The results suggest that whether the conditional random fields can be accurately simulated depends highly on the ratio of the sample distance and the autocorrelation distance. Better simulation results are obtained with smaller ratios. Additionally, compared with unconditional random field simulations, conditional random field simulations can significantly reduce the simulation variance, which leads to a narrower variation range of the FS and its location and a much lower probability of failure. The results also highlight the great significance of the conditional random field simulation at relatively large autocorrelation distances.