Abstract
In geotechnical engineering, the soil water retention curve (SWRC) is key to solving problems arising from unsaturated soil, and the methodology used to obtain the SWRC parameters is crucial for investigating rainfall infiltration and slope stability. However, on-site measurements of soil data are expensive and time-consuming, and therefore, there is high uncertainty in the SWRC parameters due to the limited amount of data available. This study explores the impact of uncertainty in SWRC parameters on unsaturated soil slope seepage and stability under rainfall conditions. Bayesian updating was initially used to update the posterior distribution of the SWRC parameters of the model and in situ soil. Subsequently, a Markov Chain Monte Carlo (MCMC) method was used to generate random samples, and the uncertainty of the parameters was analyzed. Additionally, SWRC parametric models with different confidence intervals were created, and a hydraulic coupled model was used to evaluate the influence of the SWRC parameters (with different confidence intervals) on slope seepage and stability under rainfall conditions. The results indicated that the parameters α and n affecting the air entry value of the soil and the pore size distribution, respectively, increased as the confidence interval percentile increased. The changes in these two parameters increased the effect of rainfall on the pressure head and volumetric water content of the soil. After rainfall infiltrated the slope, the soil volumetric water content and the internal suction stress of the soil increased, resulting in a reduction in the local factor of safety (LFS) and, hence, a decrease in the stability of the slope. These results show that the predictions for the pressure head and volumetric water content were affected by the uncertainty in the SWRC parameters, leading to errors in the slope stability analysis.
Highlights
The soil water retention curve (SWRC) is used to describe the relationship between matric suction and volumetric water content or the degree of saturation of unsaturated soil
The SWRC is affected by the number of samples, the predictive model, and the estimation method used for the model parameters, leading to high uncertainty in the SWRC model parameters [4, 5]
The Bayesian updating method and the Markov Chain Monte Carlo (MCMC) method were applied to evaluate the uncertainty of the SWRC model parameters and quantify the effect of parameter uncertainty on the seepage and stability of unsaturated soil slopes under rainfall conditions
Summary
The soil water retention curve (SWRC) is used to describe the relationship between matric suction and volumetric water content or the degree of saturation of unsaturated soil. The matric suction changes with the volumetric water content of the soil and affects the stability of the slope [1,2,3]. A small amount of data obtained from in situ soil cannot be used to represent the matric suction and degree of saturation for the entire SWRC. The accurate estimation of soil hydraulic parameters is critical for evaluating slope stability using a hydraulic coupled model [13]. Only a limited amount of data is available for parametric fitting and analysis owing to complex geological characteristics, limited soil samples, and measurement errors [14, 15].
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