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A large-scale obliquely inclined bedding rockslide, activated by a heavy rainstorm, occurred on July 8, 2020, at 7:05 (UTC + 8) in Shiban Village, Songtao Miao Autonomous County, Guizhou Province, China. The loss of life in this event was greatly reduced owing to the local warning system for rainstorm-induced geohazards. To understand the failure characteristics, triggering factors, the genetic mechanism of the landslide, the geomorphological features, geological characteristics, hydrological conditions, and rainfall characteristics were systematically studied by a synthetic approach including field investigations, satellite imagery, unmanned aerial vehicle (UAV) photography, laboratory tests, and rainfall data statistics. The results indicated that the interface between the soft and hard rock, the well-developed joints, and the free face in front of the slope constituted the boundaries of this landslide. The concave topography at the back and southern edge of the landslide, the bare ground, and the cataclastic structure of the rock mass provided favorable conditions for the collection or infiltration of rainwater. The concentrated rainstorm was the direct trigger for the landslide, which led to a rapid inflow and retention of rainfall in the landslide through favorable landform and geological conditions. The groundwater recharge that cannot be drained in time caused the mechanical deterioration of rock mass and induced a rapid increase in pore water pressure in the landslide. Moreover, the water level of the Ganlong River at the toe of the slope also rose rapidly, and the uplift pressure in front of the slope increased accordingly. Under the combined action of these adverse factors, the overall anti-sliding force of the slope was less than the sliding force, finally resulting in the landslide. Remarkably, the local warning system for rainstorm-induced geohazards successfully forecasted the landslide, but the shortcoming is that the forecast time in advance is short. Nevertheless, the prediction has significantly reduced human casualties and provided valuable experience for the prediction of this type of landslide. [ABSTRACT FROM AUTHOR]

Moraine soils are widely distributed in southeast Tibet of China, which poses a serious threat to local railway construction. The mechanical behavior of moraine soil containing ice in cold regions is difficult to capture under the joint action of stress conditions and temperature. To study the strength characteristics of ice-rich moraine soil, a total of 112 groups of thermal-mechanical triaxial tests under different ice forms, ice contents, and temperature conditions are carried out. The test results show that the mechanical properties of moraine soil with crushed ice and block ice are different, showing the characteristics of strain hardening and strain softening, respectively. Overall, the peak strength of moraine soil with block ice is greater than that of crushed ice. The cohesion and internal friction angle of moraine soil decreases with the temperature rise. With the increase of ice content, the peak strength of moraine soil with block ice increases, while that of crushed ice shows the opposite trend. In addition, the increase of ice content increases the cohesion of moraine soil with block ice, but there is a threshold value of ice content (25%) for moraine soil with crushed ice, which leads to the maximum cohesion at this time. Based on the test results, a unified function is proposed to describe the quantitative relationship between the strength parameters of ice-rich moraine soil, temperature, and ice content. Finally, to explore the nonlinear strength behavior of moraine soil, a binary-medium model is introduced to describe its stress-strain relationship, and the evolution of the main parameters in the model is analyzed. Comparing theoretical and experimental results demonstrates that the established model is of satisfactory applicability to simulate the mechanical behavior of moraine soil with different ice forms. • The stress-strain curve of soil containing crushed ice shows strain-hardening characteristics. • The stress-strain curve of soil containing block ice shows strain-softening characteristics. • There is an ice content threshold of moraine soil with crushed ice which makes the most effective cohesion. • The strength parameter model of moraine soil considering temperature and ice content is described. • The stress-strain relationship of ice-rich moraine soil is modeled utilizing a binary medium model. [ABSTRACT FROM AUTHOR]

On 22 October 2018, at 17:20 (UTC+8), a bedding rockslide occurred on the right bank of the Jian-En expressway in Jianshi County, Hubei Province, Central China. Fortunately, a systematic displacement monitoring network provided an early warning. Thus, no casualties were caused by the rockslide, although two transport vehicles and three houses were damaged. To deduce the triggering factors and failure mechanism of the bedding rockslide, the geological characteristics, hydrogeological characteristics, slope excavation scheme, and rainfall data statistics were systematically investigated based upon field investigations, UAV photography, geological drilling, and laboratory tests. It was found that the bedding characteristics, presence of well-developed joints, and properties of the weak interlayers were the internal factors inducing the rockslide. The free face in front of the slope, the weak interlayer with low shear strength, and the through-going joint that formed the back boundary of slope constituted the boundary of the rockslide. The combined effects of improper slope excavation and concentrated rainfall were external factors that directly triggered the rockslide. The well-developed joints provided an effective infiltration channel for rainwater, and continuous rainfall deteriorated the shear strength of the weak interlayer ultimately causing the rockslide. [ABSTRACT FROM AUTHOR]

Zigui Basin is a major landslide-prone region in the Three Gorges Reservoir region of China, and the stabilizing pile is an effective and widely employed countermeasure to reinforce landslides in this region. However, stabilizing piles are mostly designed using deterministic and stability-oriented methods, which generally ignore the system performance and cost-effectiveness. Using the Majiagou landslide reinforced with stabilizing piles as a case study, a probabilistic multi-objective optimization framework for the design of stabilizing piles is proposed and illustrated. Specifically, performance objectives related to failure probability, system robustness and life-cycle cost of the landslide-stabilizing pile system with feasible designs are evaluated, then the best compromised design is obtained by means of Pareto optimality. Expert knowledge and professional judgment are required to set necessary restrictions and finally determine the optimal design. The results show that there is a better design of stabilizing piles than the existing one, with which acceptable reinforcement effectiveness, compromised life-cycle cost and robust system performance can be realized. The optimal design will also vary with the concerned performance objectives and knowledge-based judgment. Further relationships and interpretations between design parameters and system responses are discussed through parametric analyses. [ABSTRACT FROM AUTHOR]

Variations of reservoir water level and seasonal precipitation have resulted in significant movement and destabilization of landslides in the Three Gorges Reservoir (TGR) region of China since reservoir impoundment in 2003. An example is the Baijiabao landslide, a large, actively creeping landslide located in the steep lower valley of the Xiangxi River, about 55 km upstream of the TGR dam in the Yangtze River. Twelve years of monthly monitoring at four GPS stations and routine, monthly field observations show cumulative GPS displacements as large as >1.5 m and widely developed ground cracks. GPS monitoring results show that most movement takes place in rapid steps that coincide with the rainy season and the period of annual reservoir drawdown, with particularly large steps in 2009, 2012 and 2015. This step-like pattern of displacement is also shown by daily data from an automatic monitoring system installed in 2017. The total period of acceleration shown by these daily data was about six weeks long, with rapid movement starting during rapid reservoir drawdown, and terminating when the reservoir began to rise again. In particular, most of the 2018 displacement occurred in only two weeks. Different subzones of the landslide move at different rates and exhibit different features of deformation. The neighborhood rough set theory is used to identify the triggering factors responsible for landslide deformation. The most important triggering factors vary between different sites, data types and the time interval used to define them. The surface deformation and ground crack widening are controlled by the combination of rainfall and variations in the reservoir water levels, whereas the deformation of the sliding zone is most sensitive to the latter. The results show that daily data are needed to capture important, short-term landslide responses. The neighborhood rough set theory for determination of triggering factors is suggested for deformation prediction, stability evaluation, and prevention and control of reservoir landslides in this and other regions. • Spatiotemporal deformation characteristics of Baijiabao landslide over a twelve-year period are analyzed in detail. • Different acceleration periods and threshold hydraulic conditions are observed from daily and monthly monitoring data. • Neighborhood rough set theory is used to identify the triggering factors responsible for landslide deformation. • The most important triggering factors vary between different sites, data types and the time interval used to define them. [ABSTRACT FROM AUTHOR]

The Huangtupo landslide is the volumetrically largest, most complex, and economically most significant landslide in the Three Gorges Reservoir Region. Detailed information about the character and dynamics of this tractive landslide is available because of access provided by a unique, 908-meter in situ research tunnel, augmented by long-term data provided by an extensive monitoring system for ground water levels, rainfall, and in situ deformation. Three major rupture surfaces plus 12 interlayer sliding zones (weak layers) were recognized during tunnel excavation, which also provided access and samples that establish a complex, multistage deformation history. Data from the in situ monitoring system shows that the toe of the landslide is creeping at a steady annual rate of about 25-30 mm, but at a slower rate of about 12 mm/year near the crown. However, the monthly creep rates are variable, being highest during May-August when rainfall is heavy and reservoir levels are low. Displacement-depth curves in the HZ6 borehole inclinometer establish that the creep of Slump Mass I# is most rapid above a depth of 37 m, where a major rupture zone occurs immediately above bedrock. Engineered defense structures along the toe of Huangtupo landslide can improve its stability to some extent. The effect of rapid changes of the reservoir level on landslide stability should be considered in reservoir management. [ABSTRACT FROM AUTHOR]

Huangtupo landslide, volumetrically the largest, most complex landslide in the Three Gorges Reservoir region of China, is a dangerous mass on which the district of Badong has been inadvertently situated. Risk remediation efforts include the construction of a large observational tunnel and monitoring system that are unique in the world. This tunnel and its side branches permit detailed mapping of its 3D structure while providing samples for laboratory analysis. The new investigations validate that the Huangtupo landslide is a composite of several independent landslides and that movement occurs along the major rupture zones as well as on interlayer sliding zones in the underlying Badong Formation. Uranium-thorium disequilibrium dating establishes that the northern part of the landslide, called the Riverside Slump, underwent at least two periods of movement at about 100 and 40 ka (ka stands for a thousand years). These events were induced by the steep slope created by the downcutting of the Yangzte River. The results from in situ displacement monitoring over a 7-year period confirm that the central part of the landslide is creeping at a slow, relatively stable rate of about 15 mm/year rather than being in a stage of acceleration under the protection of anchored concrete beams and other defense structures at its toe. Available data suggest that engineering measures can control the independent landslides that together constitute the huge Huangtupo mass, which will avoid the need for costly relocation of thousands of people. [ABSTRACT FROM AUTHOR]

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice. [ABSTRACT FROM AUTHOR]

Rainfall reservoir-induced landslides in the Zigui Basin, China Three Gorges Reservoir (CTGR) area, exhibit typical step-like deformation characteristics with mutation and creep states. Previous landslide displacement forecasting models yielded low prediction accuracy especially for mutational displacements. Coupled with the lack of monitoring sites and data limitations, it is extremely difficult to obtain accurate and reliable early warnings for landslides. The multi-feature fusion transfer learning (MFTL) method proposed in this paper applies the knowledge and skills obtained from the Baijiabao landslide scenario and sufficient monitoring data to improve the prediction capacity for other landslides, such as the Bazimen and Baishuihe landslides. The model barely relies on the long-time continuous monitoring process, and it can not only fill gaps in data when monitoring is interrupted, but also provide real-time displacement predictions based on accurate weather forecasting and periodic reservoir scheduling. In addition, the non-uniform weight error (NWE) evaluation method is proposed in this paper to focus more on the mutation state prediction accuracy because landslide instability is most likely to occur in this stage. Compared with other intelligent algorithms, the results indicate that the MFTL method owns low prediction error and high reliability, as well as the positive generalization ability in landslide prediction. This study paves the potential way for realizing the real-time, whole-process and accurate landslide forecasting. • An innovative model based on Transfer Learning method is firstly applied in landslide displacement prediction. • The model barely relies on long-time continuous monitoring dataset and it can fill data gaps due to monitoring interruptions. • The model is evaluated by three representative landslides with GPS monitoring datasets in Three Gorges Reservoir area, China. • The model owns low prediction error, high reliability and positive generalization ability in landslide prediction. • This study paves the potential way for realizing the real-time, whole-process and accurate landslide forecasting. [ABSTRACT FROM AUTHOR]

Five large and many small landslides are developed in Jurassic strata along the lower reaches of Xiangxi River, where interbedded weak and hard bedrock layers foster the development of landslides with a "stair-step" sliding surface. The paper investigates the evolution characteristics of these landslides and presents a novel forecasting model for their displacements. The distribution characteristics and behavior of landslides developed along Xiangxi River is revealed by the database of landslides in the larger Zigui basin, of which this area is part. Most landslides occur at rather low elevations of <300 m and in areas of moderate rainfall. The geological evolution of landslides in the Xiangxi River valley can be divided into four stages, beginning with anticline formation, followed by valley incision, then by weathering and erosion, and culminating in formation of the colluvial landslides. The accumulative displacement curves of landslides with a stair-step sliding surface in Xiangxi River region also present obvious, step-like characteristics. A novel GA-CEEMD-RF algorithm was developed to predict the displacement of these stair-step landslides, which helps to define the combination of induced factors and weak stableness of prediction results using a single displacement prediction model and the multi-field monitoring data. • The distribution characteristics of landslides developed along lower reaches of Xiangxi River was revealed. • Interbedded weak and hard bedrock layers foster the development of landslides with a "stair-step" sliding surface. • The geological evolution of landslides in the Xiangxi River valley can be divided into four stages. • A novel GA-CEEMD-RF algorithm was developed to predict the displacement of stair-step landslides. [ABSTRACT FROM AUTHOR]

• A modified Bayesian sequential updating approach is proposed to estimate GSI. • Multi-source information can be integrated in the approach. • GSI can be estimated using limited data based on various rock mass classifications. • Evaluated probabilistic characteristics of the GSI agree with the observed GSI data. • Rational result is achieved by taking model uncertainties as random normal variables. Explicit determination of geotechnical parameters is an important but difficult task in rock engineering. Paradoxically, data are often limited even though data from multiple sources (e.g., different testing procedures, testing positions, and estimation models) are commonly available, and the integration of multi-source information for the determination of geotechnical parameters in a probabilistic context remains a great challenge. We have modified an existing Bayesian sequential updating approach and used it for the first time to estimate the geological strength index (GSI) of rock masses by integrating multi-source information in a way that considers prior information, multiple estimation models and probabilistic properties of model uncertainties. The argillaceous siltstone in Zigui County in the Three Gorges Reservoir region of China was used to quantitatively illustrate this method. Three data sets using rock mass rating (RMR)-, tunneling quality index (Q)-, and rock mass index (RMi)-based estimation models were sequentially incorporated into the Bayesian sequential updating framework, through which the information on the mean value and standard deviation of the GSI can be updated. Then a large number of equivalent GSI samples were generated through Markov chain Monte Carlo (MCMC) simulation for further statistical analysis. The results showed that the proposed Bayesian sequential updating approach can provide reasonable probabilistic estimates of the GSI, which compared well with observed data using the standard GSI chart. The Bayesian sequential updating approach is a promising tool for geotechnical parameter estimation and can combine large amounts of information, thereby effectively depicting the probabilistic characteristics of GSI. [ABSTRACT FROM AUTHOR]

Rainfall represents one of the most significant cause of landslide hazards. With runoff in the saturated zone of shallow soil considered, an improved Green–Ampt model was proposed to depict the rainfall infiltration process. Furthermore, a formula for calculating the depth of wetting front was derived, and the slope stability was evaluated accordingly. Finally, a formula for calculating the safety factor of multilayer slopes above different surfaces was obtained via the limit equilibrium method. An illustration is a multilayer colluvial slope in Guangdong Province of China, the results indicated that the downward speed of wetting front decreases gradually within the same layer with continuous rainfall but increases significantly when it extends to the interfaces between the soil layers. Safety factor of the slope decreases sharply at the initial stage of rainfall but then more slowly. Slope failure is more likely to occur along the first interface between the soil layers than along the surface of wetting front. The result from the proposed model is closer to the result of the physical model test than from the existed Green–Ampt model. The improved Green–Ampt model for multilayer slopes can accurately evaluate the infiltration process and related stability of multilayer slopes under rainfall. [ABSTRACT FROM AUTHOR]

The Zigui basin is one of the most landslide-prone areas where thousands of landslides are distributed. Performing the landslide susceptibility mapping is a heated issue in the area. Whereas, whether the random selection of the landslides or non-landslides grid cells are reasonable in research of landslide susceptibility mapping is the existence problem nowadays. Based on the two step cluster (TSC) algorithm and the self-organizing mapping - random forest (SOM-RF) model, a novel hybrid model is proposed to overcome the above drawbacks in this study. Rough set (RS) theory is employed as an attribute reduction tool to identify the significant influencing factors of landslides, and SOM is used to produce a preliminary landslide susceptibility mapping. Two step cluster algorithm is applied in telling apart the most reasonable True-Positive (TP) from landslide grid cells in high-susceptibility zones and the False-Positive (FP) in low-susceptibility zones. Afterwards, the TP and FP are imported into the RF model as samples to produce the improved landslide susceptibility mapping. The proposed method is utilized to develop landslide susceptibility mapping in the Zigui basin. Compared with the susceptibility mapping produced by RF model, which selects landslides and non-landslides grid cells randomly, the results of TSC and SOM-RF model demonstrates to have the superior prediction skill and higher reliability in landslides susceptibility mapping. [ABSTRACT FROM AUTHOR]

Monitoring data show that many landslides in the Three Gorges region, China, undergo step-like displacements in response to the managed, quasi-sinusoidal annual variations in reservoir level. This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide, with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level. Most motion terminates when the water level rises back above the critical level, so the annual step size is the time integral of the instantaneous displacement rate. These responses are incorporated into a differential equation that is easily calibrated with monitoring data, allowing prediction of landslide movement from actual or anticipated reservoir level changes. Model successes include (1) initiation and termination of the annual sliding steps at the critical reservoir level, producing a series of steps; (2) prediction of variable step size, year to year; and (3) approximate prediction of the shape and size of each annual step. Annual rainfall correlates poorly with step size, probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir. Viscous landslide behavior is suggested. [ABSTRACT FROM AUTHOR]