Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65290
Title: Conditional random field reliability analysis of a cohesion-frictional slope
Authors: Liu, LL
Cheng, YM 
Zhang, SH
Keywords: Conditional random field
Probability of failure
Reliability analysis
Spatial variability
Subset simulation
Issue Date: 2017
Publisher: Elsevier
Source: Computers and geotechnics, 2017, v. 82, p. 173-186 How to cite?
Journal: Computers and geotechnics 
Abstract: 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.
URI: http://hdl.handle.net/10397/65290
ISSN: 0266-352X
DOI: 10.1016/j.compgeo.2016.10.014
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