Slope stability analysis in the Three Gorges Reservoir Area considering effect of antecedent rainfall

Tang, Dong; Li, Dianqing; Cao, Zi-Jun

doi:10.1080/17499518.2016.1193205

Cited by 22 publications

(12 citation statements)

References 17 publications

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“…Similarly, each year even in normal conditions, rain-water and melt-water have been losing. Moreover, antecedent rainfall influences slope stability, which also causes landslides [35]. Therefore, building a multi-purpose dam is needed to store water.…”

Section: Rainfall and Discharge Datamentioning

confidence: 99%

Identification of Potential Sites for a Multi-Purpose Dam Using a Dam Suitability Stream Model

Shao

Jahangir

Yasir

et al. 2020

Water

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Optimal site selection of a dam is one of the crucial tasks in water resource management. In this study, a dam suitability stream model (DSSM) is utilized to identify potential sites for constructing multi-purpose dams. In DSSM, each input parameter is weighted using the analytic hierarchy process (AHP), and then weighted overlay analysis is performed in a Geographical Information System (GIS) environment. Compared to the previous studies, this study showed different results based on the crucial parameter that is “stream order”. Two resultant site suitability maps are prepared to differentiate the importance of stream order. Each of the resulting maps visualizes four classes of suitability from highly suitable to least suitable. The proposed sites will store water for a variety of uses at the local and regional level and reduce flood risk, which can be very useful for hydrologists and disaster risk managers.

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Section: Rainfall and Discharge Datamentioning

confidence: 99%

Identification of Potential Sites for a Multi-Purpose Dam Using a Dam Suitability Stream Model

Shao

Jahangir

Yasir

et al. 2020

Water

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“…In addition, some researchers have used autoregressive-moving-average (ARMA) time series analysis and Pearson product-moment correlation coefficients (PPCC) to analyze the lagged correlation between the monthly displacement increments and their influencing factors (Cai et al, 2016;Cao et al, 2016;Zhou et al, 2016;Li et al, 2018). Moreover, based on the daily rainfall data for the TGRA, the finite element method (FEM) and discrete element method (DEM) also have been used to simulate the effect of influencing factors on landslide stability and have verified the correlation between influencing factors and landslide displacement (Kawamoto, 2005;Lollino et al, 2010;Tang et al, 2017). Data-mining technique and clustering method have been applied to interpret landslide monitoring data and to select the influencing factors of the landslide deformation (Shiuan, 2012;Hong et al, 2016;Ma et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Influencing Factors and the Monthly Displacement Increment of a Hydrodynamic Landslide Using a Pseudo-Maximum-Likelihood-Estimation-Mixed-Copula Approach

Wang

Zhang

et al. 2021

Front. Earth Sci.

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Heavy rainfall and changes in the water levels of reservoirs directly affect the degree of landslide disasters in major hydropower project reservoir areas. Correlation analyses of rainfall- and water-level fluctuations with landslide displacement changes can provide a scientific basis for the prevention and early warning of landslide disasters in reservoir areas. Because of the shortcomings of the traditional correlation analysis based on linear assumptions, this study proposed the use of a pseudo-maximum-likelihood-estimation-mixed-Copula (MLE-M-Copula) method instead of linear assumptions. We used the Bazimen landslide in the Three Gorges Reservoir Area as a case study to carry out the correlation analysis of the rainfall, water-level fluctuations, and landslide displacement. First, we selected several appropriate influencing factors to construct four candidate Copula models and estimated the parameters using the pseudo-MLE method. After the goodness-of-fit test, we selected the M-Copula model as the optimal model and used this model to study correlations between the monthly displacement increment of the landslide and influencing factors. We then established the joint distribution functions of these correlations. We computed and analyzed the overall and tail correlations between the displacement increment and the influencing factors, and we constructed the conditional probability distribution of the monthly displacement increment for different given conditions. The results showed that the pseudo-MLE-M-Copula method effectively quantified the correlation between the rainfall, reservoir-level fluctuations, and landslide displacement changes, and we obtained the return periods and value at risk of the influencing factors of the Bazimen landslide under different rainfall conditions and reservoir-level changes. Furthermore, the tail correlations between the monthly displacement increment of the landslide and the rainfall- and reservoir-level changes were higher than the overall correlations.

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“…Rainfall-induced landslides have caused many severe geological disasters globally [6][7][8]. Previous studies have found that the internal soil factors of the slope, including complex factors such as the permeability coefficient, soil strength characteristics, stress distribution, and soil deformation, are affected by rainfall infiltration [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Influence of Uncertainty of Soil Hydraulic Parameters on Stability of Unsaturated Slopes Based on Bayesian Updating

et al. 2021

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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.

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Slope stability analysis in the Three Gorges Reservoir Area considering effect of antecedent rainfall

Cited by 22 publications

References 17 publications

Identification of Potential Sites for a Multi-Purpose Dam Using a Dam Suitability Stream Model

Identification of Potential Sites for a Multi-Purpose Dam Using a Dam Suitability Stream Model

Correlation Analysis of Influencing Factors and the Monthly Displacement Increment of a Hydrodynamic Landslide Using a Pseudo-Maximum-Likelihood-Estimation-Mixed-Copula Approach

Influence of Uncertainty of Soil Hydraulic Parameters on Stability of Unsaturated Slopes Based on Bayesian Updating

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