Silty mudstone, a widely distributed soft rock, presents shear strength properties that are critical for slope stability and geotechnical safety. However, the shear behavior of silty mudstone under saturated conditions remains insufficiently characterized, despite the fundamental role of moisture in controlling rock strength and deformation. In this study, the purple-red silty mudstone from the Hongshiya Formation in the Yezhutang area of Yunnan Province, China, was selected as a representative material. Laboratory direct shear tests were conducted under both natural and saturated conditions. The shear strength parameters of the silty mudstone were obtained, and the degradation mechanism of moisture on its strength was analyzed. The roughness coefficient of the shear failure surface was obtained using 3D laser scanning technology, and then was used to quantitatively describe the damage characteristics of the shear failure surface of silty mudstone samples. Results show that the failure modes of silty mudstone samples are mainly shear failure and local shear failure. Under the local shear failure mode, the shear strength of saturated silty mudstone significantly weakens, with a 50% decrease in cohesion and a 5% decrease in internal friction angle. That is, the weakening of cohesion by water is more pronounced compared to that of friction angle. The roughness coefficient of the shear failure surface of silty mudstone in the saturated condition is smaller than that in the natural condition, and the wear degree is more significant. The results provide valuable insights into the shear strength assessment and moisture-induced damage characteristics of silty mudstone in the Yezhutang area of Yunnan Province.

To identify the driving force of the landslide, the deformation modes of landslides can be classified as retrogressive, progressive, and complex. The fluctuation of the reservoir water level has different effects on retrogressive and progressive landslides. Under the long-term action of the reservoir water level, the deformation trends of landslides in different deformation modes are also dissimilar. Therefore, classifying the modes of landslides and discovering their deformation trends not only possess academic significance but also have implications for reservoir prevention and control. Interferometric Synthetic Aperture Radar (InSAR) technology, known for its all-weather capability, traceability, and high resolution, is widely utilized for monitoring surface deformations in reservoir landslides. However, the dynamics of these landslides are significantly influenced by fluctuations in water levels; thus, results obtained at a single time point often fail to accurately represent long-term deformation trends. Consequently, this study utilized ALOS PALSAR-1 (2006–2010), ENVISAT ASAR (2010), and Sentinel-1 (2014–2024) satellite SAR data to identify deformed landslides before and after the first impoundment of the Xiluodu Reservoir using an enhanced InSAR methodology based on the average of multi-SAR data fusion. Five deformation modes based on the magnitude of deformation in different areas of the slope were summarized, and deformation areas corresponding to each cycle of water-level fluctuation and annual variations were extracted to investigate correlations between different deformation modes and water-level fluctuations, thereby inferring the long-term deformation trends. The results revealed: (1) The initial impoundment of the reservoir had a significant impact on retrogressive landslides primarily during the first three years post-impoundment; (2) The influence of water-level drawdown on landslide deformation in the Xiluodu Reservoir area was more pronounced than that caused by water-level rising; (3) Over the long term, all five deformation modes exhibited predominant decreasing trends in their respective deformations; (4) The retrogressive landslides with continuous deformation accounted for 47

The Baihetan Reservoir filling began on April 15, 2021, triggering extensive landslides and the reactivation of previously deposited slope material after two periods of 825 m trial impoundments. Reactivation events are distinct in the Heishui tributary. It is crossed by the Zemuhe Fault Zone, with broken rock soil mass inducing bank collapse processes associated with long-term tectonic activity. Following two impoundment cycles, three disastrous slope failures occurred in the Heishui tributary, posing significant threats to road and building safety. We aimed to qualitatively and quantitatively evaluate the transient evolution of bank collapse in the Heishui tributary. Terrain-following photogrammetry and innovative dual-controller cooperative UAV flight campaigns will be conducted in 2022 and 2023. Then, digital orthophoto maps (DOMs) and digital surface models (DSMs) were created to establish and analyze comprehensive bank collapse inventory and distribution laws. Next, a geomorphic change detection (GCD) method was used to calculate the erosion caused by catastrophic bank collapses. The results indicated that 80 bank collapses occurred during the initial impoundment, which increased to 90 after the second impoundment. The collapses mainly occurred at the intersection of the faults and bank slope. Catastrophic bank collapses exhibit precursor signs, with the front edge experiencing failure in areas ranging from 7 % to 46 % during the initial impoundment and destruction occurring during periods of high water levels. In addition, the bank slope increases by 2 degrees to 5 degrees. Although some bank collapses became part of the fluctuating zone after impoundment, the collapse process remained prevalent in the Baihetan Reservoir. This case study focuses on bank collapse geomorphological characteristics, distribution laws, and transient evolution, which can help enhance the understanding of disaster prevention in reservoir impoundment regions.

Constructing large hydroelectric power stations in canyon areas is widely accepted as a solution to meet energy demands. However, large-scale water storage elevates water levels, shifts the water-land boundary, increases evaporation, and alters the microclimate, potentially triggering a chain of environmental responses. This raises concerns about whether such changes could increase abnormal precipitation events, thereby stimulating more widespread slope failures and vegetation changes, ultimately disturbing the landscape. The Baihetan Hydropower Station, located on the lower Jinsha River in China, serves as a case study for exploring these effects. By monitoring long-term surface deformations, abnormal precipitation, topography, geomorphological parameters, and vegetation changes, we have gained insights into the macro disturbances caused by water level fluctuations. Since the reservoir began storing water, slope failures have markedly increased, particularly in the form of creeping slopes and bank collapses in the drawdown zone. This period has also seen a reduction in total precipitation, an increase in abnormal precipitation, and slower vegetation growth. Further analysis reveals that while rising water levels primarily destabilize reservoir shore slope-failures, precipitation also significantly influences this instability. The greatest threat to shore stability arises when water levels drop and are followed by heavy rainfall. Although the severity of abnormal precipitation has increased post-impoundment, it has not led to more extreme precipitation events. Vegetation growth on active slopes near the reservoir is mainly controlled by changes in precipitation, with vegetation decline due to slope instability being limited and not widespread. These findings contradict initial assumptions, indicating that landscape disturbances due to water storage are limited and have not led to severe, uncontrollable chain reactions.

[Objective]Slope instability triggered by reservoir water-level fluctuations represents a prevalent geohazard in mountainous regions and canyons undergoing large-scale hydropower development.Since the 21st century,accelerated hydropower development has necessitated enhanced methodologies for identifying such specific-type geohazard potentials.In recent years,InSAR observations have largely addressed the challenge of identifying large-scale,multi-target deformation;however,due to limitations in real-time monitoring capabilities,this technique cannot detect latent hazards that have not yet manifested as deformations.Therefore,there is an urgent need to establish geomorphic signatures of reservoir-induced slope failures to improve hazard identification specificity.The large-scale impoundment of the Baihetan Reservoir since 2021 has triggered a series of slope instabilities,providing an exceptional opportunity to define the geomorphic signatures.[Methods]We integrated InSAR observations,geomorphic parameters,and optical imagery.Specifically,we utilize 228 ascending and 234 descending Sentinel-1A datasets(2020-2023)processed with DS-InSAR to identify deformed slopes triggered by reservoir water-level fluctuations.[Results]The results demonstrate the explanatory power of geomorphic parameters such as toe height,slope,aspect,and roughness in relation to disaster triggers.Furthermore,the analysis reveals correlations between lithological variations,slope structures,precipitation,and reservoir water-level fluctuations.[Conclusion]The strength of lithology,slope structure,and geomorphometric parameters in the Baihetan Reservoir area,along with their corresponding numerical ranges,form composite geomorphic signatures that can be used to identify hazards associated with reservoir water-level-induced slope instability early on.Additionally,we discovered that,beyond the effects of water-level fluctuations,precipitation events also play a significant role in triggering slope instability in the reservoir area,highlighting the importance of this factor as a driving force.[Significance]These insights significantly advance risk mitigation strategies for hydropower projects,facilitating optimal site selection and operation of hydropower stations,while providing a reference framework for assessing other slope instability mechanisms.

Large-scale three-dimensional (3D) physical modeling is an important method to study landslide-induced impulse waves. In such models, the test randomness is often quite high, which necessitates systematic exploration of the randomness and error. However, only a few relevant studies have been conducted yet. To this end, this study aims to investigate the randomness and error of large-scale 3D landslide-induced impulse wave experiments and provide solutions to the different sources of error. Based on six repeatability experiments with the large-scale 3D physical model of the Wangjiashan landslide-induced impulse wave in the Baihetan reservoir of the Jinsha River, China, the errors of typical physical parameters are classified into systematic errors, which originate from instrumental factors, experimental design, observer bias, environmental factors, and random errors originating from communication and observation. The allowable error rate of landslide motion in the repeatability experiment is found to be 5%, but the dynamic chain transmission of landslide-induced impulse waves leads to the transmission and accumulation of errors, which causes a gradual increase in the errors of landslide motion, primary wave, propagating wave, and run-up process; and the coefficient of variation increases from approximately 3.8% to 25.0%. To reduce the experimental data error, a low-pass filtering model for removing high-frequency noise and a moving window smoothing model for image frame rate mutation are established, which can decrease the coefficient of variation by nearly 1.3%–4.0%. The corrected particle dynamic map exhibits a continuous and smooth flow field, which basically eliminates the velocity field mutation and discontinuity caused by communication data packet loss. Overall, this study can provide theoretical basis and technical support for large-scale 3D landslide-induced impulse wave experiments.

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices.

After the initial impoundment of the Baihetan Reservoir in April 2021, the water level in front of the dam rose about 200 m. The mechanical properties and effects of the bank slopes in the reservoir area changed significantly, resulting in many bank collapses. This study systematically analyzed the bank slope of the head section of the reservoir, spanning 30 km from the dam to Baihetan Bridge, through a comprehensive investigation conducted after the initial impoundment. The analysis utilized UAV flights and ground surveys to interpret the bank slope’s distribution characteristics and failure patterns. A total of 276 bank collapses were recorded, with a geohazard development density of 4.6/km. The slope gradient of 26% of the collapsed banks experienced an increase ranging from 5 to 20° after impoundment, whereas the remaining sites’ inclines remained unchanged. According to the combination of lithology and movement mode, the bank failure mode is divided into six types, which are the surface erosion type, surface collapse type, surface slide type, bedding slip type of clastic rock, toppling type of clastic rock, and cavity corrosion type of carbonate rock. It was found that the collapsed banks in the reservoir area of 85% developed in the reactivation of old landslide deposits, while 15% in the clastic and carbonate rock. This study offers guidance for the next phase of bank collapse regulations and future geohazards prevention strategies in the Baihetan Reservoir area.

Buckling failure is one of the classical types of catastrophic landslides developing on inclination -paralleled rock slopes, which is mainly governed by its self -weight, earthquake and ground water. However, nearly none of the existing studies fully consider the influence of slope self -weight, earthquake and ground water on the mechanical model of buckling failure. In this paper, based on energy equilibrium principle and elastoplastic slab theory, a thorough mechanical analysis on bucking slopes has been carried out. Furthermore, an analytical solution for slip bucking failure of rock slopes has been proposed, which fully considers the effect of slope self -weight, seismic force and hydrostatic pressure. Finally, the methodology is used to conduct comparative analysis with other analytical solutions for three practical buckling studies. The results show that the proposed approach is capable of providing a more accurate and reasonable evaluation for stability of rock slopes with potential buckling failure.

Reservoir filling in large hydropower stations triggers or accelerates deep-seated toppling deformations (DSTDs). In the absence of large failures, toppling can often last long. It is of great significance to study the mechanism of DSTDs for the prediction of affected areas before filling. Taking Yanwan (YW) DSTDs in Xiluodu Reservoir as a sample, we collected 17 scenes of ALOS/PALSAR-1 images and 45 scenes of ALOS/PARSAR-2 images and used the small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) and time-series offset tracking (TSOT) technologies to analyze the spatiotemporal evolution of YW DSTDs before and after reservoir filling. The correlation between deformation and reservoir filling was analyzed with the fluctuation in the reservoir water level. Before filling, there was no deformation in the area of YW DSTDs. After filling, the maximum deformation rate of YW DSTDs measured by TSOT during the study period was around 1500 mm/year in the line-of-sight (LOS) direction. YW DSTDs showed a fluctuating deformation trend, and the deformation rates of the four filling stages were successively from fast to slow: rapid drawdown of water level period, high water level period, low water level period, and rapid rise of water level period. The soft-hard-interbedded structure provides favorable lithology conditions for toppling. The incision of the Jinsha River and the development of gullies led to the topographic condition of exposure on three sides, shaping a convex slope similar to a triangular cone, which provides advantageous topographic conditions for toppling.

Optical remote sensing and field investigations cannot satisfy the accuracy and timeliness requirements of active landslide detection. Interferometric synthetic aperture radar (InSAR) technology has become the mainstream method for observing active landslides in recent years, due to its advantages of a large detection range and high sensitivity to surface deformation. However, quickly and accurately obtaining landslide boundaries from InSAR results is still a key issue for hazard mitigation and watershed management. In this study, first, an automatic recognition method for active landslides based on InSAR results is established to rapidly extract deformed slopes. In the recognition process, using the deformation value map as the object, the optimal threshold is determined using the image gradient edge information to extract the deformed pixels. Second, the contrast limited adaptive histogram equalization (CLAHE) algorithm is employed to improve the contrast of images with weak deformation. Last, morphological rules are applied to optimize the segmentation results to ensure that they are near the boundary of the natural landslide. To verify the effectiveness of the method, the Baihetan Reservoir area, which has frequent landslides in the lower reaches of the Jinsha River Basin, was selected as the test area. Under the conditions of ascending and descending orbits, 336 and 590 landslides, respectively, were recognized. Through unmanned aerial vehicle (UAV) and field investigations, the recognition accuracy of 76% is reached without sample training, and the maximum intersection over union (IOU) of a single landslide is increased by 0.3. This finding shows that the automatic recognition method can quickly identify dangerous active landslides at large spatial scales and with complex topographies.

Dealing with the risk of landslide-induced impulse waves is a significant challenge in the management of large reservoirs after impoundment. After the impoundment of the Baihetan Reservoir in Jinsha River, the WangJiaShan (WJS) landslide has been one of most active reservoir-induced landslides, and the Xiangbiling Community located on the opposite bank is at great risk of impulse waves. Based on the WJS landslide, five experiments in a physical model with dimensions of 30 m × 27 m × 1.5 m were conducted at a scale of 1:150. Measurements were performed with wave gauges and a particle image velocimetry system. The maximum potential sliding velocity of the natural WJS landslide was approximately 7.11 m/s, and the maximum amplitude of the generated impulse wave was 7.68 m at a water level of 825 m above sea level. The complex estuary topography of the Xiaojiang and Jinsha Rivers caused the generated waves to impact the Xiangbiling Community multiple times, with a maximum run-up of 11.75 m. This risk of impulse waves is significantly reduced when the sliding volume is decreased. As the landslide volume was reduced from 611 × 104 to 448 × 104 m3 by removing the upper part, the potential landslide velocity and the submerged sliding mass decreased, and the kinetic energy of the submerged sliding mass decreased from 2.69 × 1011 to 10.03 × 109 J, i.e., by approximately 96.3

在库水位反复升降条件下,滑坡的变形会滞后于库水位变动.滑坡变形的滞后性会随着时间发生改变,为解决滑坡变形滞后性随时间改变而导致的预测困难问题,基于溪洛渡库区雨林二组滑坡的长时间监测数据,通过相关性分析对滑坡变形滞后性随时间的变化规律展开了研究,并提出了一种考虑滑坡变形滞后性变化的位移预测方法.以白鹤滩库区王家山滑坡的变形情况对预测方法的普适性进行了验证.研究结果表明:滑坡变形的滞后性是随着时间逐渐显现的,由第2 蓄水周期的1d增加至第 5 周期的 11 d,且库水位升高时滑坡变形的滞后性变化得更为明显.验证结果表明,预测方法的准确性和普适性均较好.综合分析认为该法可为库区其他涉水滑坡的变形预测提供借鉴.

Since the initial partial impoundment of the Baihetan Hydropower Station in Jinsha River watershed in early April 2021, many landslides have started undergoing rapid deformation. The Wangjiashan landslide has been the most active and dangerous landslide in the Baihetan Reservoir area. Based on road deformation data, the overall average displacement rate of the landslide before impoundment was estimated to be in the range of 11–14 cm/year. After impoundment, particularly after the water level rose to approximately 790 m above sea level (asl) between September to October 2021, the average displacement rate at various monitoring sites ranged from 200 to 600 mm/day, and the maximum value was 1002.6 mm/day. When the water level decreased to approximately 796 m asl, the landslide displacement rate immediately decreased to a range of 2–3 mm/day. The deformation of the Wangjiashan landslide was positively correlated with the rise in water level. The landslide mechanism before impoundment was active–passive failure, and soil mass damage accumulated in the transition zone (Prandtl wedge) between the active and passive blocks. The landslide was strongly affected following reservoir impoundment, with the failure mechanism transforming into a composite active–passive impoundment-accompanied failure. Under the high water level during partial impoundment in 2021, the soil mass in the transition zone of the landslide had yielded and was already in the critical sliding failure stage. Studying the Wangjiashan landslide is significant to understand the response of reservoir-induced landslides in Jinshajiang River valley and similar reservoir-induced landslides around the world.

Impoundment and water level fluctuations in reservoirs can induce landslides, especially during initial filling and drawdown. Since the initial impoundment in April 2021, multiple landslides have occurred within the Baihetan (BHT) reservoir, which is located at the boundary of Sichuan and Yunnan province in southeast China. However, due to the complex terrain conditions of reservoir banks, traditional landslide research methods, such as surveys, deformation monitoring, and geotechnical experiments, cannot be effectively conducted in a timely manner. In recent years, the development of remote sensing technology has addressed the shortcomings of traditional landslide research methods that may not be promptly carried out. In particular, interferometric synthetic aperture radar (InSAR) technology, capable of measuring subtle deformations, and portable small unmanned aerial vehicles (UAVs) have played a significant role. This study integrates multiple remote sensing data sources, including InSAR results, optical remote sensing images, digital elevation model (DEM), and UAV imagery, to investigate and elucidate the deformation characteristics and mechanisms of the Xiaomidi (XMD) landslide developed on the left bank of Jinsha River, about 100 km from the BHT hydropower dam site. The spatial deformation distribution of the landslide before and after impoundment and the deformation time series during filling were examined. Monitoring water level variation and analysing the deformation process of the landslide were achieved by employing continuous synthetic aperture radar (SAR) intensity images and DEM. UAV photography was utilized to assist in the verification of ground deformation. The findings suggest that the weak strength of the reversed bedding strata structure and the steep slope eroded by the Jinsha River are inherent factors that contribute to the development of the landslide. The rise in the water level leads to softening of the rock mass at the slope toe, thereby directly facilitating the acceleration of landslide deformation. The toppling deformation of the lower rock mass initiates the formation of surface cracks and localized uneven subsidence in the overlying colluvial deposits.

After the first impoundment of the reservoir, many landslides seriously threatened the safety of the reservoir. Accurate determination of the relationship between the landslide deformation characteristics and water-level fluctuations is crucial. However, with the increasing number of water-level fluctuation cycles, the deformation characteristics of the landslides were also changing, and long-term continuous monitoring to capture the failure process of reservoir landslides is necessary. A large reacted landslide in the Xiluodu reservoir was set as an example, using InSAR technology to seek its variations of deformation characteristics over nine years. The local deformation rate and annual maximum deformation area variation were analyzed by InSAR technology based on Sentinel-1 descending SAR data from October 2014 to June 2022. According to the regional deformation characteristics, the landslide was divided into three zones: Zone I above the elevation of 950 m; Zone II below it; the front edge of Zone II, where the collapse happened, was further divided into Zone III. In general, the accumulated deformation in Zone I was the largest, followed by Zone III, and Zone II was the smallest. The average deformation rate of Zone II was the smallest. Zone I of NLJL was mainly affected by the drawdown of reservoir water level, and the impacts of water-level rising and drawdown on Zone II and Zone III were similar. After analyzing a nine-year variation of the deformation area, the deformation mechanism of NLJL changed from a retrogressive type to a progressive one after the first impoundment and then changed back to a retrogressive one after 2017. The impact of reservoir impoundment on NLJL was most substantial in the first three years after the first impoundment.

Reservoir landslides greatly threaten reservoir safety. Understanding the deformation characteristics and mechanism of reservoir landslides can help evaluate their stability and prevent secondary disasters. A detailed analysis of the deformation characteristics and landslide reactivation mechanism of the Wangjiashan (WJS) ancient landslide during the initial impoundment of the Baihetan Reservoir region was performed using comprehensive in situ monitoring and drilling data. The WJS landslide slowly deformed before impoundment. Reservoir impoundment was the main factor driving the intensifying deformation of the WJS landslide. The rise in reservoir water resulted in bank collapse at the landslide toe. After the reservoir water flooded the sliding zone of the landslide toe, creep deformation occurred along the deep sliding zone, which developed into overall sliding on July 7. The further rise in the reservoir water level has led to the rapid sliding of the landslide. The WJS landslide is a buoyancy weight-reducing landslide. When the reservoir water rises to a high level, the buoyancy force of the reservoir water acts on the resisting section, which reduces the resisting force and leads to the rapid sliding of the landslide. When the reservoir water level drops from the high level, the buoyancy acting on the resisting section decreases gradually, and the stability of the landslide can be restored. At present, the WJS landslide deformation rate gradually decreases with the reservoir water level, and the probability of large-scale landslides is low. However, WJS landslide monitoring needs to be strengthened to more closely study its deformation mechanism.

The deep-seated gravitational slope deformation (DSGSD) triggered by impoundment has attracted worldwide attention. After impoundment of Wudongde reservoir downstream of Jinsha River, China, Zaogutian DSGSD occurred with an estimated volume of 129 million m3, which provided an opportunity for in-depth analysis and cognition of its mechanism and risk. The DSGSD sits on a chair-shaped bedding bank slope with multi-weak interlayers at the bottom, forming a lithology structure of brittle cap overlying a ductile substratum. Based on the traditional engineering geological exploration, 3D observation of the DSGSD was carried out by UAV photogrammetry, and the cracks, discontinuities, and macroscopic deformation characteristics of different areas were identified. Combining the InSAR survey and surface-parallel flow assumption, the mm-level 3D deformation rate field and time-series displacement from December 2019 to December 2020 were reconstructed. Finally, the following conclusions were arrived at: according to the cracks and the boundary between rock mass and deposit, the DSGSD could be divided into four zones: the loose deposit near the bank, the scarp area in the front part, the major sliding area in the middle part, and the stable area. The loose deposits were deforming by uplifting with a maximum rate of 70 mm/year. The deformation rate in the western part of the major sliding area was the fastest, and the rates of the maximum settlement and southward deformation peaked at 100 mm/year and 250 mm/year, respectively, which were 2–3 times data in the scarp area. Under the soaking of reservoir water, the mechanical magnitude of the weak layer in the lower Dengying Formation and the Guanyinya Formation got reduced, which was the triggering factor of Zaogutian DSGSD. As a result, the major sliding area pushed the scrap area to creep along with the weak layer, and sliding accompanied by tensile fracturing is the instability mode in case of failure. The combination of InSAR and UAV observation provides a deeper insight into the deformation mechanism of DSGSD, which is not only conducive to slope stability evaluation but also demonstrates the role of remote sensing technology in the study of DSGSD.

该文针对金沙江库岸牛滚凼滑坡在一个库水调度周期内的稳定性变化开展研究,采用有限元分析软件(Geo-Studio),分析库水位变化、降雨及其共同作用等环境因素对堆积体岸坡稳定性的影响规律.结果表明:在一个库水位调度周期内,该滑坡最易发生滑动的时刻出现在库水位下降且伴随有降雨时,最不稳定的位置在滑坡中部;滑坡安全系数变化与库水位变化趋势基本一致,库水位不变时,降雨渗透导致滑坡坡面岩土力学参数降低、地下水位线上升从而影响滑坡的稳定性,安全系数下降;前缘滑动和整体滑动除在蓄水前的偶然工况(自然条件+地震)外,稳定性满足规范要求,但安全系数均较低,属于欠稳定性滑坡,在遇到特殊外力(如极端降雨、地震等)时有发生滑动的可能,需加强对岸坡的稳定性监测.

赋存环境直接决定滑坡的稳定性,其中库水位涨落、暴雨及地震作用是影响滑坡稳定性的关键因素.干海子滑坡体位于溪洛渡水电站库区,水库调度过程中滑坡体的稳定性是保障溪洛渡水电工程健康运营的关键.根据干海子滑坡体现场变形监测资料,运用二维极限平衡分析法,开展了滑坡体在水电站建设期、蓄水期及运营期的稳定性分析;并评价了赋存环境对干海子滑坡体稳定性的影响,以确定可能的破坏模式、规模和临界条件.研究结果表明:前缘滑坡体安全系数明显小于整体滑坡体和中部滑坡体,蓄水期间随着库水位的上升,滑坡体的安全系数减小,导致前缘塌岸的风险增加;在一个运行周期内,滑坡体的最不稳定条件为库水位下降且遇到暴雨时;当有地震作用时,前缘滑坡体失稳的可能性进一步增加.研究成果对滑坡体的变形破坏机理及稳定性的评价预测具有一定的借鉴意义.