Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Weiping Liu,Faming Huang,Xiaoyan Luo,Mingfu Fu

doi:10.3390/w9100758

Weiping Liu, Faming Huang + Show 2 more

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https://doi.org/10.3390/w9100758

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Journal: Water	Publication Date: Oct 9, 2017
Citations: 27	License type: CC BY 4.0

Affiliation: Nanchang University, Nanchang Institute of Technology

Abstract

It is important to determine the soil–water characteristic curve (SWCC) for analyzing slope seepage and stability under the conditions of rainfall. However, SWCCs exhibit high uncertainty because of complex influencing factors, which has not been previously considered in slope seepage and stability analysis under conditions of rainfall. This study aimed to evaluate the uncertainty of the SWCC and its effects on the seepage and stability analysis of an unsaturated soil slope under conditions of rainfall. The SWCC model parameters were treated as random variables. An uncertainty evaluation of the parameters was conducted based on the Bayesian approach and the Markov chain Monte Carlo (MCMC) method. Observed data from granite residual soil were used to test the uncertainty of the SWCC. Then, different confidence intervals for the model parameters of the SWCC were constructed. The slope seepage and stability analysis under conditions of rainfall with the SWCC of different confidence intervals was investigated using finite element software (SEEP/W and SLOPE/W). The results demonstrated that SWCC uncertainty had significant effects on slope seepage and stability. In general, the larger the percentile value, the greater the reduction of negative pore-water pressure in the soil layer and the lower the safety factor of the slope. Uncertainties in the model parameters of the SWCC can lead to obvious errors in predicted pore-water pressure profiles and the estimated safety factor of the slope under conditions of rainfall.

Highlights

The soil–water characteristic curve (SWCC) is one of the fundamental elements used to describe unsaturated soil
The SWCC is affected by the number of samples, the predicting model and the model parameters estimation method [10,11], and the SWCC model parameters are subject to high uncertainty [12,13]
This paper presented use of the Bayesian approach and Markov chain Monte Carlo (MCMC) method to evaluate the uncertainty of the SWCC

Summary

Introduction

The soil–water characteristic curve (SWCC) is one of the fundamental elements used to describe unsaturated soil. The SWCC describes a relationship between water content or degree of saturation and suction in the soil. It is influenced by many complex factors such as initial water content [4,5], particle size distribution [6,7], stress state [8], and temperature [9]. The SWCC is affected by the number of samples, the predicting model and the model parameters estimation method [10,11], and the SWCC model parameters are subject to high uncertainty [12,13].

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