scholarly journals Analysis of Earth Dam Body Behavior under Rapid Drawdown Conditions in Reservoir Water Level by Laboratory and Numerical Methods

2021 ◽  
Vol 25 (3) ◽  
Keyword(s):  
Numerical Methods ◽  
Water Level ◽  
Earth Dam ◽  
Reservoir Water ◽  
Reservoir Water Level

scholarly journals Experimental and numerical study of upstream slope stability in an earth dam reservoir under rapid drawdown conditions

2020 ◽  
Vol 8 (1) ◽  
pp. 1-15
Author(s):  
Seyed Habib Mousavi Jahromi ◽  
Mansour Pakmanesh ◽  
Amir Khosrojerdi ◽  
Hossein Hassanpour Darvishi ◽  
Hossein Babazadeh
Keyword(s):  
Water Level ◽  
Pore Water Pressure ◽  
Numerical Study ◽  
Water Pressure ◽  
Earth Dam ◽  
Dam Reservoir ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Seepage Analysis ◽  
Upstream Slope

The rapid ‎drawdown of the dam reservoir is one of the most common situations occurring in the lifetime of a dam. For this reason, one of the main factors in the design of the upstream slope is the rapid drainage of the reservoir. In this case, the upstream slope is in a critical condition and the slope may be unstable. When the water surface in the reservoir is drawdown suddenly, the water level in the dam body does not decrease at the same time as the reservoir water level. The analysis of seepage from the earth dam body and calculation of the water loss play an important role in calculating the amount of pore water pressure, and, consequently, the stability analysis of the dam body. In addition, any seepage analysis is dependent on the hydraulic properties of the dam materials. In order to investigate the effect of hydraulic conductivity on the rapid drawdown of water level and the seepage, an experimental model was constructed of an earth dam. By accurate measurement of hydraulic parameters of the materials in saturated and unsaturated media, the flow through this model was modeled using a disk penetrometer by seep/w software. The results were then compared with the observed data.

scholarly journals Effect of hydraulic conductivity of unsaturated soil on the earth dam performance

2018 ◽  
Vol 162 ◽  
pp. 01008
Author(s):  
Mohammed Fattah ◽  
Mahmood Ahmed ◽  
Nawar Ali
Keyword(s):  
Hydraulic Conductivity ◽  
Water Level ◽  
Water Levels ◽  
Degree Of Saturation ◽  
Earth Dam ◽  
Core Material ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Partially Saturated ◽  
The Core

In this paper, the finite element method is uzed to solve the governing equations of flow through earth dams. The computer program Geo-Slope is used in the analysis through its sub-program named SEEP/W. A case study is considered to be Al-Adhaim dam which consists of zoned embankment with a total length of 3.1 km. The dam in its actual design is analyzed. Then, an attempt is made to study the seepage in unsaturated zone of the dam through studying the effect of several parameters including the effect of changing the unsaturated hydraulic conductivity with the degree of saturation of the core soil and changing of water level in the reservoir. A procedure is proposed to define the hydraulic conductivity function from the soil water characteristic curve which is measured by the filter paper method. Fitting methods are applied through the program SoilVision. A parametric study was carried out and different parameters were changed to study their effects on the behavior of partially saturated soil. The study reveals that the rate of flow is decreased by about 20 - 27% when the degree of saturation of the core material is decreased from 100% to 50% at water level 115.75 m, while the exit gradient of flow is decreased by about 13 -15%. This decrease in flow rate becomes 13-15% and 8-9.5% when the reservoir water level is 131.5 m and 143.5 m, respectively, while the exit gradient of flow is increased by about 23-29.5% and 29-29.5% when the reservoir water level is 131.5 m and 143.5 m, respectively. When the state of soil changes from fully saturated S= 100% to partially saturated S= 90%, a rapid increase in head gradient and pore water takes place at the embankment base for different water levels in the reservoir. This decrease plateaus out on further decrease in the degree of saturation.

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.

Seasonal succession and long-term stability of pelagic community in a productive reservoir

2005 ◽  
Vol 56 (8) ◽  
pp. 1137 ◽  
Author(s):  
V. F. Matveev ◽  
L. K. Matveeva
Keyword(s):  
Time Series ◽  
Water Level ◽  
Seasonal Succession ◽  
Perca Fluviatilis ◽  
Taxonomic Composition ◽  
Stationary Time Series ◽  
Introduced Fish ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Murray River

In Lake Hume, a reservoir located in an active agricultural zone of the Murray River catchment, Australia, time series for the abundances of phytoplankton and zooplankton taxa, monitored from 1991 through to 1996, were stationary (without trends), and plankton taxonomic composition did not change. This indicated ecosystem resilience to strong fluctuations in reservoir water level, and to other potential agricultural impacts, for example eutrophication and pollution. Although biological stressors such as introduced fish and invertebrate predators are known to affect planktonic communities and reduce biodiversity in lakes, high densities of planktivorous stages of alien European perch (Perca fluviatilis) and the presence of carp (Cyprinus carpio) did not translate into non-stationary time series or declining trends for plankton in Lake Hume. However, the seasonal successions observed in the reservoir in different years did not conform well to the Plankton Ecology Group (PEG) model. Significant deviations of the Lake Hume successional pattern from the PEG model included maxima for phytoplankton abundance being in winter and the presence of a clear water phase without large zooplankton grazers. The instability of the water level in Lake Hume probably causes the dynamics of most planktonic populations to be less predictable, but did not initiate the declining trends that have been observed in some other Australian reservoirs. Both the PEG model and the present study suggest that hydrology is one of the major drivers of seasonal succession.

Dam Safety Reliability Analysis Based on Artificial Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 3620-3625
Author(s):  
Hai Wei ◽  
Hua Shu Yang ◽  
Liang Wu ◽  
Yue Gui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reliability Index ◽  
Safety Evaluation ◽  
Mathematic Model ◽  
Dam Safety ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

There are many factors, such as climate, flood, material, geology, structure, management, to influence dam safety. So dam safety evaluation, involving many fields, is very complicated, and very difficult to establish mathematic model for assessment. Artificial Neural Network (ANN) has many obvious advantages to deal with these problems influenced by multi-factor, consequently is widely used in engineering fields. This paper considered water level, temperature, main factors influencing dam deformation, as random variables, employed ANN and statistical model to establish performance function of dam hidden trouble deformation and abnormal deformation. Then reliability theory was used to analyze dam safety reliability and sensitivity. The results show that temperature has great effect on probability of dam hidden trouble deformation and abnormal deformation than reservoir water level, due to great variability of temperature. Change of Reliability index of dam is contrary to reservoir water level. Temperature, especially average temperature in 10 days and 5 days, has great effect on sensitivity of reliability index than water level.

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