Conditional random field reliability analysis of a cohesion-frictional slope
Conditional random field reliability analysis of a cohesion-frictional slope
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DOI：
10.1016/j.compgeo.2016.10.014
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发表时间：
2017-02
影响因子：
5.3
通讯作者：
Leilei Liu;Y. Cheng;Shaohe Zhang
中科院分区：
文献类型：
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作者：
Leilei Liu;Y. Cheng;Shaohe Zhang
在空间变化的土壤中进行边坡可靠性分析时，丢弃取芯样品中的已知数据是对现场调查工作的浪费。传统的无条件随机场模拟忽略了这些已知的数据，可能会高估潜在的土性随机场的模拟方差。本文试图在考虑特定位置的已知数据的情况下，评估空间可变土壤中边坡的可靠性。条件随机场是基于克里格法和Cholesky分解技术模拟的，以匹配测量位置处的已知数据。然后进行子集模拟（SS）来计算边坡破坏的概率。以一个假定的均质粘滞-摩擦边坡为例，研究了其在多个虚拟样本条件下的可靠度。不同的参数进行研究，探讨不同的虚拟样本的布局上的安全系数（FS），临界滑动面的空间变化和边坡破坏的概率的影响。结果表明，条件随机场能否被准确模拟在很大程度上取决于样本距离与自相关距离的比值。在较小的比值下得到了更好的仿真结果。此外，与无条件随机场模拟相比，条件随机场模拟可以显著减小模拟方差，从而使FS及其位置的变化范围更窄，失效概率更低。结果还突出了条件随机场模拟在相对较大的自相关距离的重要意义。
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.