Reservoir Landslide Research Articles

Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

AbstractReservoir landslides are typical geohazards with sophisticated thermo‐hydro‐mechanical interactions. However, direct observations of subsurface multi‐physical processes in bank slopes remain rare. Herein we present the design, implementation and evaluation of an innovative fiber‐optic nerve system based on weak‐reflection fiber Bragg grating to monitor a giant landslide located in the Three Gorges Reservoir region, China. The system is capable of measuring and imaging spatiotemporal distributions of temperature, moisture and strain along boreholes in near real‐time. Such visual profiles offer unique insights into the subsurface thermo‐hydro‐mechanical link between external conditions and geotechnical deformation, making it possible to decipher the substantial trigger of accelerated movements. Multi‐physical responses at depths of interest also enable us to better understand the evolution mechanism of reservoir landslides. Fiber‐optic nerve sensing can synergize with remote sensing and surface‐based technologies to build a space‐sky‐ground‐subsurface integrated monitoring system for landslides.

Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China

Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.

Data mining technology for the identification and threshold of governing factors of landslide in the Three Gorges Reservoir area

Due to the complex geological conditions and external triggering factors, the deformation of landslide disaster often has spatial differences, especially in the Three Gorges Reservoir area. It is of great significance to explore the governing factors and their thresholds in different parts of landslide. In this research, the deformation of Shuping landslide is analyzed. First, 9 hydrological factors are selected for data mining analysis by comprehensive research, including 5 reservoir water factors and 4 rainfall factors. Then, the numerical variables are transformed into discrete variables by two-step clustering method, and the Apriori algorithm is utilized to deal with the classified variables to generate the correlation criterion meeting the minimum confidence, and the correlation criterions between triggering factors and landslide displacement are established. Finally, the threshold of governing factors are mined out by decision tree C5.0 models. The results indicate that governing factors controlling the deformation of different parts of landslide are distinct. Specifically, the rear landslide is jointly controlled by the reservoir water and rainfall. On the contrary, the reservoir level controls the deformation of other parts of Shuping landslide. Generally, the daily drop of water level is the most important factor causing the deformation of Shuping landslide. During the period of low water level (138.951 ~ 147.437 m), once the daily drop of water level exceeded 0.416 m/d, the landslide will show severe deformation. This research reveals that the study of association criterion and threshold is of great significance for landslide deformation analysis. Data mining technology can be better applied to the prediction of the reservoir landslide.

Characterizing the Fundamental Controls on Deformation and Stability of an Active Reservoir Landslide, Southwest China

Characterizing the Fundamental Controls on Deformation and Stability of an Active Reservoir Landslide, Southwest China

Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter

Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter

Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area, China

The Three Gorges Hydropower Station is the largest hydropower station worldwide with the impoundment of the 660-km long reservoir. More than 500 landslides have been triggered by the reservoir water level fluctuation since the first impoundment in 2003. The classification of the reservoir affected landslide (seepage-driven and buoyancy-driven landslides) is crucial for landslide early warning and risk management. There are still no classification criteria for the reservoir landslide in TGRA. In this study, based on the long term in-situ monitoring, numerical simulation and field investigation methods, two typical reservoir landslide of Tangjiao landslide (seepage-driven) and Tanjiahe landslide (buoyancy-driven) in TGRA were taken as study cases. The comparative analysis of the response relationship between the long term deformation and the influencing factors were carried out. It can be found that the intensive deformation of Tangjiao landslide occurred during the rapid drawdown period of the reservoir water level, while Tanjiahe landslide has been deforming in the whole water year. Moreover, by analyzing the cumulative displacement curve, the permeability of the sliding mass, and the sliding surface of six reservoir landslides in TGRA, the classification criteria for seepage-driven and buoyancy-driven landslides in TGRA were proposed.

Features of Landslide in Dam Reservoir

Features of Landslide in Dam Reservoir

A comparative study of different machine learning methods for reservoir landslide displacement prediction

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability.

Field Characterization of Landslide-Induced Surge Waves Based on Computational Fluid Dynamics

Numerical simulation analysis of landslide-induced surge wave field characteristics offers great insights for engineering practices and is crucial for studying reservoir landslide disaster warning, prevention, and control. Based on the computational fluid dynamics, a numerical calculation model of typical landslide surge was established, and the surge waves caused by the movement of a rigid landslide body along the slope and its propagation process was to simulated and analyzed. The results were compared with classical physical experiments to validate the applicability of the numerical calculation model. Furthermore, effects of the shape and density of landslide body on the surge wave generation, water entry characteristics of landslide body, propagation pattern of surge waves were also studied. It was showed that the starting position of far-field landslide surge negatively correlated with the landslide body density. The shape of landslide body did not substantially impact the far-field starting position, which was mainly influenced by the area of water surface in contact with the sliding body.

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

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.

An Analysis of Slope Stability Based on Finite Element Method and Distinct Element Method

The analysis of slope stability involves complex geological and topographical boundary conditions, nonlinear behavior of material stress-strain, coupling analysis of initial in-situ stress, water pressure and seismic load, etc., and in most cases, analytical solutions cannot be obtained. Under the background of the continuous development of computer and calculation method, the numerical analysis method represented by finite element has been gradually popularized and applied in geotechnical engineering in 1970s, and has developed into a powerful calculation and analysis tool. Among them, the finite element strength reduction method and the discrete element method are the two most widely used slope numerical analysis methods. In this paper, two typical cases, Ankang reservoir landslide and Wenma Highway slope, are simulated by the two methods.Taking Ankang reservoir landslide as the research object, this paper would use MIDAS / GTS finite element analysis software, and two-dimensional finite element numerical simulation would be carried out to study the influence of reservoir water level periodic fluctuation on the reinforcement effect of anti-slide pile. Under the condition of water saturation and water loss cycle, main material of landslide body and landslide belt, namely the strong weathered phyllite, displays obvious deterioration phenomenon, showing the trend of rapid decline first and then slow decline; after the anti-slide pile is set in the middle and front of the slope, the stability of it has been greatly improved, but with the increasement of the number of water level changes, the reinforcement effect of the anti-slide pile continues to weaken, and the weakening speed is fast at first, and then slows down.Taking the bedding slope of Wenma Highway as the research object, this pater would adopt UDEC discrete element software to simulate the deformation and failure process of the slope after excavated, and analysize the failure mechanism at the same time. The failure process of bedding slope can be divided into four stages: the formation of tension cracks caused by excavation, the expansion of cracks and the formation of deformation body, the sliding of deformation body and the accumulation of damaged rock mass at the foot of slope. Tensile failure is the main failure mode, and shear failure occurs locally. The failure of bedding slope starts from the foot of slope, which is traction sliding.

Failure characteristics of the reservoir landslide and surge calculations

Failure characteristics of the reservoir landslide and surge calculations

Application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction

The slope displacement prediction is crucial for the development of an early warning system, which can help prevent or reduce losses of lives, properties, and the local environment. This problem is particularly important in the Three Gorges Reservoir (TGR) area, where the influence of geological, weather, and hydraulic conditions on landslides is significant. It is generally acknowledged that reservoir landslides are complex nonlinear systems with dynamic and inter-related features. However, most studies focus on how to express the static relationships between triggering factors and the landside displacement. In this paper, a long short-term memory (LSTM) neural network model was applied for predicting the total displacement of the Bazimen landslide, based on the decomposition of displacement time series. The accumulated displacement can be divided into two main parts: the trend and the periodic terms. The long-term trend was fit with a cubic nonlinear regression model; the residual one (the periodic displacement) was predicted via the LSTM model. By analyzing historical information and the Pearson correlation coefficient, a dynamic model was developed using six controlling factors. The good consistency between the predicted and monitored data proves the superiority of the model in predicting dynamic time-series problems. Compared with conventional static methods (i.e., MARS and SVM), the LSTM model can make full use of historical information due to its special “memory” structure. However, all these three methods can only perform well in forecasting one-step problems. To meet the requirement of multi-step forecasting, the Facebook Prophet model was also used in this study to predict landslide displacements with a longer period. The predicted results demonstrate the model’s superiority in efficiency and practice, at a cost of prediction accuracy.

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

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 analyse the potential threat. The stability of the rock slope was analysed using the limit equilibrium method, and the potential landslide movement and subsequent waves were simulated. Results indicated that lithology, geological structure, reservoir water-level changes and artificial activities all play an important role in large deformations of rock slopes that are 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 the blockage of the inlet to the water-diversion structure on the opposite right bank. These findings provide implications for the control of reservoir rock slopes: (i) serious attention should be paid to the influence of water on rock strength in the early investigation of geological disasters; 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

The triggering threshold estimation of Majiagou landslide based on data mining

The deformation of the reservoir landslide is mainly governed by the combined action of rainfall and the fluctuation of the reservoir water level. The determination of the threshold of triggering factors is of great significant in the stability analysis and evaluation of the potential landslide. Existing empirical threshold model is mainly based on statistical analysis to fit the explicit function between triggering factors and displacement, which is widely used in rainfall-triggered landslides. However, for reservoir landslides, the relationship among rainfall intensity, reservoir water level, fluctuation rate and landslide displacement is highly nonlinear, which hindered the application of existing empirical threshold model. To tackle the scientific challenge, a novel data mining-based threshold estimation method is proposed in this study. The Majiagou landslide, located in the Three Gorges Reservoir (TGR) region, is selected as the study stie. Firstly, the Distributed Fiber Optical Sensing (DFOS) technology has been adopted to record the rainfall, reservoir water level fluctuations, and deformation information for two years in real time; Then, the evolution pattern of Majiagou landslide was analyzed in depth; Finally, the cluster analysis and decision tree algorithm are used to determine the threshold value of the rainfall and the fluctuation of the reservoir water level. Among which, 80% of the data set is used for training model, and the remaining 20% is used for validation. The study here provides a new and effective method to estimate the triggering threshold and contribute to the prediction and early warning of reservoir landslides.

Reactivation characteristics and hydrological inducing factors of a massive ancient landslide in the three Gorges Reservoir, China

With the construction of the Three Gorges Reservoir (TGR), water impoundment began in 2003. The reservoir water level (RWL) has been annually adjusted to a fluctuation level between 145 and 175 m above sea level (a.s.l.) since 2008. Numerous large scale ancient landslides have been reactivated in the region. This paper focuses on the reactivation characteristics and influence of hydrological inducing factors on deformation of a massive ancient reservoir landslide. The Taping landslide is taken as an example, and its geological setting, material composition, structural characteristics and spatiotemporal deformation characteristics of the landslide are presented. According to the surface deformation characteristics, the Taping landslide can be divided into three subzones. Then, an attribute reduction algorithm based upon neighborhood rough set theory is used to quantitatively analyze the influences of key hydrological factors on the movements within the three subzones of the landslide. Long-term monitoring data are used to identify the key threshold values that induce fast movements within the three subzones of the landslide. Finally, the reactivation mechanism of the Taping landslide is proposed. The results show that the Taping landslide is in a state of periodically consistent creep deformation. The movements within the three subzones of the Taping landslide are affected by different hydrological factors. For the leading section, the hydraulic gradient is the most important hydrologic factor affecting the slope movement, and 0.07 is the key hydraulic gradient threshold for fast movement. For the middle section, rainfall and hydraulic gradient are the two most important hydrologic factors affecting the slope movement. The average daily precipitation over the last seven days and hydraulic gradients of 10 mm and 0.15 are the threshold values that induce its fast movement. For the rear section, the slope movement is mainly affected by the ground water level (GWL), and a GWL of 185.5 m a.s.l., is identified as the key threshold value that induces its fast movement. The findings in this study can help in managing reservoir landsides and designing landslide reinforcements in the regions of the world with geological settings similar to that of the TGR region.

Application of uncertain models of sliding zone on stability analysis for reservoir landslide considering the uncertainty of shear strength parameters

Application of uncertain models of sliding zone on stability analysis for reservoir landslide considering the uncertainty of shear strength parameters

Effect of weakening of sliding zone soils in hydro-fluctuation belt on long-term reliability of reservoir landslides

The impoundment of the Three Gorges Reservoir in China inevitably caused the reservoir landslides to develop toward instability, which has attracted widespread attention. Taking the Huangtupo Riverside Slump 1# with the structural characteristics of double sliding zones as a case, this study focuses on the sliding zone soils in the hydro-fluctuation belt affected by the periodic fluctuations of reservoir water level. The wetting-drying cycles test is carried out to simulate the cyclic impact of the fluctuations on the sliding zone soils. Hence the weakening law of the strength parameters is illustrated and well characterized by an exponential function model. Considering the different number of wetting-drying cycles, the long-term reliability of the shallow and deep sliding surfaces is quantified via the imbalance thrust force method and reliability theory. The results indicate that the local failure probability of both sliding surfaces gradually rises as the number of wetting-drying cycles increases. In particular, the shallow sliding surface presents a greater local failure probability than that of the deep one since more slices are in the hydro-fluctuation belt. Besides, the sensitivity of the landslide response to the strength parameters is different, and the internal friction angle is much greater than the cohesion. The area of the hydro-fluctuation belt plays a significant role on the long-term reliability of the landslide, which should be closely monitored to guide the reservoir operation.

Towards on Slope-Cutting Scheme Optimization for Shiliushubao Landslide Exposed to Reservoir Water Level Fluctuations

Bank slopes with huge accumulative sliding masses in a reservoir area suffer from landslide risks subjected to reservoir water fluctuations, and slope-cutting is an effective measure for the slope reinforcement. In this paper, a suitably and practicably integrated approach was put forward to the optimization design of the slope-cutting measure on the Shiliushubao landslide in Three Gorges Reservoir, China. It includes the determination of the most unfavorable reservoir water level variation condition according to the Three Gorges operation process, the mainly slope-cutting parts based on calculated residual sliding force curves, the main controlling factors of slope-cutting and their optimization ranges. The optimization analysis of the slope-cutting scheme for the Shiliushubao landslide was performed by taking advantage of the uniform design, transfer coefficient method, GA and ANN. The optimized layout of slope-cutting measure for Shiliushubao landslide after optimization study was to set the three main controlling factors of slope-cutting as i1 = 2.11, xb = 279.6 m and i2 = 2.01, which satisfied the objectives of both economy and landslide stability. The research results are helpful for the optimization design of slope-cutting scheme on reservoir landslides with large-scale accumulations.

BFTS - Engineering geologists' field station to study reservoir landslides

Abstract Accurate characterization and assessment of complex landslide behaviors represent a major challenge. The engineering geologist usually relies on analytical solutions, numerical simulation, and model testing for such evaluations. In recent years, in-situ experimental and monitoring platforms have become increasingly popular for studying landslide behaviors; they provide more reliable data and complement traditional evaluation methods. The impoundment of the 660 km long reservoir behind the Three Gorges Dam has initiated or reactivated thousands of landslides in the greater reservoir region. To study the long-term landslide behaviors during the Three Gorges Reservoir (TGR) operation, a field test station, known as the Badong Field Test Site (BFTS), was constructed at the site of the Huangtupo landslide that reactivated in the TGR Area (TGRA). BFTS is an engineering geologist's field station to study landslides, especially those in the TGRA. This article introduces BFTS from multiple perspectives, including its primary purposes, the facilities and research activities at BFTS, the role of BFTS in the prevention and mitigation of landslides in the TGRA, and the educational functions of BFTS.