scholarly journals Evaluation of Deformation and Permeability Behavior of Amygdaloidal Basalt under a Triaxial Cyclic Loading Creep Test

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Long Yan ◽  
Weiya Xu ◽  
Biao Li ◽  
Hua Ji ◽  
Jinjian Gu
Keyword(s):  
Cyclic Loading ◽  
Creep Test ◽  
Creep Deformation ◽  
Axial Stress ◽  
Axial Direction ◽  
Axial Deformation ◽  
Reservoir Water ◽  
Lateral Direction ◽  
Reservoir Water Level ◽  
Dam Foundation

Amygdaloidal basalt is a typical rock mass in the dam foundation of the Baihetan hydropower project in southwest China. With rising and drawdown of the reservoir water level, the permeability and creep deformation characteristics of the amygdaloidal basalt are much complicated in the long-term cyclic loading processes. A cyclic loading-unloading creep test on the amygdaloidal basalt was performed to evaluate its deformation and permeability behavior. The results showed that Poisson’s ratio and elastic modulus of the rock specimen varied significantly under different loading processes with a relatively large irreversible deformation. The permeability and strain rates of rock changed in two phases under lower deviatoric stresses, while there are three typical stages of strain growth with the final stress level of 121.8 MPa. For axial stress of 128 MPa, the creep deformation and creep rate in the axial direction are smaller than these in the lateral direction. Before the sample failure, the lateral deformation accelerates earlier than the axial deformation. The results also suggested that the permeability of the rock specimens decreases considerably during each loading process and then tends to be constant with time. No apparent change in steady permeability is observed with variation of stress. For 128 MPa axial stress, the permeability first decreases, then tends to be in a stable value, and at last increases during the sample failure.

A Method for Estimating Creep Deformation of Structures Subjected to Cyclic Loading

1973 ◽  
Vol 40 (4) ◽  
pp. 928-934 ◽  
Author(s):  
J. J. Williams ◽  
F. A. Leckie
Keyword(s):  
Cyclic Loading ◽  
Creep Strain ◽  
Effective Stress ◽  
Creep Test ◽  
Creep Deformation ◽  
Cyclic Creep ◽  
Uniaxial Stress ◽  
Time Varying ◽  
Proportional Loading

A method is proposed for estimating structural creep deformation due to histories of cyclic proportional loading. The method applies to structures composed of materials whose creep strain due to constant uniaxial stress is given by an equation of the form ε(t)/ε0={σ/σ0}n{t/t0}m Knowledge of the form of the creep law for time-varying stress is not required, as use is made of an effective stress obtained from a single cyclic creep test.

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 Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

2021 ◽  
Vol 13 (2) ◽  
pp. 224
Author(s):  
Xin Liang ◽  
Lei Gui ◽  
Wei Wang ◽  
Juan Du ◽  
Fei Ma ◽  
...  
Keyword(s):  
Time Variable ◽  
Development Pattern ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Colluvial Landslide

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.

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.

Analysis of creep deformation under cyclic loading conditions

1975 ◽  
Vol 20 ◽  
pp. 261-266 ◽  
Author(s):  
D.K. Shetty ◽  
T. Mura ◽  
M. Meshii
Keyword(s):  
Cyclic Loading ◽  
Creep Deformation ◽  
Loading Conditions
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