Abstract:It is significant to study the variations in the stability coefficients of hydrodynamic pressure landslides with different permeability coefficients affected by reservoir water level fluctuations and rainstorms. The Sifangbei landslide in Three Gorges Reservoir area is used as case study. Its stability coefficients are simulated based on saturated-unsaturated seepage theory and finite element analysis. The operating conditions of stability coefficients calculation are reservoir water level variations between 175 m and 145 m, different rates of reservoir water level fluctuations, and a three-day continuous rainstorm. Results show that the stability coefficient of the hydrodynamic pressure landslide decreases with the drawdown of the reservoir water level, and a rapid drawdown rate leads to a small stability coefficient when the permeability coefficient ranges from 1.16 × 10 −6 m/s to 4.64 × 10 −5 m/s. Additionally, the landslide stability coefficient increases as the reservoir water level increases, and a rapid increase in the water level leads to a high stability coefficient when the permeability coefficient ranges from 1.16 × 10 −6 m/s to 4.64 × 10 −5 m/s. The landslide stability coefficient initially decreases and then increases as the reservoir water level declines when the permeability coefficient is greater than 4.64 × 10 −5 m/s. Moreover, for structures with the same landslide, the landslide stability coefficient is most sensitive to the change in the rate of reservoir water level drawdown when the permeability coefficient increases from 1.16 × 10 −6 m/s to 1.16 × 10 −4 m/s. Additionally, the rate of decrease in the stability coefficient increases as the permeability coefficient increases. Finally, the three-day rainstorm leads to a significant reduction in landslide stability, and the rate of decrease in the stability coefficient initially increases and then decreases as the permeability coefficient increases.

We propose prior distributions for all parts of the specification of a Markov mesh model. In the formulation we define priors for the sequential neighborhood, for the parametric form of the conditional distributions and for the parameter values. By simulating from the resulting posterior distribution when conditioning on an observed scene, we thereby obtain an automatic model selection procedure for Markov mesh models. To sample from such a posterior distribution, we construct a reversible jump Markov chain Monte Carlo algorithm (RJMCMC). We demonstrate the usefulness of our prior formulation and the limitations of our RJMCMC algorithm in two examples.

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.

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