# Random Finite Element Analysis for the Reliability of Loess Slopes(PDF)

## 《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

- Issue:
- 2019年第01期

- Page:
- 116-126

- Research Field:
- 工程地质

- Publishing date:

## Info

- Title:
- Random Finite Element Analysis for the Reliability of Loess Slopes

- Author(s):
- LI Ping; BAI Jian-zhong; GRIFFITHS D V; LI Tong-lu; ZHENG Ya-nan
- 1. School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, Shaanxi, China; 2. Department of Civil and Environmental Engineering, Colorado School of Mines, Golden 80401, Colorado, USA; 3. Xi’an Branch of Tianshui Kimcheon Mining Co., Ltd., Xi’an 710065, Shaanxi, China

- Keywords:
- loess slope; random field; finite element; cohesion; internal friction angle; autocorrelation distance; reliability; stability factor

- PACS:
- P642.13+1;TU43

- DOI:
- -

- Abstract:
- In order to study the reliability of loess slopes, the variability and probability distribution characteristics of loess strength parameters were estimated with 4 018 groups of loess direct shear data, and the autocorrelation distances were analyzed with two cone penetration test(CPT) data. The cohesion and internal friction angle were taken as the random variables, which form random fields on a two-dimensional slope section. The factor of safety was calculated by the elastic-plastic finite element method based on the mean strength parameters. The probabilistic analyses used a mean and standard deviation distribution in the finite element mesh through the method of local average subdivision. The failure probability was calculated by the Monte-Carlo method. The data show that on the loess plateau of China, the coefficient of variation of the cohesion is usually more than 0.30, and that of the friction angle is less than 0.20. The cohesion and friction angle are assumed to be lognormally and normally distributed, respectively, and the autocorrelation distance is limited to 0.2 of the slope height. In a single random variable approach (autocorrelation distance is equal to infinity), the probability of failure is overestimated for low coefficients of variation, and underestimated for high coefficients of variation.

Last Update: 2019-01-25