scholarly journals Numerical Analysis of Slope Stability under Reservoir Water Level Fluctuations Using a FEM-LEM-Combined Method

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qingxiang Meng ◽  
Kun Qian ◽  
Lin Zhong ◽  
Jinjian Gu ◽  
Yue Li ◽  
...  
Keyword(s):  
Slope Stability ◽  
Water Level ◽  
Large Scale ◽  
Numerical Study ◽  
Soil Strength ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Impact

Large-scale slopes at the banks of reservoirs pose a serious threat to the safety of hydropower stations. The fluctuation of the reservoir water level is a key factor in the slope stability. However, the parameters to describe the relationship among water content, matric suction, and soil strength are difficult to measure using unsaturated soil strength theory. To solve this problem, a simple FEM-LEM-combined scheme considering pore pressure, seepage force, and strength weakening is presented to calculate the safety factor. A numerical study on the impact of reservoir water level fluctuations on stability of a glaciofluvial deposit slope is implemented. Two typical profiles are used to estimate the stability of the glaciofluvial deposit slope in response to rising and lowering water levels. The results indicate that this method proposed a simple and efficient tool for water level-induced slope stability analysis.

Deformation and stability analyses of a near-dam rocky slope and its potential landslide-generated wave threats

2021 ◽  
pp. qjegh2021-018
Author(s):  
Ming-liang Chen ◽  
Xing-guo Yang ◽  
Shun-chao Qi ◽  
Hai-bo Li ◽  
Jia-wen Zhou
Keyword(s):  
Water Level ◽  
Rock Slope ◽  
Limit Equilibrium ◽  
Water Levels ◽  
Reservoir Rock ◽  
Water Diversion ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Impact

Occurrence of a reservoir landslide and its potential secondary hazards near a dam can result in significant losses and casualties, such as those that resulted from the Vajont landslide. In this study, a cataclinal rock slope in the Maoergai reservoir was taken as a case to study the characteristics of the gravitational deformation process and to analyze the potential threat. The stability of rock slope is analyzed by the limit equilibrium method, and the potential landslide movement and subsequent waves are also simulated. Results indicate that lithology, geological structure, reservoir water level changes and artificial activities all play an important role for the large deformation of rock slope deformation, which is characterized by a combination of bending-toppling and principally shear-slip. Pre-calculations of potential threats indicated that the impact of a landslide wave would be greater at dead water levels than at the normal water level and could result in blockage of the inlet to the water diversion structure on the opposite right bank. These findings provide implication for the control of reservoir rock slopes: (i) serious attention should be paid to the influence of water on rock strength in early and (ii) infiltration must be prevented during water level rise.Thematic collection: This article is part of the Role of water in destabilizing slopes collection available at: https://www.lyellcollection.org/cc/Role-of-water-in-destabilizing-slopes

scholarly journals Sensitivity Analysis of Anchored Slopes under Water Level Fluctuations: A Case Study of Cangjiang Bridge—Yingpan Slope in China

2021 ◽  
Vol 11 (15) ◽  
pp. 7137
Author(s):  
Jinxi Liang ◽  
Wanghua Sui
Keyword(s):  
Sensitivity Analysis ◽  
Slope Stability ◽  
Water Level ◽  
Controlling Factors ◽  
Slope Instability ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Slope Inclination ◽  
Main Controlling Factors

This paper presents an improved slope stability sensitivity analysis (ISSSA) model that takes anchoring factors into consideration in umbrella-anchored sand and clay slopes under reservoir water level fluctuation. The results of the ISSSA model show that the slope inclination and the layout density of anchors are the main controlling factors for sand slope stability under fluctuation of the water level, while the slope inclination and water head height are the main controlling factors for slope stability in the Cangjiang bridge—Yingpan slope of Yunnan province in China. Moreover, there is an optimum anchorage angle, in the range of 25–45 degrees, which has the greatest influence on slope stability. The fluctuation of the reservoir water level is an important factor that triggers slope instability; in particular, a sudden drop in the surface water level can easily lead to landslides; therefore, corresponding measures should be implemented in a timely manner in order to mitigate landslide disasters.

scholarly journals Impact of Water Level Fluctuations on Landslide Deformation at Longyangxia Reservoir, Qinghai Province, China

2022 ◽  
Vol 14 (1) ◽  
pp. 212
Author(s):  
Shufen Zhao ◽  
Runqiang Zeng ◽  
Hongxue Zhang ◽  
Xingmin Meng ◽  
Zonglin Zhang ◽  
...  
Keyword(s):  
Water Level ◽  
Single Point ◽  
Water Levels ◽  
Monitoring Data ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Landslide Deformation ◽  
Level Monitoring ◽  
Water Level Monitoring

The construction of Longyangxia Reservoir has altered the hydrogeological conditions of its banks. Infiltration and erosion caused by the periodic rise and fall of the water level leads to collapse of the reservoir banks and local deformation of the landslide. Due to heterogeneous topographic characteristics across the region, water level also varies between different location. Previous research on the influence of fluctuations in reservoir water level on landslide deformation has focused on single-point monitoring of specific slopes, and single-point water level monitoring data have often been used instead of water level data for the entire reservoir region. In addition, integrated remote sensing methods have seldom been used for regional analysis. In this study, the freely-available Landsat8 OLI and Sentinel-2 data were used to extract the water level of Longyangxia Reservoir using the NDWI method, and Sentinel-1A data were used to obtain landslide deformation time series using SBAS-InSAR technology. Taking the Chana, Chaxi, and Mangla River Estuary landslides (each having different reservoir water level depths) as typical examples, the influence of changes in reservoir water level on the deformation of three wading landslides was analyzed. Our main conclusions are as follows: First, the change in water level is the primary external factor controlling the deformation velocity and trend of landslides in the Longyangxia Reservoir, with falling water levels having the greatest influence. Second, the displacement of the Longyangxia Reservoir landslides lags water level changes by 0 to 62 days. Finally, this study provides a new method applicable other areas without water level monitoring data.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

scholarly journals Stability Analysis of Hydrodynamic Pressure Landslides with Different Permeability Coefficients Affected by Reservoir Water Level Fluctuations and Rainstorms

Water ◽  
2017 ◽  
Vol 9 (7) ◽  
pp. 450 ◽  
Author(s):  
Faming Huang ◽  
Xiaoyan Luo ◽  
Weiping Liu
Keyword(s):  
Water Level ◽  
Permeability Coefficient ◽  
Hydrodynamic Pressure ◽  
Water Level Fluctuations ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Stability Coefficient ◽  
Permeability Coefficients ◽  
Landslide Stability ◽  
The Stability

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.

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