Reservoir Water Level Fluctuation Research Articles

Deterministic and probabilistic analysis of a multi-sliding zone reservoir bank landslide influenced by the combined effects of reservoir water level fluctuation and precipitation

Deterministic and probabilistic analysis of a multi-sliding zone reservoir bank landslide influenced by the combined effects of reservoir water level fluctuation and precipitation

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Deformation characteristics and mechanism of Mala paleo-landslide during the Miaowei reservoir initial impoundment period

This study employs a multifaceted approach, encompassing field investigations, borehole surveys, surface deformation displacement monitoring, deep-seated deformation monitoring, and numerical simulation analysis, to conduct an exhaustive examination of the deformation processes and characteristics exhibited by the Mala Landslide. The findings elucidate a close correlation between the deformation of the Mala Landslide and the elevation of the reservoir water level. During the escalation of the reservoir water level, the landslide body progressively developed surface cracks, spanning from the frontal edge to the rear edge. The centre of the sliding body is situated in the central-lower downstream region, and presently, the landslide is undergoing a phase of comprehensive creep deformation. Due to the reservoir water level fluctuation rate being greater than the permeability coefficient, the deformation of the landslide displays a delayed response. As the reservoir water level reaches 1401 m during high-water operation, the two important ingredients, the buoyancy weight reduction effect and the influence of submerged reservoir water, significantly reduce the sliding resistance of the sliding mass, thereby exacerbating the deformation of the landslide. Following a comprehensive analysis of the findings, it can be firmly concluded that this landslide conforms to the characteristic traits of a typical buoyant force reduction type-retrogressive type landslide.

Elucidating the complex interplay between natural and anthropogenic factors in the deformation of the Muyubao landslide through time-series InSAR analysis

In the Three Gorges Reservoir area, landslide disasters occur frequently, making scientific monitoring and risk prediction crucial for disaster prevention and mitigation. However, most previous studies have been constrained by analysis of singular influencing factors. In this study, we employed multi-temporal InSAR techniques coupled with multivariate geospatial statistical analysis to monitor and analyze the dynamic evolution of the Muyuba landslide in Zigui County, Hubei Province, China from 2016 to 2023. The findings indicate that the Muyuba landslide was predominantly characterized by continuous, gradual subsidence. Key factors inducing deformation included well-developed drainage networks, gentle slopes of 15–30°, and the orientation of rock strata. Deformation rates in residential areas and along roadways exceeded background levels, implicating anthropogenic activities in the heightened landslide risk. A significant correlation was observed between landslide deformation and reservoir water level fluctuations, as opposed to rainfall patterns, highlighting reservoir regulation disturbances as a critical landslide triggering factor.

Phreatic Line Calculation of Reservoir Landslide under Complex Hydraulic Conditions—A Case Study

A seepage field, influenced by rainfall and reservoir water level fluctuation, is closely associated with the stability of the reservoir landslide. Understanding the phreatic line variation inside the landslide is of significant importance for the analysis and evaluation of slope stability. Currently, most of the boundaries of phreatic line analytical models and the hydrological conditions are simplified, resulting in discrepancies between the outcomes derived from these models and the actual situation. Given this, the newly proposed analytical model is refined by addressing the following two issues. Firstly, the consideration of variable-speed reservoir level fluctuations is incorporated, and secondly, the reservoir bank within the water-level fluctuation zone is treated as non-vertical. Under the combined effect of reservoir water level fluctuation and rainfall, the Boussinesq Differential Equation of unsteady seepage is established and applied to the Majiagou landslide in Three Gorges reservoir area. The results of the analytical solution are basically consistent with the measured groundwater level results, which has demonstrated the accuracy of the proposed model. Consequently, the proposed model can quickly and accurately calculate the groundwater level of landslides, which provides an effective means for the prediction and early warning of reservoir landslides.

Conditional random field-based stochastic analysis of unsaturated slope stability combining Hoffman method and Bayesian updating

This paper proposes a new framework for discretizing the conditional random fields that combines the Hoffman method and Bayesian updating, where multiple types of geotechnical data can be utilized, such as the field monitoring data and the site investigation geological borehole data. The feasibility of the proposed method is demonstrated using a rainfall-induced slope example in spatially varying soils. Results showed that the proposed framework of incorporating Bayesian updating to Hoffman method can effectively reduce the uncertainty in site characterization and guarantee precise risk assessment of unsaturated slope stability. Otherwise, the slope failure probability and landslide sliding volume would be overestimated when only the Hoffman method is adopted for site characterization. Subsequently, the developed method is further used to estimate the probabilistic stability of Baishuihe landslide in the presence of rainfall infiltration and reservoir water level fluctuation. It is hoped that this study can provide a reference for risk assessment of the unsaturated slope stability in reservoir areas.

Influence of Reservoir Water Level Fluctuations of Reservoir B (Rusunawa) on Groundwater Availability at the Environment of Lampung University

The presence of four reservoirs as rainwater collection infrastructure at the University of Lampung is expected to enhance the infiltration process and recharge the groundwater aquifers. However, the threat of water scarcity in the area may arise due to the reduction of recharge areas. Therefore, a comprehensive study is necessary to understand the important factors in maintaining groundwater availability. This research aims to analyze groundwater aquifers, identify fluctuations in the water level of Reservoir B, and analyze the relationship between the elevation of the water level in the reservoir and the availability of groundwater in the University of Lampung environment. The research methodology involves the interpretation of geoelectric data and the calculation of water level fluctuations in the reservoir. The analysis results indicate that the shallow groundwater aquifer is estimated to be located at depths ranging from 10 to 30 meters, while the deeper aquifer is situated at depths of 80 to 130 meters with a layer of sandy tuff. The highest fluctuation in water level is recorded at 14 cm during rainfall with an intensity of 52.1 mm/day. There were no significant changes in the water level observed during rainfall with intensities ranging from 0 to 2.4 mm/day, and the average fluctuation observed is approximately 0.5 cm every 8 hours.

Centrifuge modeling of unreinforced and multi-row stabilizing piles reinforced landslides subjected to reservoir water level fluctuation

With the construction of the Three Gorges Reservoir dam, frequent reservoir landslide events have been recorded. In recent years, multi-row stabilizing piles (MRSPs) have been used to stabilize massive reservoir landslides in China. In this study, two centrifuge model tests were carried out to study the unreinforced and MRSP-reinforced slopes subjected to reservoir water level (RWL) operation, using the Taping landslide as a prototype. The results indicate that the RWL rising can provide lateral support within the submerged zone and then produce the inward seepage force, eventually strengthening the slope stability. However, a rapid RWL drawdown may induce outward seepage forces and a sudden debuttressing effect, consequently reducing the effective soil normal stress and triggering partial pre-failure within the RWL fluctuation zone. Furthermore, partial deformation and subsequent soil structure damage generate excess pore water pressures, ultimately leading to the overall failure of the reservoir landslide. This study also reveals that a rapid increase in the downslope driving force due to RWL drawdown significantly intensifies the lateral earth pressures exerted on the MRSPs. Conversely, the MRSPs possess a considerable reinforcement effect on the reservoir landslide, transforming the overall failure into a partial deformation and failure situated above and in front of the MRSPs. The mechanical transfer behavior observed in the MRSPs demonstrates a progressive alteration in relation to RWL fluctuations. As the RWL rises, the mechanical states among MRSPs exhibit a growing imbalance. The shear force transfer factor (i.e. the ratio of shear forces on pile of the nth row to that of the first row) increases significantly with the RWL drawdown. This indicates that the mechanical states among MRSPs tend toward an enhanced equilibrium. The insights gained from this study contribute to a more comprehensive understanding of the failure mechanisms of reservoir landslides and the mechanical behavior of MRSPs in reservoir banks.

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

Landslides triggered by extreme rainfall due to global climate change are becoming more frequent. The Earth surface processes activity and landform evolution caused by landslide movement seriously affect human survival and the environment. In recent years, the reservoir slope located in the Three Gorges reservoir (TGR) area has been subject to the combined effects of extreme rainfall and reservoir regulation, which have been highly susceptible to inducing landslides. Reservoir landslides usually show step-like slow movement characteristics. The causes of slow-moving landslides are complex, the mechanisms behind them are difficult to grasp, and it may evolve into violent landslide disasters. Therefore, accurately predicting step-like displacement in slow-moving landslides is an effective solution for risk reduction. To achieve the accurate prediction of the step-like displacement of slow-moving landslides and the analysis of the failure mechanism, this study establishes a prediction model applicable to step-like displacement prediction of slow-moving reservoir landslides is developed based on 16 years of continuously monitored cumulative displacement of the Jiuxianping landslide, combined with the Empirical Mode Decomposition (EMD) method and the Improved Particle Swarm Optimization (IPSO) optimized the Long short-term memory (LSTM) neural network. This approach can effectively improve step-like displacement prediction accuracy. In particular, this approach can better represent the association between the step displacement of slow-moving landslides with environmental characteristics (e.g., rainfall, reservoir level). The study reveals that there are differences in the mechanisms and influencing conditions that cause the deformation of different areas of the landslide. Additionally, the reservoir water level fluctuation and seasonal rainfall are identified as the primary reasons contributing to the stepped displacement of the Jiuxianping landslide. The method provides a basis for the step-like displacement prediction, instability mechanism and landform evolution study of slow-moving reservoir landslides and provides valuable guidance for the prevention and control of similar reservoir geomorphic hazards worldwide.

Complementary scheduling rules for hybrid pumped storage hydropower-photovoltaic power system reconstructing from conventional cascade hydropower stations

The reconstruction of conventional cascade hydropower plants (CHP) into hybrid pumped storage hydropower plants (HPSH) by adding a pumping station has the potential to increase the hydropower's flexibility and promote the consumption of renewable energy into the power grid. However, the complex hydraulic and electric connections between cascade hydropower stations and multi-energy sources pose challenges to safe and economic operation. This study explores the complementary scheduling for hybrid pumped storage hydropower-photovoltaic (HPSH-PV) system and evaluates the operation benefit and risk. First, the complementary scheduling rules that consider the demand for long-distance and across-regions power transmission are proposed to guide the peak-shaving operation of the system. Then a case study is performed with a hydro-PV complementary project in China, and the benefit and risks are evaluated quantitatively while considering the uncertainty of PV power prediction. The results demonstrate that after a pumping station is added (1) the power generation profit can be increased, especially in the dry seasons and the upstream reservoir; (2) the regulation capacity and power-supply reliability of the upstream reservoir is increased, while that of the downstream reservoir is slightly reduced; (3) the reservoir water level fluctuation and operation risk increase, but it can be lowered as the inflow increases.

Creep behavior and permeability evolution of red sandstone in three Gorges Reservoir area subjected to cyclic seepage pressure

The creep behavior of the rock mass in an anchoring section affects the control effect of the stabilizing piles and the long-term stability of the landslide-stabilizing pile system. Additionally, periodic reservoir water level fluctuations cause cyclic water-seepage pressure changes in the rock mass of bank slopes, which may promote rock creep deformation, further compromising the stability of the slope and increasing the risk of landslides. In this experimental study, the creep properties and permeability of red sandstone from the bedrock of the Majiagou landslide in the Three Gorges Reservoir area were investigated under cyclic seepage pressure conditions. Four seepage pressures (0, 1, 2, and 1-2 MPa) were selected for comparison. The results demonstrated that creep strain curves, especially volumetric strain, exhibited fluctuations under cyclic seepage pressure conditions. The creep deformation of red sandstone was enhanced by cyclic seepage pressure. Creep strain increased with increasing deviatoric stress, except at 60 MPa deviatoric stress. Furthermore, the creep time to failure at 108 MPa deviatoric stress was inversely proportional to the seepage pressure. The steady-state creep rate exponentially increased with deviatoric stress, and the steady-state creep rate under cyclic seepage pressure was higher than that under constant seepage pressures of 1 and 2 MPa. Finally, the permeability of the red sandstone fluctuated in accordance with the fluctuating seepage pressure. The permeability evolution of the red sandstone samples under different deviatoric stresses was attributed to the competing processes of preexisting microcrack closure and the propagation of new microcracks. Accurate evaluation of the long-term performance of stabilizing piles and the stability of stabilized landslides necessitates consideration of the influence of cyclic seepage pressure on rock mass creep behavior.

Time-Lag Response of Landslide to Reservoir Water Level Fluctuations during the Storage Period: A Case Study of Baihetan Reservoir

Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

InSAR-based method for monitoring the long-time evolutions and spatial-temporal distributions of unstable slopes with the impact of water-level fluctuation: A case study in the Xiluodu reservoir

Slope instability in reservoir areas is one of the greatest concerns for public and governments worldwide. The long-time evolution of the unstable slopes in reservoir areas after the impoundments remain poorly understood. Among the technologies applied to detecting and monitoring unstable slopes, interferometry synthetic aperture radar (InSAR) technology is one of the most effective. However, owing to the slopes in a reservoir suffering from periodic water-level fluctuations, InSAR technology has shortcomings for monitoring unstable slopes with large displacement for a long time. In this study, a post-processing method of InSAR and a practical strategy were proposed to monitor the long-time evolution of the unstable slopes in reservoir areas both spatially and temporally. The strategy can capture the evolution trends of the unstable slopes based on the variations in their deformation areas with reservoir water-level fluctuations for a long time. The case study was carried out in the Xiluodu reservoir area of China. The monitoring data of the unstable slopes before and after reservoir impoundment were extracted from multi-source SAR data, including those from ALOS-1, ENVISAT ASAR, and Sentinel-1. It was found that there were 40 unstable slopes before the reservoir impoundment and 152 unstable slopes after the reservoir impoundment. Of the 152 slopes, 142 slopes (94%) began deforming in the first 3 years after reservoir impoundment, 15 slopes have been experiencing continuous deformation, and 137 slopes underwent intermittent deformation. The areas of 79 slopes (52%) increased during the water-level drawdown periods and decreased during the rising periods. The areas of 22 slopes (14%) demonstrated the opposite trend, and the areas of 51 slopes (34%) increased independently of the water-level fluctuations. In terms of evolution trends in the deformation area, 30% of slopes experienced a decreasing trend, 15% were under a constant deformation trend, and 55% showed an increasing trend. The distribution of the unstable slopes varied from the dam site to the end of the reservoir, and most of them favorited the areas where slope angle and height were 20–35° and 30–300 m, respectively. The soil slopes composed of the previous landslide deposits were prone to deformation, followed by the dip to overdip layered rock slopes.

Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy

The application of ensemble learning models has been continuously improved in recent landslide susceptibility research, but most studies have no unified ensemble framework. Moreover, few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level. This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models. For this purpose, the present work focuses on three different basic classifiers: decision tree (DT), support vector machine (SVM), and multi-layer perceptron neural network model (MLPNN) and two homogeneous ensemble models such as random forest (RF) and extreme gradient boosting (XGBoost). The hierarchical construction of deep ensemble relied on two leading ensemble technologies (i.e., homogeneous/heterogeneous model ensemble and bagging, boosting, stacking ensemble strategy) to provide a more accurate and effective spatial probability of landslide occurrence. The selected study area is Dazhou town, located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China, which is a strategic economic area currently characterized by widespread landslide risk. Based on a long-term field investigation, the inventory counting thirty-three slow-moving landslide polygons was drawn. The results show that the ensemble models do not necessarily perform better; for instance, the Bagging based DT-SVM-MLPNN-XGBoost model performed worse than the single XGBoost model. Amongst the eleven tested models, the Stacking based RF-XGBoost model, which is a homogeneous model based on bagging, boosting, and stacking ensemble, showed the highest capability of predicting the landslide-affected areas. Besides, the factor behaviors of DT, SVM, MLPNN, RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area, wherein unfavorable lithological conditions and intense human engineering activities (i.e., reservoir water level fluctuation, residential area construction, and farmland development) are proven to be the key triggers. The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.

Application of spectral analysis to the reservoir-triggered earthquakes in Three Gorges reservoir region, China

In the Three Gorges reservoir region, central China, seismic activity increased substantially after the reservoir impoundment in 2003 which continues till date. Previous studies show that these are reservoir-triggered earthquakes and various factors are responsible for the increase in seismic activity after the reservoir impoundment. However, these studies do not provide a comprehensive assessment of influence of reservoir water level variations on spatiotemporal distribution of earthquakes. In this study, we statistically analyze the influence of the water level variations on the increased seismic activity in the reservoir region for the period from May 2003 to April 2020, using the power spectrum and singular spectrum techniques. Our statistical analyses confirm the influence of long-term variations in the water level time series on the occurrence of earthquakes after the reservoir impoundment. The analysis also indicates a positive role of annual reservoir water level fluctuations in the total seismicity of the region. Depending on the cluster patterns and relationship with faults, the earthquakes of the Three Gorges reservoir region are divided into three seismic zones (A, B, and C). For zone C, both the power spectrum and singular spectrum analyses confirm the strong periodic influence of reservoir water level variations on the earthquakes. Increase in seismicity of zone B is only in the initial period but not in the later stages of water impoundment and our statistical analyses indicate that the seismicity of this zone is not directly related to the annual reservoir water level variations. This confirms the conjecture in the earlier studies that the seismicity of this zone is related to the collapse of coal mines present in the area in the initial stages of reservoir impoundment. For zone A, our statistical analyses do not show strong influence of the annual reservoir water level variations on the occurrence of earthquakes. We suggest that this is due to the contribution of various other factors along with reservoir impoundment in the occurrence of earthquakes in this zone, as also opined in some earlier studies.

Application of 210Pbex in elucidating contemporary sedimentary dynamics in the water-level fluctuation zone of the Three Gorges Reservoir, China

Excess 210Pb (210Pbex) has been widely applied as a proxy indicator for establishing the chronology of sediments in lakes where the sedimentation processes comply with the assumptions for using 210Pbex dating models. However, little is known regarding the applicability of 210Pbex to elucidate contemporary seasonal sedimentary dynamics in reservoir water-level fluctuation zones (WLFZs). In the present study, sedimentary core and bulk sediments were collected from the WLFZ of the Three Gorges Reservoir (TGR) in China, and the physical and chemical properties were determined. Results for 210Pbex activity, total organic carbon, and total nitrogen of bulk sediments indicate pronounced elevation-dependent spatial variation (p < 0.05), but 137Cs activity and particle composition show no systematic trends, allowing 210Pbex activity to be used to differentiate seasonal stratigraphy. For the sedimentary core, therefore, sediment layers with lower 210Pbex activity were inferred to represent sedimentation during wet seasons, when the 210Pbex concentration was diluted by a higher suspended sediment load, whereas sediment layers characterised by higher 210Pbex activity were ascribed to sedimentation during dry seasons, when the tranquil water favoured 210Pbex fallout to be absorbed by suspended sediments and then deposited in the WLFZ. Hereupon, temporal variation is observed in annual sedimentary thickness, with relatively low sedimentation rates during the initial stage (2006–2008) of the TGR impoundment, followed by relatively high sedimentation rates from 2009 to 2013. Over the period from 2014 to 2017, sedimentation rates were low because the suspended sediment was intercepted by upstream cascade reservoirs. Subsequently, sedimentation rates were higher during 2018–2019 as a consequence of an increase in extreme rainfall events. The findings herein highlight that 210Pbex-based seasonal stratification discrimination is effective approach to establish high-resolution sedimentary chronosequences and that net sedimentation rates in WLFZ are controlled by inflow suspended load of the TGR.

Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils

The Three Gorges Reservoir Area (TGRA) is one of the most important landslide-prone regions in China, and rational stability evaluation of reservoir slopes in it is of great significance to design mitigation measures and prevent landslide disasters. It is well recognized that seasonal rainfall and periodic reservoir water level fluctuation are the two major factors influencing the stability of reservoir slopes, and thus the reservoir slope stability may be varying with the external triggering factors. Although geotechnical reliability analysis offers a novel means to quantify slope stability in a probabilistic manner, the previous research focuses more on the time-independent slope reliability analysis, ignoring the effects of time-varying factors. How to evaluate the time-dependent reliability of reservoir slopes accurately and efficiently remains an open question. This study proposes deep learning (DL)-based time-dependent reliability analysis approach, and a practical case adapted from the Bazimen landslide in the TGRA is used for illustration. The predictive performances of the three DL algorithms, namely Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Light gradient boosting machine (LightGBM) are systematically investigated. Results show that the proposed DL-based approach can reasonably portray the variation tendency of the Bazimen landslide time-dependent failure probability, which provides a promising way to rationally evaluate the time-dependent failure probability of reservoir slopes considering the spatial variability of soil properties. Among the three DL algorithms, the CNN performs the best in the Bazimen landslide example.

Study on the Hysteresis Effect of the Stability of Longyangxia Dam Bank Slope with the Fluctuation of Water Level

Reservoir landslide is a common geological disaster during the operation of hydropower projects. The fluctuation of reservoir water level is an important factor that induces slope instability. The natural slope will gradually lose stability under the rapid rise and fall of reservoir water level. A variety of landslide examples show that, on the premise of not suffering from extreme environmental conditions, the reservoir landslide often occurs after the rapid fluctuation of the reservoir water level for a period of time, and there is a certain time difference between its instability failure and the rapid rise and fall of the reservoir water level. In order to study the hysteresis effect between the stability change of the reservoir slope and the fluctuation of the reservoir water level, taking the Chana landslide in the reservoir area of Longyangxia Hydropower Station as an example, combining the field survey, geological survey data, and the actual change of the reservoir water level, the finite element simulation software is used to restore the actual working condition, and the research is carried out from three aspects of slope seepage field, displacement field, and stability. The results show the following: (1) The hysteresis of slope seepage change during the rising period of reservoir water level is more obvious than that during the falling period of reservoir water level. The deformation potential of Chana landslide is greater during the falling period of reservoir water level, while the deformation is restrained during the rising period of reservoir water level. (2) The incremental displacement simulation results of Chana landslide show that there are deformation incubation period and deformation release period of Chana landslide. The deformation incubation period occurs during the rapid rise and fall of reservoir water level, and the deformation release period occurs during the stable change of reservoir water level. (3) The incremental displacement produced during the deformation release period of the slope is large, but the slope remains stable, which reflects the existence of the internal antisliding mechanism of the landslide from the side. (4) The stability coefficient of Chana landslide changes greatly during the drawdown period. When the drawdown rate of water level is kept at 0.03~0.07 m/d, the stability coefficient of Chana landslide changes slightly and the slope is relatively stable.

Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms

The Three Gorges Reservoir Area (TGRA) is one of the most important areas for landslide prevention and mitigation in China. Rational reliability analysis of reservoir slope stability is a significant prerequisite for designing mitigation measures and preventing landslide disasters. Due to the influences of periodic reservoir water level fluctuation and seasonal rainfall, the reservoir slope reliability may be varying with the external environment. Although geotechnical reliability analysis has been widely applied, how to evaluate the time-variant reliability accurately and efficiently is still a challenging task. This study develops an efficient time-variant reliability analysis approach by integrating the advanced machine learning algorithms of extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM). The proposed approach is applied to a practical case adapted from Bazimen landslide in the TGRA. The performances of XGBoost and LightGBM in the evaluation of Bazimen landslide time-variant failure probability are systematically explored. Results show that the proposed approach can evaluate the time-variant failure probability of Bazimen landslide accurately and efficiently, addressing the prohibitive computational cost of conducting a large number of deterministic analyses at each time instant repeatedly. This greatly facilitates the acquisition of time-variant failure probability for geotechnical engineers in practical applications.

Mechanical response and deterioration mechanism of sandstone with intermittent joints under cyclic drying–saturation

A drying–saturation cyclic water–rock interaction test simulating the periodic fluctuation of reservoir water level was designed. The results are as follows. (1) With increasing number of cycles, the compressive strength of a sandstone joint gradually decreases. The compressive strength decreases rapidly for the first four cycles and a deterioration effect is obvious. As the joint dip angle increases, the compressive strength of the sandstone joint shows a U-shaped distribution. (2) With increasing number of cycles, the elastic modulus of the sandstone joint gradually decreases. With increasing joint inclination angle, the elastic modulus shows a V-shaped trend of change. (3) With increasing number of cycles, the brittleness of the sample obviously decreases and the sample shows the characteristics of looseness and weakness. With the increase of joint inclination, the failure mode of the sample gradually changes from tensile failure to tensile-shear failure to tensile failure. (4) The mechanical properties of an intermittent sandstone joint under circulating water–rock interaction show obvious deterioration effects, which are the result of physical, chemical and mechanical actions occurring inside the sandstone joint.