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Landslide is one of the most destructive geohazards around the world. The destruction of a landslide can be estimated from its deformation and runout behaviors, which might be simulated with numerical software such as particle flow code (PFC). In the PFC simulation of the runout behavior of a landslide, the results are dependent upon the particle‐particle contact micro‐parameters (e.g., contact modulus and friction coefficient). The calibration of the micro‐parameters of the PFC models is a challenge and various uncertainties are involved. The uncertainty in the selection of the modelling parameters leads to uncertainty in the simulated runout behavior of the landslide. As a result, the damage potential of the landslide cannot be accurately evaluated. To this end, this paper presents a probabilistic analysis of the runout behavior of the Jiweishan landslide, in which the uncertainty in the selection of the particle‐particle contact micro‐parameters is explicitly considered. Here, the input micro‐parameters are modeled as discrete random variables, the possible realizations of which are obtained through an orthogonal analysis of the PFC simulation‐based uniaxial compression tests. The derived possible realizations of the micro‐parameters are then adopted as the inputs to the built model of the landslide. With the results obtained from these PFC simulations, the runout behaviors of the Jiweishan landslide, such as the runout distance and the deposit thickness, are studied probabilistically. The results provide an improved estimate of the landslide runout behavior and further aid in making an informed risk assessment of the landslide. [ABSTRACT FROM AUTHOR]

[Display omitted] • Centrifuge modelling as an important tool in the understanding of deformation and failure behaviours of landslides. • Various mitigation methods modelled and verified through centrifuge modelling. • Seven important aspects suggested for future research on centrifuge modelling for landslides. Landslides are serious geohazards that occur under a variety of climatic conditions and can cause many casualties and significant economic losses. Centrifuge modelling, as a representative type of physical modelling, provides a realistic simulation of the stress level in a small-scale model and has been applied over the last 50 years to develop a better understanding of landslides. With recent developments in this technology, the application of centrifuge modelling in landslide science has significantly increased. Here, we present an overview of physical models that can capture landslide processes during centrifuge modelling. This review focuses on (i) the experimental principles and considerations, (ii) landslide models subjected to various triggering factors, including centrifugal acceleration, rainfall, earthquakes, water level changes, thawing permafrost, excavation, external loading and miscellaneous conditions, and (iii) different methods for mitigating landslides modelled in centrifuge, such as the application of nails, piles, geotextiles, vegetation, etc. The behaviors of all the centrifuge models are discussed, with emphasis on the deformation and failure mechanisms and experimental techniques. Based on this review, we provide a best-practice methodology for preparing a centrifuge landslide test and propose further efforts in terms of the seven aspects of model materials, testing design and equipment, measurement methods, scaling laws, full-scale test applications, landslide early warning, and 3D modelling to better understand the complex behaviour of landslides. [ABSTRACT FROM AUTHOR]

Landslide displacement monitoring plays a fundamental role in the study of landslide evolution mechanisms, forecasting, risk assessment, prevention, and control. To fill the deficiencies of traditional instrumentation for measuring landslide displacement distributed along lateral direction, a landslide displacement measurement method based on deformation-coupled pipeline trajectory measurement is proposed, and a pipeline trajectory inertial measurement instrument is developed. The developed instrument, primarily comprised of a single shaft gyro, two axis accelerometers, and an external roller encoder, is designed as an axial half strapdown-radial half platform structure combined with a mechanical gravity platform. This structure avoids the singularity of pitch angle and roll angle and can expediently calculate a pipeline trajectory with an Eulerian transformation when obtaining several basic physical variables, e.g., the axial linear velocity, pitch angle, roll angle, and azimuth angle. Additionally, the pipeline trajectory, measured at different times, possesses the ability to reflect the displacement evolution feature of landslides. The results of prototype simulation tests imply a single measurement accuracy of a 12 cm/100 m span and a singly periodic multiple (more than five times) measurement accuracy of a 3 cm/100 m span, which meets medium-precision displacement measurement requirements for a landslide. Additionally, the finished instrument has been successfully applied to the deformation monitoring of the Majiagou I# landslide, which further verifies its feasibility and offers a reference for similar landslides. [ABSTRACT FROM AUTHOR]

The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges. [ABSTRACT FROM AUTHOR]

Toppling is a widespread failure mode in natural and excavated anaclinal rock slopes and threatens the construction and operation of important infrastructure such as hydropower and transportation corridors in southwest China. Therefore, comprehending the mechanisms underlying toppling is essential for relevant landslide prediction and prevention. In this study, we developed a physical model of an anaclinal rock slope using the Linda landslide as a site case. Based on field monitoring using Gopro cameras, high-speed cameras, strain sensors, stress sensors, and acoustic emission systems, the evolution of the anaclinal rock slope model was analysed. The reliability of the physical model test was verified by comparing the deformation characteristics of the landslide site with those of the slope model. The monitoring results suggested that the slope deformation process could be divided into five stages: an initial interlayer dislocation stage, a deformation initiation stage, a crack generation and propagation stage, a crack penetration stage, and a failure stage. The rock mass could be categorised into four deformation zones: the fresh zone, weakly toppled zone, moderately toppled zone and highly toppled zone. Acoustic emission sensors deployed near the failure-prone region held the potential to serve as early warning indicators for landslides on anaclinal rock slopes. Valley incision played a significant role in the formation of sliding surfaces in anaclinal rock slopes. Furthermore, the model experiment observed multiple failure surfaces and the failure behaviour of the landslide rock mass, shedding light on their connection to the deformation process and the stress state in the model. The study revealed the deformation and failure mechanism of an anaclinal rock slope from many perspectives and deepened our understanding of toppling type of landslides. • Detailed study of Linda landslide's background and structure. • In-depth analysis of anaclinal rock slope using multi-field monitoring. • Summarized complex anaclinal rock slope failure mode and rock mass features. • Emphasized valley incision's role in forming sliding surfaces on anaclinal slopes. • Revealed the generation of multiple failure surfaces during toppling deformation. [ABSTRACT FROM AUTHOR]

The behavior of stabilizing piles with different embedded lengths in bedrock with a hard upper layer and a weak lower layer is examined using physical model tests. Based on the geological model of the landslide with bedrock with a hard upper layer and a weak lower layer, the pile–landslide physical model is developed and includes the data collection system, the pile–landslide model and the loading system. The real-time deformation and moment of the stabilizing pile is obtained in the process of loading at the trailing edge of the landslide for different percentages of hard bedrock. The test results indicate that the percentage of hard bedrock has a strong influence on the deformation and moment of the stabilizing pile embedded in bedrock with a hard upper layer and a weak lower layer. A higher percentage of hard bedrock can result in a greater maximum bending moment of the stabilizing pile and a smaller displacement at the stabilizing pile head, and vice versa. The results also show that 40% of hard bedrock can be considered the critical condition in bedrock with a hard upper layer and a weak lower layer. The percentage of hard bedrock has a limited influence on the deformation of the stabilizing pile when the percentage is greater than 40%. Moreover, four tests with stabilizing piles with different embedded lengths are conducted to investigate the most suitable embedded length in the bedrock with a hard upper layer and a weak lower layer. The results indicate that 28.5 cm is a reasonable embedded length for the stabilizing pile in the two-layered bedrock with a hard rock percentage of 20%. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) technique has been widely utilized in geohazards assessment. In this work, the effectiveness of the UAV photogrammetry in the remote sensing and assessment of the landslide behavior is demonstrated through a case study of a landslide that occurred in Guizhou, China on 10 June 2018. The post-landslide assessments were conducted through a field investigation by a team of experts; and, two UAV photogrammetry-based surveys were carried out. On the basis of a detailed inspection of the high-resolution aerial photographs collected from the UAV photogrammetry and historical satellite imagery, subsurface stratigraphic configuration revealed from borehole explorations and the local rainfall data collected, the failure mechanism of this landslide is investigated. The occurrence of this landslide is probably attributed to the combined influence of the long-term rainfall and the engineering activities at this site. An automatic landslide cracks recognition model, which is based on the deep learning-based image recognition technique of RetinaNet, is further developed to map the cracks at the landslide site. The effectiveness of this automatic crack recognition model is validated through quantitative comparisons between the landslide cracks recognized and the field survey results. Based upon the landslide cracks identified on two different dates after this landslide event (by the field survey and automatic crack recognition model) and the subsurface displacement revealed from a drilled hole, the evolution behavior of this landslide is analyzed. The results show that the stability of this landslide was not achieved during the first slide in June 2018 and the limit of the landslide was increased much from June to November 2018. In such a situation, the elements at risk in the zones that are potentially impacted by this landslide are identified (with the aid of the UAV images collected), and a preliminary consequence assessment is conducted. • The failure mechanism of a landslide in Guizhou, China is investigated. • An automatic landslide cracks recognition model is developed using RetinaNet. • The landslide behavior is studied based on the cracks recognized on two dates. • A UAV images-based preliminary assessment of the landslide consequence is made. [ABSTRACT FROM AUTHOR]

Effective detection of groundwater in landslide areas is crucial for landslide monitoring. The magnetic resonance sounding (MRS) method is a geophysical technique that can directly detect groundwater. However, its application in landslide groundwater monitoring is often hindered by the complex topography of landslides. This paper presents an MRS framework that accounts for the impact of landslide topography on groundwater detection. The framework includes the use of drone photogrammetry to obtain high-precision topography data and real-time kinematic equipment to acquire the coordinates of key points along the MRS coil. A three-dimensional finite element model is then constructed to calculate the alternating magnetic field generated by the MRS coil and the kernel function required for MRS inversion. Two typical landslide topography models—terrace and gully—are constructed and analyzed using numerical simulations. The results indicate that landslide topography significantly impacts the quality of groundwater information retrieved by MRS in both one-dimensional and two-dimensional inversions, affecting the accurate detection and identification of infiltration surfaces, sliding zone water, and bedrock surfaces. If accurate topography data are obtained and appropriate kernel functions are calculated, the accuracy of the inversion can be greatly improved. An example of using the MRS method to detect groundwater in a complex landslide in the Three Gorges Reservoir area of China is then presented. The MRS inversion results that consider landslide topography are confirmed using borehole data, electrical resistivity tomography detection results, and on-site drainage tunnel investigations. The water-content distribution inside the landslide body is obtained, providing information helpful to define sliding zones in multi-level landslides. The proposed landslide MRS detection framework makes it possible to apply MRS methods to the detection of landslide groundwater distribution, analysis of seepage fields, and discrimination of landslide geological structures. • Landslide topography significantly affects the quality of groundwater information retrieved by MRS. • Obtaining accurate topography data and calculating appropriate kernel functions improve MRS inversion results. • MRS field work on a complex landslide is validated by borehole data, ERT, and on-site drainage tunnel investigations. [ABSTRACT FROM AUTHOR]

Landslide early warning systems can be implemented based on the monitoring and prediction of landslide displacements. The internal mechanisms of landslides are very complex, and precise mechanistic models of landslides are difficult to obtain; therefore, data-driven models are usually applied. From the perspective of dynamic system theory, landslide development should be considered a dynamic process. Because traditional models, such as feed-forward neural networks, can only express static relationships, the applicability of these static models is quite limited in landslide prediction tasks. In our study, recurrent neural networks are established and trained into dynamic predictors of landslide displacement using a training algorithm named reservoir computing. A shortcoming of the reservoir computing algorithm is that the models will not perform well with insufficiently large training sets. Because practical landslide displacement recordings are usually quite short, interpolation techniques are employed to enhance the training of the dynamic predictors. The method proposed in this paper is applied to predict the developments of three landslides in the Three Gorges area, and the predicted values are close to the actual measurements. Improvements relative to sophisticated static predictors and basic dynamic predictors are obvious. [ABSTRACT FROM AUTHOR]

The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle <47°, while >80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities. • Database of 462 reservoir-induced landslides in Zigui basin, TGR was created. • Identifying failure modes and quantifying contributions of influential factors. • Conditional probability and information entropy model yields landslide susceptibility. • Susceptibility map, failure modes, and strategies suppressing the slope instability. [ABSTRACT FROM AUTHOR]