Multi-Scale Displacement Prediction and Failure Mechanism Identification for Hydrodynamically Triggered Landslides

Hydrodynamic Pressure landslides are the most typical landslides affected by the water level in the Three Gorges Reservoir. These types of landslides are large and cause severe deformation. Therefore, they may significantly affect the safe operation of the Three Gorges Reservoir once losing stability. Taking the Shuping landslide in the Three Gorges Reservoir as a typical case, this study explored the deformation characteristics and influencing factors of the Hydrodynamic Pressure landslide using monitoring data of its surface deformation, 17 years of GPS displacement data, automatic displacement metre of surface cracks, and groundwater level. To determine the deformation mechanism of the Hydrodynamic Pressure landslide, the model of Hydrodynamic Pressure landslide was designed and carried out to simulate the fluctuation of reservoir water level. The results showed that the hydrodynamic pressure landslides deformation was mainly affected by the overall deformation of the front edge of the landslide, and the amount of deformation was relatively large. The back part of the landslide mainly encountered tensile deformation. The Hydrodynamic Pressure landslides are a typical retrogressive deformation. The deformation of Hydrodynamic Pressure landslide was affected by the periodic change in the reservoir water level and the overall deformation trend is increasing. The intrinsic factor influencing the deformation of the hydrodynamic pressure landslide is the low permeability coefficient of the sliding mass, and the extrinsic factor is the drawdown of the reservoir water level. When the reservoir water table drawdown occurred, there was a noticeable water level difference with the underground water level in the landslide mass and generated hydrodynamic pressure. The Hydrodynamic Pressure landslide model results showed that the pore-water pressure, effective stress, and groundwater level in the landslide were consistent with the reservoir water level but with noticeable hysteresis. The key hydrodynamic factor for the deformation of this type of landslide was the hydrodynamic pressure towards the outside of the slope, formed when the reservoir water level dropped. Moreover, it was found that the faster reservoir water-level drawdown rates led to an increased influence on the stability of the landslide.

This paper evaluated the hydrodynamic and deformation characters of the Hongyanzi landslide in Pubugou hydropower station reservoir area, Sichuan province, Southwest China. The fluctuations of slope groundwater level, slope surface displacement and deep seated displacement with the fluctuation of reservoir water level were monitored. The results showed that the fluctuation of groundwater level followed the reservoir water level with two month of time lag. Approximate 0.23 to 0.25 m/day of declination rate for reservoir water level could be benefit for maintaining the global stability of the bank slope when the elevation of reservoir water level varied from 820 to 847 m. A constant head difference between the groundwater level and reservoir water level is important to the stability of reservoir slope.

Numerous wading landslides formed in Three Gorges Reservoir. Water level cyclical fluctuations causes the changes of properties of the rock-soil bank landslides, also make the changes of seepage field and the stability of the landslides. This paper studies the response of the wading landslides stability induced by water level fluctuation. It analyses the hydrodynamic pressure landslide using the classification of significant wading landslide in Three Gorges Reservoir. It takes the Zigui Shuping Landslide as a case study. The SEEP and SLOPE modules Geo-studio software are used to model the process of seepage field and calculate the stability, analyzes the law of effect hydrodynamic pressure landslide under different permeability coefficients of the landslide and the water level rate. The results show that the groundwater level has a concave phenomenon and the stability coefficient increases when the reservoir water level rises. The groundwater level has a convex phenomenon and the stability coefficient obvious decreases when the reservoir water level reduces for hydrodynamic pressure landslide. The reservoir water level fluctuation has the larger rate, and the permeability coefficient is smaller. The reservoir water level fluctuation has more obvious effect on the seepage and stability of the landslide.

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide.

Hydrodynamic-induced landslide is a type of landslide whose stability can be reduced by rainfall, groundwater or reservoir water level fluctuations (RWLF). Based on the monitoring data of Wangjiashan landslide located in the reservoir area of Baihetan Hydropower Station, this article illustrates the deformation characteristics of Wangjiashan landslide on time and space distribution. Spearman correlation coefficients are used to make analysis of the relationship between surface deformation and different dynamic factors such as RWLF, seismic activity, and rainfall. The results indicate that RWLF is a dominant dynamic factor of Wangjiashan landslide. Besides, two variables of deformation factors are presented to find differences during various deformation periods. The results show that the displacement and its rate are both strongly correlated with the RWLF, especially during the acceleration period and the deceleration stage. It is indicated that Wangjiashan landslide is a typical hydrodynamically induced landslide. This study provides a useful reference for understanding the instability mechanism of the landslide.

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.

Unsaturated slope stability around the Three Gorges Reservoir under various combinations of rainfall and water level fluctuation

Landslide prediction is very important and challenging for reducing geological hazards. In the Three Gorges Reservoir area, landslides show stepped deformation due to seasonal rainfall and periodic fluctuation of reservoir water level. The purpose of this study is to use complete ensemble empirical mode decomposition with adaptive noise and grey wolf optimization to support the vector regression method for displacement prediction. Firstly, the cumulative displacement is decomposed by CEEMDAN to obtain both trend term and fluctuation term displacement. Secondly, according to the cumulative displacement, rainfall, and reservoir water level data, the influencing factors related to the displacement of the trend term and the fluctuation term are determined. Then, the GWO-SVR model is used to predict the trend and fluctuation displacement. The final prediction result is obtained by adding the calculated predicted displacement values of each component. The Shuizhuyuan landslide in the Three Gorges Reservoir area, China, was taken as an example, and the long-term displacement data of monitoring point SZY-03 were selected for analysis. The results show that the root mean square error (RMSE) and coefficient of determination (R2) between the measured displacement values and the prediction values were 0.9845 and 0.9964, respectively. The trained model has high computational accuracy, which proves that the GWO-SVR model can be used for displacement prediction of this type of landslide in the Three Gorges Reservoir area.

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.

The stability of the reservoir bank landslide is affected by a variety of external factors, and the fluctuation of reservoir water level is one of the important influencing factors. The Erdaohe landslide is a typically colluvial landslide in the Three Gorges Reservoir area with periodic reservoir water level fluctuations. According to landslide displacement data, the displacement of the Erdaohe landslide exhibits the significantly stepwise feature. Its failure mechanism was analyzed using strength reduction method by the FLAC3D package in the case of reservoir water level changes. The results indicate that the hydrodynamic pressure has an important impact on the initialization of the landslide failure. When reservoir water level rises rapidly or maintains constant at the lower level, the landslide stability would be higher. When the reservoir water level decreases rapidly or maintains constant at the higher level, the landslide stability will be smaller. When the reservoir water level was in the lowest elevation, the factor of safety (FS) reached the minimum value of 1.11. Findings in this paper can provide guidelines for the risk assessment of colluvial landslides.

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved.

Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide

The Majiagou landslide is located on the left side of the Zhaxi-he River, a tributary of the Yangtze River. There is a large old landslide there originally, on which three secondary landslides were formed. The Majiagou landslide is one of the secondary ones. After the water level of the Three Gorges Reservoir rose from 95 to 135 m in June 2003, the Majiagou landslide occurred including a 20 m- long, 3–5 cm wide, (locally 10 cm-wide) fissure at its back that occurred in a time-frame of 3 months. The implication is that the reservoir impounding reactivated the landslide. The slide mass of the Majiagou landslide is composed of a thin layer of silty clay on the top with low permeability and a thick layer of angular pebbles as the main portion beneath the silt clay which has high permeability. The rate of water level fluctuation is between 0.6 and 4.0 m/d with regard to the altitude between 145 and 175 m in the reservoir during its normal operational state. Under these conditions, the FEM method was applied to simulate the groundwater changes in the slide mass, coinciding with the reservoir water level fluctuation. The results suggest that the groundwater level almost changes with the reservoir water level simultaneously within the slide mass, which means that the groundwater gradient is very gentle. Therefore, the effect of the reservoir water level fluctuation on the landslide stability is mainly by the action of buoyant force. Without considering dam failure, the seepage force is so little as to be negligible. The stability of the landslide in cases of different water levels is then calculated using the Morgenstern-Price method, and the results show that the factors of safety decrease with the water level rise. As the water level increases to 165 m, the factors of safety are at the minimum value, increasing with the water level rise. The minimum value reflects that the landslide is in a critical state, so stabilizing design was applied using stabilizing piles as well as a water drainage system. The project was completed in early 2006, and the water level has risen to 156 m in October 2006. Monitoring data illustrated that there is no further deformation of the landslide and the stabilizing piles, so the stabilizing work is effective.

Reservoir water level fluctuation is one of the main extrinsic factors that could change the stress field in landslides, as well as the mechanical strength of geomaterials, hence affecting the deformation and stability of landslides. The largest reservoir landslide in the Three Gorges Reservoir area was selected for a case study. The impact of reservoir water level fluctuation is represented by the dynamic change in the underground seepage field and was thereby analyzed with numerical modeling. The deformation behavior considering the rheological properties of the slip zone soil was studied. The sudden change in the displacement–time curve was selected as the failure criterion for the investigated landslide. The evolution process of the accelerated deformation stage was divided into slow acceleration, fast acceleration, and rapid acceleration stages. The Huangtupo landslide is characterized by a retrogressive landslide and is currently in the creep deformation stage; the deformation mechanism and deformation characteristics are closely related to the reservoir water level fluctuation. Research was carried out by means of field investigation, in situ monitoring, and numerical simulation to provide a true and reliable result for stability evaluation.

Rainfall and reservoir water level (RWL) fluctuations are the most important factors affecting reservoir landslide stability. Although extensive research has explored landslide stability under the combined effect of rainfall and RWL fluctuation, quantitative investigations on the individual contributions of rainfall and RWL fluctuation to landslide stability are limited. To address this issue, taking the Majiagou landslide in the Three Gorges Region (TGR) as an example, the seepage field of the Majiagou landslide was simulated and analyzed under three different scenarios: the individual effect of rainfall; the individual effect of RWL fluctuation; and the combined effect of rainfall and RWL fluctuation. The corresponding stability condition of the three scenarios was evaluated. The results show that the fluctuation of RWL is the critical factor that governs the stability of the Majiagou landslide. Specifically, when the water level drops rapidly from 165 m to 145 m, with an average rate of 0.859 m/d, the landslide safety factor decreases most significantly. The reason is that rapid water level decline creates outward-directed seepage forces that promote slope deformation. In contrast, rainfall has a limited effect on slope stability, with the safety factor only decreasing when rainfall exceeds 50 mm/d. This is because a seepage force directed outward from the slope develops only when rainfall reaches a certain threshold, leading to a reduction in the slope’s safety factor. In addition, this study reveals that the combined effect of rainfall and RWL fluctuations generates a synergistic amplification mechanism. Specifically, the safety factor variation under combined hydrological conditions significantly exceeds the arithmetic sum of individual rainfall-induced variation and RWL-induced variation. This study helps us understand how rainfall and RWL fluctuation affect slope stability by altering the seepage field, which is crucial for preventing landslides.