Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-07 DOI:10.1007/s00477-024-02789-x

Lei-Lei Liu, Yue-Bing Xu, Wen-Qing Zhu, Khan Zallah, Lei Huang, Can Wang

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Abstract

Slope reliability is of great importance in geotechnical engineering, and it is susceptible to various factors, such as slope cutting and rainfall. Currently, how the copula dependence structure affects the reliability of cutting slopes under rainfall conditions is still an open question. This study investigates the influence of copula dependence structure on the reliability analysis of a real slope, considering the slope cutting and rainfall characteristics (i.e., rainfall intensity, duration, and pattern). The Gaussian, Plackett, Frank, and No.16 copulas are first employed to model the joint probability distribution of the measured soil strength parameters. The optimal copula is subsequently identified using Akaike information criterion and Bayesian information criterion. The probability of failure (P_f) and the distribution of critical slip surface (CSS) for different slope cutting and rainfall conditions are then obtained within the framework of Monte Carlo simulation. The results show that the copula dependence between shear strengths has significant influence on the P_f for the cutting slope under rainfall conditions. The commonly used Gaussian copula may underestimate the P_f, while the No.16 copula would overestimate the P_f for different slope cutting angles and rainfall intensities, durations and patterns. The differences in P_f obtained by different copula functions decrease with the increase of cutting angle, cutting distance and rainfall intensity. Furthermore, the differences in P_f obtained by different copula functions show little variations with changes in rainfall duration and pattern. Although the copula function has a significant influence on the P_f, it has negligible influence on CSS. This study provides a practical tool for the selection of copula function and valuable insights for slope design and management under slope cutting and rainfall conditions.

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降雨条件下切削斜坡的可靠性分析（考虑剪切强度之间的共轭相关性

边坡可靠性在岩土工程中非常重要，它容易受到边坡切割和降雨等各种因素的影响。目前，copula 依存结构如何影响降雨条件下切削边坡的可靠性仍是一个悬而未决的问题。本研究考虑了边坡切割和降雨特征（即降雨强度、持续时间和模式），研究了 copula 依赖结构对实际边坡可靠性分析的影响。首先采用高斯、Plackett、Frank 和 No.16 协方差对测量的土壤强度参数的联合概率分布进行建模。随后利用 Akaike 信息准则和贝叶斯信息准则确定了最优共线。然后，在蒙特卡洛模拟的框架内得到了不同切坡和降雨条件下的破坏概率（Pf）和临界滑移面（CSS）的分布。结果表明，剪切强度之间的协整关系对降雨条件下切削边坡的 Pf 有显著影响。常用的高斯共线可能会低估 Pf，而 No.16 共线则会高估不同切坡角度和降雨强度、持续时间及模式下的 Pf。随着切削角、切削距离和降雨强度的增加，不同共线函数得到的 Pf 差异也会减小。此外，随着降雨持续时间和降雨模式的变化，不同协整函数得到的 Pf 差异也很小。虽然 copula 函数对 Pf 有显著影响，但对 CSS 的影响却微乎其微。这项研究为选择 copula 函数提供了实用工具，并为边坡切割和降雨条件下的边坡设计和管理提供了宝贵的启示。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.