Reservoir water level effects on nonlinear dynamic response of arch dams

2008 ◽  
Vol 24 (3) ◽  
pp. 418-435 ◽  
Author(s):  
M. Akkose ◽  
A. Bayraktar ◽  
A.A. Dumanoglu
Keyword(s):  
Dynamic Response ◽  
Water Level ◽  
Nonlinear Dynamic ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Nonlinear Dynamic Response ◽  
Arch Dams

scholarly journals Static analysis of a high arch dam

2019 ◽  
pp. 45-57
Author(s):  
Yaser Ghafoori ◽  
Andrej Kryžanowski ◽  
Dejan Zupan
Keyword(s):  
Water Level ◽  
Static Analysis ◽  
Three Dimensional ◽  
Water Reservoir ◽  
Arch Dam ◽  
Three Dimensional Model ◽  
High Arch Dam ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Arch Dams

The paper presents the design and static analysis of a high arch dam. A feasibility study was conducted on the dam in the 90s and a preliminary layout was designed. However, the dam’s construction phase has been never started. In this paper, the design and layout of the dam under consideration are in accordance with the US manuals for the design of arch dams. The structure’s three-dimensional model was entered into the program SAP2000 and three-dimensional solid finite elements were used to discretize the model. This paper considers the hydrostatic pressure of the water reservoir and concrete self-weight. The analysis was performed for both the maximum and the minimum designed water level and for the case when the reservoir is empty. Special attention is given to the boundary conditions of the dam at its abutments and foundation. The results show that the planned layout is good for the dam’s construction. The arch dam’s curve transfers the loads to the abutments. The significant role of the foundation rigidity and the reservoir water level in the stress distribution and nodal displacements within the arch dam is observed.

Analysis of Dynamic Response of Landslide Stability to Reservoir Water Level Fluctuation Using Numerical Simulation

2012 ◽  
Vol 594-597 ◽  
pp. 407-414
Author(s):  
Wu Yi ◽  
Zhao Ping Meng ◽  
Guo Qing Li ◽  
Zhi Wei Jin
Keyword(s):  
Dynamic Response ◽  
Water Level ◽  
Water Level Fluctuation ◽  
Level Fluctuation ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Landslide Stability ◽  
Reservoir Drawdown ◽  
The Stability ◽  
Reservoir Water Level Fluctuation

Reservoir water level is one important factor influencing the stability of landslides. The dynamic response of landslide stability under reservoir water level function and its features are analyzed using theoretical and numerical methods. The results show that, in terms of reservoir water level fluctuation and landslide permeability, the seepage filed of landslide can be divided into four types: lag behind impoundment(X-Ⅰ), lag behind drawdown(T-Ⅰ), synchronization with impoundment(X-Ⅱ) and synchronization with drawdown (T-Ⅱ). Under lag behind drawdown, at a certain rate of reservoir drawdown, the stability drops with the permeability of landslide. Under lag behind impoundment, with the rise of water level, the lower the permeability of landslide is, the more stable the landslide is. Under synchronization with impoundment or drawdown, the stability of landslide drops with reservoir impoundment and rises with reservoir drawdown.

scholarly journals The Monitoring-Based Analysis on Deformation-Controlling Factors and Slope Stability of Reservoir Landslide: Hongyanzi Landslide in the Southwest of China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Han ◽  
Bin Tong ◽  
Jinkai Yan ◽  
Chunrong Yin ◽  
Liang Chen ◽  
...  
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Monitoring Program ◽  
Significant Risk ◽  
Controlling Factors ◽  
Seepage Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
Reservoir Landslide

Reservoir landslide is a type of commonly seen geological hazards in reservoir area and could potentially cause significant risk to the routine operation of reservoir and hydropower station. It has been accepted that reservoir landslides are mainly induced by periodic variations of reservoir water level during the impoundment and drawdown process. In this study, to better understand the deformation characters and controlling factors of the reservoir landslide, a multiparameter-based monitoring program was conducted on a reservoir landslide—the Hongyanzi landslide located in Pubugou reservoir area in the southwest of China. The results indicated that significant deformation occurred to the landslide during the drawdown period; otherwise, the landslide remained stable. The major reason of reservoir landslide deformation is the generation of seepage water pressure caused by the rapidly growing water level difference inside and outside of the slope. The influences of precipitation and earthquake on the slope deformation of the Hongyanzi landslide were insignificant.

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.

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