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Stochastic seepage and slope stability analysis using vine-copula based multivariate random field approach: Consideration to non-Gaussian spatial and cross-dependence structure of hydraulic parameters

机译：基于Vine-Copula的多变量随机场方法的随机渗流和斜坡稳定性分析：考虑到液压参数的非高斯空间和交叉依赖性结构

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摘要

Stochastic seepage and slope stability analysis is conventionally performed in a random field framework. However, most of the studies are limited to Gaussian spatial and cross-dependence structure of hydraulic parameters. Using a well documented hydraulic conductivity (k) data from Borden aquifer, Canada (Sudicky, 1986), evidence of non-Gaussian spatial dependence is provided within a copula framework. It is shown that the non-Gaussian spatial dependence structure is a field reality for k. To handle the non-Gaussian spatial as well as cross-dependence structure, a multivariate random field framework based on vine copula theory is presented. It is shown that the vine-copula approach can efficiently model the non-Gaussian dependence structure of hydraulic parameters. For investigating the practical engineering importance of dependence structure, stochastic seepage and slope stability analysis under steady and transient seepage conditions is conducted. It is shown that the assumption of arbitrary spatial dependence structure, can significantly affect (by a factor of 100) the failure probability of slopes across the entire acceptable range (Salgado and Kim, 2014) of 10(-4) to 10(-2). It is also shown that the choice of spatial dependence structure is more crucial than the cross-dependence for stochastic seepage and slope stability analysis.
机译：随机渗流和斜率稳定性分析通常在随机场框架中进行。然而，大多数研究仅限于液压参数的高斯空间和交叉依赖性结构。使用来自加拿大Borden Aquifer的良好的液压导电性（K）数据（Sudicky，1986），在Copula框架内提供了非高斯空间依赖的证据。结果表明，非高斯空间依赖结构是k的场现实。为了处理非高斯空间以及交叉依赖性结构，提出了一种基于藤蔓编程理论的多变量随机场框架。结果表明，葡萄拷贝的方法可以有效地模拟液压参数的非高斯依赖结构。为了调查依赖结构的实际工程重要性，进行稳定和瞬态渗流条件下的随机渗流和边坡稳定性分析。结果表明，任意空间依赖结构的假设可以显着影响（通过100倍），整个可接受范围（Salgado和Kim，2014）为10（-4）至10（-2）的斜坡上的故障概率）。还表明空间依赖结构的选择比随机渗流和斜坡稳定性分析的交叉依赖性更为重要。

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