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Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

The reactivation of ancient landslides, a persistent issue that hinders urban development and threatens human safety, is the central focus of this paper. Specifically, this paper offers a detailed description and analysis of an ancient landslide that reactivated on February 26, 2021, in Guoye town, Zhouqu County, Gansu Province, China, and has remained slow-moving since then. With a volume of approximately 21.2 million m3 of rock and soil masses involved, this landslide poses a significant risk to the safety of 1119 individuals from 282 households in Moli village and Guoye village. A combination of field investigations, remote sensing interpretation, in situ monitoring, and interferometric synthetic aperture radar (InSAR) was used to investigate the landslide characteristics and reactivation mechanisms. The results revealed that the presence of sliding-prone rock formations and tectonic movement were the primary contributors to the formation of the Moli landslide. Furthermore, the accumulation of bedrock blocks in the quarry and collapsed rock masses caused by fault activity at the rear parts, along with the additional load from buildings on the middle and rear sections, increased the sliding force. Simultaneously, the erosion of the Dashuiba River resulted in a reduction in the anti-slip force, thereby directly facilitating landslide reactivation. The notable increase in annual rainfall in recent years has played a crucial role in ancient landslide reactivation by elevating the groundwater level and softening the shear strength of the sliding zone soil. The findings of this study contribute to an enhanced understanding of the reactivation mechanisms in giant, ancient landslides situated within areas characterized by highly complex geological conditions. [ABSTRACT FROM AUTHOR]

In this paper, for the engineering problem of tunnel deformation damage caused by a tunnel–landslide orthogonal system landslide in northwest China, based on similar theory, a variety of materials and their ratios were explored to make similar models according to the physical and mechanical parameters of the site engineering rock, indoor physical model tests were constructed, and monitoring techniques, such as speckle displacement strain measurement, strain acquisition, and pressure sensors, were used to test the macroscopic deformation characteristics; through the detailed analysis and law summary of the test macroscopic deformation characteristics, displacement, anchor cable axial force, and strain data, it was finally found that under the orthogonal system of tunnel and sliding surface, the damage form of tunnel lining is mainly compression damage and shear damage, and the cracks are mainly tension cracks, and the effect of tunnel primary lining on the surrounding rock support is extremely limited under the action of huge sliding force; combined with the analysis of the model deformation characteristics and the axial force curve of the anchor cable during loading, it was found that the axial force change curve is consistent with the NPR Newton force curve can be used to evaluate slope stability and predict the occurrence of landslide disasters. Highlights: This paper focuses on the tunnel and surrounding rock damage evolution characteristics under tunnel–landslide orthogonal and tunnel crossing sliding surface conditions. The evolution of displacement field, NPR anchor cable axial force, and strain field were revealed using indoor large similar physical model test. The mechanism of tunnel–landslide interaction within the slope and the deformation and damage characteristics of the tunnel surrounding rock were analyzed to reveal the slope deformation law under different loads. The stress change law of constant-resistance large deformation anchor cable (NPR anchor cable) is explored, and a landslide warning mechanism based on constant-resistance large deformation anchor cable is established. [ABSTRACT FROM AUTHOR]

The dynamic effect is a very important issue widely debated by scholars when studying the genetic and disaster-causing mechanisms of earthquake-triggered landslides. First, the dynamic effect mechanism and phenomena of earthquake-triggered landslides were summarized in this paper. Then, the primary types of dynamic effects were further used to interpret the Mogangling landslide in Moxi Town of Luding County, China. A field investigation, remote sensing, numerical calculation and theoretical analysis were carried out to illustrate the failure mechanism of slope rock masses affected by earthquakes. The interaction between seismic waves and slope rock masses and the induced dynamic effect of slope rock masses were primarily accounted for in the analysis. The slope topography, rock mass weathering and unloading characteristics, river erosion, regional seismogenic structure, and rock mass structure characteristics were also discussed. The results showed that the formation of the Mogangling landslide was mainly related to the high amplification effect of seismic acceleration and back slope effects, interface dynamic stress effects, and double-sided slope effects of seismic waves caused by the catastrophic Ms 7.75 Moxi Earthquake in 1786. The principles for the site and route selection of large-scale infrastructure in the planning stage and the scientific prevention of seismic geological disasters were proposed on the basis of the dynamic effect of earthquake-induced landslides. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

The collection of relevant information about the vulnerability of infrastructure damaged by landslides is not an easy task due to the existence of several compounding factors and uncertainties. This makes it difficult to quantitatively estimate their vulnerability to slow-moving landslides. This paper presents a new vulnerability assessment model for masonry buildings on slow-moving landslides based on physical models and field observations. A masonry building model is made of brick and concrete at a scale of 1:10 to physically simulate the damage in structures caused by ground tension cracks commonly developed on slow-moving landslides. The tension crack opening process is simulated through a load-controlled table with an aperture on which the building model is constructed. The strain on the wall and its foundation were measured, and the damage of the model (crack formation and evolution for each loading step) was collected, described, and analyzed. These data were used to develop failure criteria for masonry buildings in rural areas in China in terms of a quantitative vulnerability curve. The quantitative model of vulnerability for masonry structures was established based on fuzzy mathematics and the Weibull function applied on the test data and observations. The vulnerability curve is verified with field cases of masonry buildings damaged by ground tension cracks associated with slow-moving landslides in the Three Gorges Reservoir area. The results support further testing and use of vulnerability curve proposed. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [ABSTRACT FROM AUTHOR]

Landslide failure near a reservoir generates impulse waves, greatly increasing casualties and property losses. This paper presents a field investigation and potential impulse wave simulation analysis of the Wulipo (WLP) landslide, which deformed after the initial impoundment of the Baihetan Hydropower Station, China. According to geological surveys, the WLP landslide is a weak base-type, weathered, fragmented rockslide with soft rock in the lower portion and hard rock in the upper portion. The deformation and failure mechanism under the action of reservoir water consist of compression, fracturing, and shearing. The periodic fluctuation of reservoir water may cause overall high-speed slip failure of the WLP landslide. According to the inferred failure mode of the WLP landslide, the impulse waves caused by the overall failure of the landslide at the highest (825 m) and lowest (765 m) water levels are predicted. Due to the significant height difference between the center of gravity of the landslide and the reservoir water level, the wave height and fluctuation degree of the overall failure of the WLP landslide for 765 m are significantly greater than those for 825 m. However, the wave flood area for 825 m is larger than that for 765 m. Although the impulse waves triggered by the overall landslide at either water level will not destroy the nearby Menggu community, at 765 m, impulse waves will overflow the bridge deck of Ganhu Road. Therefore, engineering measures such as reducing the landslide volume can lower the secondary disaster risk posed by WLP landslides. This study provides insights into landslide disaster risk in the Baihetan Reservoir region and similar areas worldwide. [ABSTRACT FROM AUTHOR]

Landslides are one of the main geological disasters in the riparian zone of the Three Gorges Reservoir Area (TGRA) in China, causing massive losses in terms of industrial and agricultural production and people's lives and property. Due to the difficulty of data collection and lack of facade information, the remote sensing methods currently used for landslide mapping and detection are insufficient. The current landslide information acquisition method is mainly based on visual interpretation and automatic computer classification from aerial orthophotographs, which is subjective and time-consuming and yields low accuracy. New shipborne photogrammetry has unique advantages in small and steep slope landslide detection. In recent years, the deep learning technology represented by convolutional neural networks (CNNs) exhibits more powerful feature learning ability in image recognition than traditional machine learning technology. Meanwhile, transfer learning can make the network suitable for small datasets through fine-tuning, which could transfer the parameters of the pretrained model to the new model in a certain way, thereby increasing the learning efficiency of the model. In this paper, the training of multiple deep learning models is realized through transfer learning, and automatic identification of landslides based on shipborne images is achieved through decision-level fusion. The experimental results show that through the integration of decision-level fusion and transfer learning, it performs well in the classification of landslides based on shipborne images. [ABSTRACT FROM AUTHOR]

China has planned and built several world-class cascade high dams in Jinsha River, Dadu River, Lancang River, and Yarlung Zangbo River. The complex geological conditions in the reservoir area and numerous large-scale landslide bodies make the potential disaster risk of overflowing and cascade dam failure caused by landslide-generated tsunami under increasing severe situations. However, the study on describing and predicting the complex dynamic processes of generation, propagation, overflowing, wave setup, and the interaction between tsunami and lakeshore has not been systematically carried out. Based on the high-order Boussinesq-type equations, the development of the dynamic system of tsunamis in lakes coupled with the landslide process is realized using the finite volume method in this paper. To verify the accuracy and reliability of the study, the Xiluodu Reservoir is selected as the object to simulate the potential landslide-generated tsunamis. The factors such as the generation and propagation of tsunamis, dam overflowing, and wave setup in the downstream river are quantitatively evaluated and analyzed. The constructed landslide with a total volume of 24×106 m3 generates a near-field wave amplitude of about 28 m. The maximum wave run-up height is about 95 m, the volume of the dam overflowing water up to 2.13×106 m3, and the maximum wave height above the dam crest presents an M-shaped distribution. This LGWs event raises the downstream water level by nearly 40 m. The results show that the risk of landslide-generated tsunamis in the reservoir area in China cannot be ignored. The developed Boussinesq-type equations coupled with the landslide dynamics can simulate the whole process of generation, propagation, runup, and estimating the overflowing water volume of the tsunamis in the lake, laying a foundation for the quantitative risk assessment of tsunamis in lakes of high cascade dams in China. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

The Jinsha River flows through one of the most geologically complex regions in western China with extremely high altitudes and capricious climates. Frequent landslides (e.g., the Baige landslides on 11 October and 3 November 2018) occurred along its stretch which posed severe damage to bridges, dams, and roads, and put the safety of local residents at risk. Systematic and comprehensive landslide detection has been rarely carried out in the Jinsha River corridor because it is a wide and hard-to-reach region. With 30 years of development, InSAR has become an effective way to map ground motion, which in turn makes it feasible to detect active landslides over wide regions. However, several challenges remain in applying InSAR in the Jinsha River corridor, such as low coherence due to dense vegetation and/or large gradient surface movements, and atmospheric effects due to the spatio-temporal variations of the atmosphere (especially the part due to tropospheric water vapor). In this paper, we propose a framework to overcome the limitations of conventional InSAR through the integration of GACOS-assisted InSAR, advanced SBAS InSAR, and SAR pixel offset tracking techniques. This integrated framework enables us to detect active landslides under complex topographic and climate conditions. Our results show that the detected active landslides are largely controlled by active faults, most of them are found at elevations of 2500–4000 m, their slopes fall between 25 and 45 ∘ facing southwest and northwest, and 83.3% of the active landslides have a NDVI value of less than 0.3. Furthermore, precipitation is one critical triggering factor of active landslides, evidenced by the high temporal correlation between average precipitation and surface displacement time series. [ABSTRACT FROM AUTHOR]

A hybrid framework by integrating stacking ensemble with two deep learning methods of convolutional neural network (CNN) and recurrent neural network (RNN) is introduced in this paper for landslide spatial prediction in the Three Gorges Reservoir area, China. The proposed framework is summarized in following steps. First, a spatial database consists of 20 landslide conditioning factors and 196 landslide polygons was established. Then, landslide and non-landslide pixels were randomly divided into training (70% of the total) and test (30%) sets. Next, a stacking ensemble method that integrates CNN and RNN was constructed using the training set. Finally, the proposed stacking framework was applied for landslide susceptibility mapping and evaluated. Experimental results demonstrated that the proposed framework can obtain the best predictive capability (0.918) than CNN (0.904), RNN (0.900) and logistic regression (0.877) in terms of area under the receiver operating characteristic curve (AUC). Therefore, it can be useful for landslide disaster management and assessment. [ABSTRACT FROM AUTHOR]

As the global climate warms, the frequency of soil slope failures induced by snowmelt is gradually increasing, especially loess landslide disasters in seasonally frozen areas. Landslide disasters in seasonally frozen areas pose a serious threat to human lives and engineering constructions and are gradually drawing greater public attention around the world. However, the mechanism that regulates deformation and failure processes, resulting in snowmelt-driven landslides, remains elusive. The Yili River Valley in Xinjiang, China, is an ideal location for exploring the mechanism of snowmelt-driven landslides. Based on detailed field surveys, remote sensing image identification, meteorological data analysis and loess characteristic tests, this paper takes a representative high-level loess landslide in north-west China as a case in order to explore its evolution history, movement process and resurrection mechanism. It was found that this loess slope had suffered two large-scale sliding failures and different degrees of inherited slope deformation had been found between the two slope failures. The 32 surface cracks identified have a total length of 3,505.10 m, and the total area of the landslide was found to be 135,462m2. The average thickness of the sliding body was about 30 m, and the volume was approximately 504,000 m3. The actual movement time lasted for 32 s with an average moving speed of about 15 m/s. Human grazing activities dominate the formation and development of slope surface cracks in the early stage. The strong water sensitivity of distinctive loess controls soil strength deterioration in the slip zone. Rapid snow removal and infiltration, driven by an abnormal temperature rise in Spring, is the most important triggering factor for slope deformation, evolution and failure. As regional grazing activities increase and global warming intensifies, the potential for resurrection landslides will increase. The results provide essential information for a comprehensive understanding of early warning systems and risk assessment for snowmelt-triggered landslides in cold areas. [ABSTRACT FROM AUTHOR]

On 5 April 2021, a high-position landslide occurred in Tiejiangwan, Liujiang Town of Hongya County, Sichuan Province, China. The detached mass transformed into a debris avalanche traveled about 1300 m with an elevation difference of 665 m and caused three fatalities. Then landslide deposits were initiated by continuous rainfall and evolved to a debris flow five days later, forming a secondary disaster chain. The rapid characterization of a landslide-debris flow chain (LDC) is a critical step in remediation works and future early warning of failure. This paper proposes a rapid disaster information integration and assessment method for providing information on LDC occurrence, failure mechanisms, and potential hazard-affected areas via examining and characterizing the Tiejiangwan LDC based on multi-method including geomorphological mapping, structural characterization, kinematic analysis and numerical simulation. The results suggested that the first landslide initiated from a mountain ridge that experienced historical deformation extended over the fractured argillaceous siltstones. Constrained by three discontinuity sets, the failure mode was determined as planar failure along a sub-vertical defect. The cause of first landslide can be attributed to a jointed effect of structural plane, tectonic activity, terrain and slope structure, while rainfall is the dominating triggering factor. Deposits with loose structure subjected to persistent rainfall and gully topography contribute to the occurrence of secondary debris flow. Using UAV and LiDAR identification, the unstable elements in the surrounding cliffs of the source area are derived and a three-dimensional simulation was implemented to detect the energy, velocity, height and reach of detached blocks. The proposed method leads to rapid characterization of LDC and assists the evaluation of cliff instability of these environments. Highlights: We proposed a rapid and integrated assessment system for landslide-debris flow chain occurrence, failure mechanisms, and potential hazard-affected areas identification. Quantitative study was conducted on the evolution from landslide deposits to debris flow. 3D simulation was performed to reveal the runout behaviors of surrounding potential dangerous rockfall areas. [ABSTRACT FROM AUTHOR]

This paper presents a recent rainfall-induced reactivated compound soil slide in Yanyuan County, China, on July 19, 2018. In this paper, according to the elaborate investigation of the soil slide area at field and prefailure slope stability analysis, the corresponding failure mechanisms are comprehensively studied. Unmanned aerial vehicle (UAV) based on remote sensing, three-dimensional modelling, in situ and laboratory experimental testing, borehole exploration, and numerical simulation were carried out in this work. According to the results, two different sliding processes were identified involving the main sliding and lateral collapse. The main sliding occurred as multiple stages along with two different types of slip zones, which involve a preexisting basal zone of the ancient landslide and an interlaminar shear zone within the ancient landslide deposits. The predominance of reactivated surface geomorphology provides a clue to elucidate the dominant compound movement of the event. Unconsolidated sliding mass and the basal sliding surface are ideal for the development of the reactivation motion. Saltatory microrelief of the original slope determined the retrogressive failure type of the soil slide. The well-developed groundwater in the landslide area significantly increased the soil mass saturation, with an average saturation value of 98%, formed dominant seepage in the sliding mass, and promoted landslide reactivation. Furthermore, rainwater infiltration due to 14 days' antecedent cumulative rainfall of 350.6 mm directly contributed to the occurrence of the landslide by increasing the pore water pressure in the slope. The maximum shear plastic strain and displacement obtained by numerical simulation show that the lateral slope was unstable with the removal of the main sliding. Because the soil slide is located in the west area of the Emeishan Large Igneous Province (ELIP), it is a partial resurrection of the ancient basalt landslide. Therefore, this study has a certain significance for revealing the reactivated mechanism of basalt ancient landslide. [ABSTRACT FROM AUTHOR]

Based on an analysis of the formation mechanism, stability state and development trend, this paper focuses on monitoring key indicators, such as surface deformation and rainfall, to realize the early warning of landslides and provide some guidance for landslide disaster prevention and avoidance in the alpine mountainous area of Nangqian, Qinghai Province. The landslide of the Baima Elin Monastery has the topographic and geomorphological conditions of landslide formation and has the basic characteristics indicating that it can be influenced by rainfall and climate, so a landslide warning should be comprehensive. At the same time, the landslide is currently in an unstable state, and the seasonal freeze–thaw cycle will cause it to become more unstable. Based on macroscopic signs, it is predicted that the deformation of the Baima Elin Monastery landslide will continue to intensify without abatement, the stability will be further reduced after winter, developing in an unfavorable direction, and engineering measures should be taken as soon as possible to manage the landslide. [ABSTRACT FROM AUTHOR]

On 24 June 2017, a rockslide-debris avalanche occurred in Xinmo Village, Maoxian County, Sichuan Province, China. The displaced materials traveled approximately 2.6 km and caused 83 casualties. Combined with the field investigation, seismic signal, and numerical simulation, the dynamic process and deposit characteristics of the Xinmo landslide were analyzed. Based on the Arias intensity and Hibert time–frequency analysis of the seismic signal, the Xinmo landslide is determined to have a total duration of 120 s and with three-stage dynamic processes, namely, the initial accelerating stage (50 s), erosion impact stage (50 s), and accumulation stage (20 s). Then, taking the field investigation results, the total duration, and kinetic parameters inverted from the seismic signal as criteria, the DAN3D (Dynamic Analysis of Landslides) was employed to reconstruct the dynamic process of the Xinmo landslide. The results show that the erosion and deposit depth distributions obtained from the simulation are in agreement with the pre- and post-landslide DEM (digital elevation model) data and field results. Further, the three-stage dynamic processes (40 s, 60 s, 20 s) obtained from the numerical simulation are also close to the seismic analysis results. In this study, the results of the field investigation and seismic analysis provide a more detailed set of inversion criteria for the numerical simulation work, which contributes to reconstructing the dynamic processes of the Xinmo landslide more accurately. Based on the approach of multi-source data collaboration in this paper, those landslides that lack direct observation data can be better understood and the numerical parameters used for landslide dynamics inversion are better calibrated, which can be applied to predict the runout behavior and disaster scope of potential landslides in the immediate area. [ABSTRACT FROM AUTHOR]

Based on field geological survey and unmanned aerial vehicle (UAV) photography, this paper studied the inherent causes, intrinsic mechanisms, and kinematic characteristics of a catastrophic landslide of dissolved and fractured rock slope in a mountainous area of Southwestern China. The discrete element method (DEM) model of representative volume element of fractured rock mass considering karst existence was developed with its micromechanical parameters calibrated from laboratory element tests. The coupled finite difference and discrete element methods (FDM-DEM) were then employed to simulate deformation and failure evolution and collapse development of the rock slope with both internal and externally triggering factors properly addressed. The kinematic characteristics of mobile collapse debris flow were analyzed, and the numerical simulation results were validated by laboratory physical model test. The results show that the internal causes were mainly manifested in slope structure, lithology combination, karst, and fracture development, among which the unfavorable interaction disintegrated rock masses. The primary external cause was the staged underground coal-mining operations underneath the collapsed body, which led to large cracks appearing at the back edges of the slope. The maximum velocity of mobile collapse debris was about 65 m/s with the maximum travel distance of more than 600 m. Numerical simulation results matched well with both field forensic investigation and laboratory physical model test results. The findings would help further understand the deformation and failure process of fractured rock slope subject to underground mining and provide technical reference for accurate assessment and proper mitigation of similar landslide disasters. [ABSTRACT FROM AUTHOR]

This paper presents a study on an ancient river-damming landslide in the SE Tibet Plateau, China, with a focus on time-dependent gravitational creep leading to slope failure associated with progressive fragmentation during motion. Field investigation shows that the landslide, with an estimated volume of 4.9 × 107 m3, is a translational toe buckling slide. Outcrops of landslide deposits, buckling, toe shear, residual landslide dam, and lacustrine sediments are distributed at the slope base. The landslide deposits formed a landslide dam over 60 m high and at one time blocked the Jinsha River. Optically stimulated luminescence dating for the lacustrine sediments indicates that the landslide occurred at least 2,600 years ago. To investigate the progressive evolution and failure behavior of the landslide, numerical simulations using the distinct element method are conducted. The results show that the evolution of the landslide could be divided into three stages: a time-dependent gravitational creep process, rapid failure, and granular flow deposition. It probably began as a long-term gravitationally induced buckling of amphibolite rock slabs along a weak interlayer composed of mica schist which was followed by progressive fragmentation during flow-like motion, evolving into a flow-like movement, which deposited sediments in the river valley. According to numerical modeling results, the rapid failure stage lasted 35 s from the onset of sudden failure to final deposition, with an estimated maximum movement rate of 26.8 m/s. The simulated topography is close to the post-landslide topography. Based on field investigation and numerical simulation, it can be found that the mica schist interlayer and bedding planes are responsible for the slope instability, while strong toe erosion caused by the Jinsha River caused the layered rock mass to buckle intensively. Rainfall or an earthquake cannot be ruled out as a potential trigger of the landslide, considering the climate condition and the seismic activity on centennial to millennial timescales in the study area. [ABSTRACT FROM AUTHOR]

Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability process of the landslide. However, there are few studies on the relationship between the instability process of landslides and the tilting angle of the slope surface on the time scale. Therefore, there are even fewer cases in using the inclination angle of the landslide as an early warning method in practice. On August 24, 2018, a wireless tilt sensor network was deployed in the Heifangtai area of Gansu Province in China, to monitor the inclination state of loess landslides to study the relationship between the tilting angle of the slope surface and the landslide instability process. At 4 o'clock on October 5, 2019, the wireless tilt sensor network mentioned above detected a new loess fall landslide. Through the analysis of the monitoring data from the tilting sensor nodes named T3 and T4, we found that the tilting angle of the landslide surface in the pre-sliding stage has significant change compared with the stable stage, which directly verifies that the inclination data can be used for landslide warning. At the same time, we inputted the monitored inclination data into the inclination warning model proposed by Xie et al. (Landslides 17:301–312, 2020) for verification and found that the trend of inclination changes matches the model well. However, the forecast time error is large. Therefore, this paper optimizes the inclination early warning model proposed by Xie. Using the optimized early warning model, the predicted landslide occurrence time based on the data of the T3 node was 0.517 days, which was about 8 h later than the actual landslide that occurred on October 4, 2019, which is an improvement of forecasting. This case serves as an on-site direct evidence to further verify the feasibility of using the landslide body inclination state as a landslide monitoring and early warning parameter which can be promoted and used in practice. [ABSTRACT FROM AUTHOR]

Tunnel excavation in mountainous areas can sometimes reactivate or trigger landslides. The problem of tunneling-induced landslides may be influenced by not only the tunnel location and slope geological conditions but also construction deficiency without timely treatment. This paper presents a case study of Jimei landslide in Sichuan Province, China. This dormant landslide was largely induced by the excavation of a twin tunnel in the landslide body. Field investigation, slope movement monitoring, and numerical simulations show that the formation mechanism of the Jimei landslide triggered by tunneling can be summarized as follows: (1) The adverse geological conditions of stratified sliding mass consists of multilayer sliding surfaces and cracked sliding blocks. (2) The rock surrounding the tunnel underwent large and continuous deformation because of construction deficiency without timely treatment. (3) There was a reduction in the shear strength and increase in the bulk density of the sliding mass because of rainfall infiltration during the rainy season. [ABSTRACT FROM AUTHOR]

There has been considerable research on the prolonged effects and evolution of post-earthquake landslides, as they are of significance for disaster relief and rebuilding plans in earthquake-impacted regions. There is a need to better quantify these prolonged effects and evolution based on the hydro-mechanical mechanisms of post-earthquake landslides. This study investigated the evolution of the hydro-mechanical behaviour and parameters of a single landslide by integrating experimental and theoretical analyses. Analyses were undertaken using a time series of in situ monitoring data over five hydrological years on a single landslide site in the Baisha River basin of Dujiangyan, in the Wenchuan earthquake region, China. Results indicated a linear increase in the angle of internal friction under prolonged effects, while soil cohesion decreased non-linearly, gradually approaching a lower limit. The saturated hydraulic conductivity ( K s ) increased, and the soil residual water content ( θ r ) evolution curve conformed to the Boltzmann function distribution model. The soil–water characteristic curve parameters gradually increased. Based on the hydro-mechanical parameter evolution, the spatiotemporal decay of the landslide over the next decade under extreme rainfall conditions was predicted using the rainfall slope stability models and TRIGRS. This paper proposed a mechanistic explanation for the prolonged effects of a post-earthquake landslide in response to rainfall. Furthermore, it provided a reliable theoretical basis for the early warning and prevention of such landslides. [ABSTRACT FROM AUTHOR]

Bank slopes with huge accumulative sliding masses in a reservoir area suffer from landslide risks subjected to reservoir water fluctuations, and slope-cutting is an effective measure for the slope reinforcement. In this paper, a suitably and practicably integrated approach was put forward to the optimization design of the slope-cutting measure on the Shiliushubao landslide in Three Gorges Reservoir, China. It includes the determination of the most unfavorable reservoir water level variation condition according to the Three Gorges operation process, the mainly slope-cutting parts based on calculated residual sliding force curves, the main controlling factors of slope-cutting and their optimization ranges. The optimization analysis of the slope-cutting scheme for the Shiliushubao landslide was performed by taking advantage of the uniform design, transfer coefficient method, GA and ANN. The optimized layout of slope-cutting measure for Shiliushubao landslide after optimization study was to set the three main controlling factors of slope-cutting as i1 = 2.11, xb = 279.6 m and i2 = 2.01, which satisfied the objectives of both economy and landslide stability. The research results are helpful for the optimization design of slope-cutting scheme on reservoir landslides with large-scale accumulations. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

A rainfall-induced catastrophic landslide occurred at UTC + 8 03:50 local time on August 21, 2020, in Zhonghaicun village (N 29°20′30″, E 102°41′40″), Hanyuan County, Sichuan Province, China, resulting in damage to 6 buildings and 9 casualties (7 fatalities and 2 missing). In this paper, multitemporal remote sensing images, comprehensive field investigations, drilling holes, deformation monitoring, and numerical simulations are used to identify the main characteristics and the formation mechanism of the event. The results showed that long-term intensive rainfall in 2020 played an essential role in the failure of the Zhonghaicun landslide and that strong earthquakes in Wenchuan County (Ms 8.0, on 12 May 2008) and Lushan County (Ms 7.0, on 20 April 2013) also exerted a negative influence on the evolution process of this catastrophic landslide. Several traction zones appeared after the occurrence of the landslide. Fortunately, no evident deformation was observed and these slopes were in a stable state. In addition, a large-scale potential sliding slope, four small-scale landslides, and a debris flow were found in the study area. [ABSTRACT FROM AUTHOR]

The Jianshanying landslide in Guizhou province, China, is a typical mining-induced landslide with its complex formation in karst mountainous area. However, the spatiotemporal deformation characteristics and failure mechanism are still unknown. In this paper, the surface deformation time series from 2017 to 2020 is firstly estimated by combining multi-temporal synthetic aperture radar (SAR) interferometry and improved offset-tracking methods to overcome the difficulties caused by the large gradient surface deformation and surface material changes. Then, the spatiotemporal characteristics of Jianshanying landslide is analyzed in terms of line-of-sight (LOS) deformation with stacking InSAR method and two-dimensional (2D) deformation with improved SAR offset-tracking method. And the different deformation features between sliding body and surrounding areas are uncovered. Accordingly, we point out the "three-section" surface deformation model. We further analyze the deformation time series by considering the joint effects of rainfall and underground mining. Finally, we conceptualize the landslide failure mode for the Jianshanying landslide, which can be extended to the similar landslide in karst mountainous areas of southwestern China. [ABSTRACT FROM AUTHOR]

Landslide-induced tsunamis may cause fatalities, damages and financial losses. In the Three Gorges Reservoir Area of China, several large landslides are still unstable and persistently creeping toward the Yangtze River. In this paper, we investigate the impacts of landslide-induced tsunamis in the Three Gorges Reservoir by using a hybrid numerical approach. One of the largest unstable mass in this area, the Huangtupo landslide, is chosen as the study object. First, the landslide deformation and initiating velocities are obtained by using the finite-discrete element method. The landslide-induced tsunamis and their impacts on shipping on the Yangtze River are then investigated through smooth particle hydrodynamics modelling. Our results reveal that an approximately 80% reduction in shear strength of the tip in the landslide will lead to catastrophic failure of the landslide, with sliding velocities of up to 8 m/s. Subsequently, such a collapse may initiate a river tsunami, propagating up to 9 m on the nearby reservoir banks within 3 km. The impacts on surrounding floating objects, such as surges and sways, heaves and rolls, are up to 110 m, 8 m and 6°, respectively. The simulations indicate that although the likelihood of a catastrophic failure of the whole landslide is low, the partial sliding still poses severe threat to the nearby reservoir banks and shipping on the Yangtze River. Thus, we recommend continuous monitoring as well as landslide early warning systems at this and also other hazardous sites in this area. [ABSTRACT FROM AUTHOR]

Landslide failure time prediction is considered a challenging issue in landslide research. A significant goal of landslide research is to provide scientific and accurate prediction methods. In this paper, the short-term forecasting of landslides (STFL) model is proposed by analyzing landslide deformation characteristics in a known evolution process. The landslides on the southern slope of the West open pit mine (SSWOPM) in Liaoning province, northeast China, and Huangci in Gansu province, northwest China, were selected as the study cases. After pre-processing, which include eliminating abnormal data (t test), adding missing data (cubic spline interpolation), and smoothing noisy data (Savitzky–Golay filters), the displacement data are assembled to serve as the model's input parameters. The parameters of STFL can be obtained through the Levenberg–Marquardt (LM) algorithm. Using the forecasting criterion, the time of failure of landslides can be predicted and determined. Forecasting results provide evidence that the STFL model can achieve an accurate prediction of landslide displacement (correlation coefficient R2 > 0.99). The forecasting results of the SSWOPM and Huangci landslides indicate that the forecasting failure time of the STFL model is March 10, 2014 and January 31, 1995, respectively, which are near the actual failure time compared with those obtained using the Verhulst model. This finding indicates that the new method has excellent reliability and accuracy in landslide prediction and a robust description of the landslide movement. [ABSTRACT FROM AUTHOR]

At about 04:00 on 10 August 2019, the heavy rainfall carried by Typhoon Lekima induced a catastrophic rockslide in Shanzao Village, Yongjia County, China. The heavy rainfall triggered the tuff rock mass of about 12×104 m3 in volume sliding along the interbedded mudstone. Subsequently, the Shaozao river at the slope toe was blocked by the sliding mass, which formed a landslide dam afterwards. The dam broke later, ruining many houses and causing 32 casualties. We investigated the geological setting, characteristics, and failure mechanisms of the Shanzao rockslide through field investigation. In addition, the dynamic course of the landslide was analyzed using discrete element method. This paper reveals the dynamic course and risk assessment of the rockslides in the mountainous areas of the Southeast China. The findings can provide guidelines for processing the landslides with similar geological conditions. [ABSTRACT FROM AUTHOR]

Owing to the heavy rainfall, a landslide occurred at the Anqian Iron mine, at 18:00(UTC + 8) on November 24, 2019, in China. The landslide was about 3.0 × 104 m3 and caused damage to the road of transporting waste materials. Failure characteristics and the mechanism of this landslide were analyzed in this study. The landslide area was divided into three parts: the rear tension cracking area, the middle sliding deformation area, and the front colluvium area. A contact-free measuring technique using the new ShapeMetrix3D system was applied and 204 joints were analyzed based on equal-angle stereographic projection. Thus, a conceptual model of the mechanism of the landslide was constructed and the formation process of the landslide was divided into three stages: the first shearing and dislocation stage; the second sliding, front bulging, and rear tractive cracking stage; and the third local rock mass collapse and colluvium depositing stage. Numerical modeling was performed to discover the landslide mechanism by progressively reducing the shear strength of rock mass. The results showed that the original slope was stable, whereas heavy rainfall triggered the landslide, and the predicted failure surface matched closely the field investigations. The factor of safety obtained by real three-dimensional analyses was slightly higher than that obtained by plane problem analyses, and the difference was attributed to the three-dimensional effect of the landslide. This paper also presents the results obtained from the parametric analysis in order to understand the impact of shear strength parameters on the overall stability of the slope. [ABSTRACT FROM AUTHOR]

The Loess Plateau can be considered as a landslide-prone area in northwest China. The genera consensus about the interaction between landslide deposit and terrace sediments is not well studied; this paper summarised 40 loess landslides in the South Jingyang Platform, Shaanxi Province, China to help understand of this issue. Four of the loess landslides with high mobility have been analysed in detail. Three trenches T1, T2 and T3 dug after the loess landslides LD37, LD11 and LD38 highlighted the landslide-induced changes in geomorphology and internal geometry of geology, respectively. Furthermore, observation of upwards seepage flow on the profile of trench T3 is believed to be the trigger of the high speed, and long runout flowslides in the study area. A newly developed sandbox apparatus is used to reproduce the landslide kinematics due to a mass travelling over an inclined plane. The sandbox experiments show that the sediments are sheared and pushed upwards after the collision with the deposits. The deposits are then wrapped in a space between sediments, which tends to form the 'sandwich' structure. The distal sediments are thrust when the loess deposits' kinetic energy consistently dissipates, developing the accumulated folded strata. These results reveal the deposits' interactions with the sediments in the study area and provide key guideposts regarding prevention and mitigation of loess landslide hazards. [ABSTRACT FROM AUTHOR]

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches. [ABSTRACT FROM AUTHOR]

Rapid assessment of the distribution of earthquake-triggered landslides is an important component of effective disaster mitigation. The effort should be based on both seismic landslide susceptibility and the ground shaking intensity, which is usually measured by peak ground acceleration (PGA). In this paper, we address this issue by analyzing data from the Mw6.1 2014 Ludian, China earthquake. The Newmark method of rigid-block modeling was applied to calculate the critical acceleration of slopes in the study area, which serve as measurement of slope stability under seismic load. The assessment of earthquake-triggered landslide hazard was conducted by comparing these critical accelerations with the distribution of known PGA values. The study area was classified into zones of five levels of landslide hazard: high, moderate high, moderate, light, and very light. Comparison shows that the resulting landslide hazard zones agree with the actual distribution of earthquake-triggered landslides. Nearly 70% of landslides are located in areas of high and moderately high hazard, which occupy only 17% of the study region. This paper demonstrates that using PGA, combined with the analysis of seismic landslide susceptibility, allows a reliable assessment of earthquake-triggered landslides hazards. This easy-operation mapping method is expected to be helpful in emergency preparedness planning, as well as in seismic landslide hazard zoning. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

This research aims to explore detailed spatial distribution characteristics, identify key factors weight, and establish an accurate susceptibility regionalization model of coseismic landslides by the Mw 6.6 Iburi, Japan, earthquake sequence of 6 September 2018. Based on the remote sensing interpretation database, 5,977 individual coseismic landslides were delineated, which occupies an area of about 15.26 km2. The relationship between eight key factors and spatial distribution of coseismic landslides were precisely analyzed by the landslide area density (LAD) and landslide point density (LPD) curves. In order to obtain the weight of eight key factors more accurately, this paper compares the similarities and differences of the two curves in the earthquakes with similar magnitude, which was included the Lushan and Jiuzhaigou earthquake in China. According to the weight of eight key factors, the improved weight of evidence method was used to produce the susceptibility regionalization map of coseismic landslides in the Iburi earthquake. The results show that the improved weight of evidence method has better precision and can provide a scientific coseismic landslides susceptibility regionalization map for disaster prevention and mitigation in the Iburi earthquake. [ABSTRACT FROM AUTHOR]

This paper describes a recent landslide event, which occurred at Liucheng village in Tianquan County, Sichuan Province, China, on July 15, 2018. The Laochang landslide described in this research is an outstanding and valuable reference for understanding the characteristics of such kind of landslides that are geologically similar to the landslide. The deformation characteristics of the landslide are investigated based on field investigations, drilled boreholes, and exploratory trenches. The 225 residents of 64 households living on the flat platform were threatened by the landslide. Therefore, to guarantee the safety of human life and property becomes the primary emergency task. The anti-sliding piles were taken to stabilize the landslide and mitigate impacts caused by the landslide. Based on the analysis of the monitoring data, the effectiveness of anti-sliding piles is evaluated. The results indicate that the anti-sliding piles are effective in increasing the stability of the landslide, and this work can provide a reference for similar slope engineering projects. [ABSTRACT FROM AUTHOR]

Notwithstanding its complexity in terms of numerical implementation and limitations in coping with problems involving extreme deformation, the finite element method (FEM) offers the advantage of solving complicated mathematical problems with diverse boundary conditions. Recently, a version of the particle finite element method (PFEM) was proposed for analyzing large-deformation problems. In this version of the PFEM, the finite element formulation, which was recast as a standard optimization problem and resolved efficiently using advanced optimization engines, was adopted for incremental analysis whilst the idea of particle approaches was employed to tackle mesh issues resulting from the large deformations. In this paper, the numerical implementation of this version of PFEM is detailed, revealing some key numerical aspects that are distinct from the conventional FEM, such as the solution strategy, imposition of displacement boundary conditions, and treatment of contacts. Additionally, the correctness and robustness of this version of PFEM in conducting failure and post-failure analyses of landslides are demonstrated via a stability analysis of a typical slope and a case study on the 2008 Tangjiashan landslide, China. Comparative studies between the results of the PFEM simulations and available data are performed qualitatively as well as quantitatively. [ABSTRACT FROM AUTHOR]

This paper provides newly found and profound insight into the initiation mechanism of the first Baige landslide occurred on the upper reaches of the Jinsha River in October 10, 2018. The detailed geological characteristics are interpreted by comprehensive field surveys in the past year, which indicate that the Baige landslide developed in the Jinsha River tectonic suture zone, and the active tectogenesis significantly contributes to broken stratigraphic structures and complex spatial distribution of lithologies. The initiation is considered to be long-term creep under the exogenic and endogenic integration, while the active tectogenesis and the influence of serpentinite and foliation, respectively, are the primary exogenic and endogenic factors. In addition, this creep process can be analyzed within three stages: evolution and formation of failure-prone geological structures, progressive deformation and fracturing, and shear failure of the locking section. The distribution and easily degraded behavior of the serpentinite are the fundamental, enabling the formation of failure-prone structures and also responsible for the subsequent deformation evolution. The foliation controls the toppling deformation-failure mode and direction. Furthermore, this catastrophic landslide further reminds us to pay attention to the landslides along the upper reaches of the Jinsha River, and our preliminary results indicate that the distribution characteristics of landslides significantly depend on the Jinsha River tectonic suture zone and the serpentinite mélange belt and thus are always associated with tectonically induced damage. [ABSTRACT FROM AUTHOR]

Rainfall infiltration in unsaturated slopes alters the effective stress through pore water pressure changes, thus causing ground deformation. Although important to assess the timescale over which the margin of safety of a slope decreases, such coupled processes are rarely accounted in the context of spatially distributed hazard assessment procedures. In this paper, a physically based, spatially distributed model accounting for full hydro-mechanical coupling is discussed. The model relies on a vectorized finite element (FE) solver to calculate the stability of deformable unsaturated infinite slopes subjected to transient flow. First, the FE solver is used to study the response of individual slopes to a prolonged rainfall for three scenarios (i.e., rigid, swelling, and collapsible soil). Then, the model is used in the context of spatially distributed computations to assess spatiotemporal variations of factor of safety over a large area. For this purpose, a series of shallow landslides occurred in a mountainous landscape covered by collapsible loess deposits in northwestern China was used as test site. The analyses show that hydro-mechanical couplings affect the performance of the model in terms of computed failure time and areal extent of the unstable zones. Specifically, volume collapse due to suction decrease is found to reduce the time of failure compared with uncoupled computations obtained for a rigid soil scenario. The most substantial advantages of using coupled analyses have been reported with reference to gentle slopes, for which the higher rate of suction reduction driven by volume change was crucial to capture landslide source areas that would otherwise be overlooked by uncoupled analyses. The proposed methodology offers a complete tool for landslide hazard assessment, in that it incorporates sources of coupling between hydrology and mechanics that are crucial to replicate the physics of landslide initiation. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of the deformation characteristics and reactivation mechanisms of a giant ancient translational landslide in the Three Gorges Reservoir (TGR) based on more than 6 years of monitoring data with the assistance of in situ surveys. The Outang landslide, divided into three active parts, has a total volume of approximately 90 million m3 and slides along weak interlayers (WIs). Since the impoundment, the landslide has been experiencing persistent deformations with some distinct movement signs. The temporal displacements are marked by seasonal cycles of fast displacements in the summer followed by almost imperceptible movements for the remainder of each hydrologic year. The spatial displacement rates gradually increased with the increase in altitude. The primary triggering factors for slope movement could transition from the drawdown of the reservoir water to the periodic rainfall. The affected areas by the former and latter are mainly concentrated in the front part and the upper part of the slope, respectively. The landslide exhibited a composite push-retrogression-type failure evolution with the fluctuating reservoir levels destabilizing rock masses at the front part of the landslide and the precipitation mainly mobilizing materials in the upper part of the slope. Currently, the landslide is in a state of continuous movement. The toe area and upper part of the landslide should receive high priority for the development of preventative measures. [ABSTRACT FROM AUTHOR]

Most landslides induced by reservoir impoundment and rainfall in the Three Gorges Reservoir area of China are usually characterized by bedding-plane shear zones. This paper presents an experimental study of the interaction between water and shear-zone materials. A series of tests, including wetting-drying cycles and soaking tests, were carried out. The results show that the fluid-solid interaction not only reduces the shear strength of shear-zone soil but also accelerates the disintegration process of clastic rock from shear zone. After the fluid-solid interaction at different pH values, the clastic rock samples produce the most clay minerals in acidic solution. Similarly, the clay minerals of shear-zone soil samples increase dramatically after 45 days of soaking in acidic environment. According to the tests, it is found that the shear-zone soil in eluvium may originate from the weathered clastic rock near the shear zones under long-term fluid-solid interaction. [ABSTRACT FROM AUTHOR]

In this paper, a new safety control method of the jointed slope under blasting excavation is presented. The spatial distribution of blasting damage and time-history characteristic of dynamic stability around the joint were investigated carefully based on the secondary development of the explicit FE code, LS-DYNA. Results clearly indicated that the evolution law of damage and stability are opposite with the change of joint angle, and the dual-controlled mechanism of damage extension and stability fluctuation exists in the safety control of the high rock slope with joints. Then a new safety control method is proposed by implementing the equivalent Blasting Safety Control Standard (BSCS) which includes coupled effects of damage and stability. Results reveal that a critical joint angle exists, and if the inclination is larger than the threshold value, the BSCS depends on damage extension, otherwise it was determined by stability fluctuation. Finally, site experiments are implemented carefully based on the blasting excavation of the famous Baihetan high rock slope with columnar joint in China to verify the rationality of the new control approach. Sonic wave test, displacement measurement and blasting vibration test are implemented for nearly two years to track the evolution process of damage and stability of the jointed slope. Experimental results demonstrate that the new approach could improve the accuracy rate of control by 40%. [ABSTRACT FROM AUTHOR]

Currently, the frequency of landslides is increasing. Scientific monitoring methods are playing an essential role in effectively reducing landslide disasters. This paper proposes a method for identifying the cracks at the trailing edge of a landslide (TEL) based on image processing technology and adopts the custom interval median comparison algorithm (IMCA) to calculate the crack motion parameters. First, we perform a series of processes on the TEL images, including image preprocessing, Otsu's algorithm processing, and Canny edge detection processing, to identify the outline of the TEL. Then, we propose using the azimuth and displacement to characterize the motion of the cracks and using the IMCA to calculate the changes before and after motion of any two groups of cracks. Finally, we design a computer program using a free and open-source widget toolkit (named QT platform) based on the calculation model that corresponds to the proposed method, and we apply the crack monitoring test to a 3D simulation model, a gravel model, a soil model, and a collapsed body of the Panzhihua Airport landslide in southwestern China. From the results, it can be assessed that the method can identify the outline of the TEL and calculate the azimuth and displacement of two crack curves before and after motion. These two parameters can describe the movement of the trailing edge cracks of the monitored landslide. Thus, this method can be used in early warning system for landslide hazards. [ABSTRACT FROM AUTHOR]

The accurate modeling and predicting of landslide deformation is crucial to the prevention of landslide hazard. This paper presents a pioneering study of modeling and predicting the reservoir landslide displacement with deep learning algorithm. A data-driven framework using deep belief network and control chart has been introduced to explore the temporal patterns of displacement and potential of identifying seasonal faster displacement. First, the continuous wavelet analysis has been applied to decompose the time-series precipitation, reservoir water level, and displacement into seasonal and residual components. Second, the deep belief network has been constructed to predict the future displacement. Third, it utilizes the exponentially weighted moving average (EWMA) control chart to derive the boundaries as alarm conditions of seasonal faster displacement. A group of tests are conducted to compare the performance of the deep belief network with other state-of-the-art machine learning algorithms. Computational results demonstrated the effectiveness of the deep belief network in extracting highly non-linear data features. In addition, the advantage of utilizing control charts has been further validated by the accuracy of examining the seasonal faster displacement based on the case study in Baishuihe landslide in Three Gorges Reservoir, China. [ABSTRACT FROM AUTHOR]

In March 15, 2019, a small-scale loess landslide occurred at Zaoling, Shanxi Province, China, destroying several buildings and killing 20 people. Based on a digital orthophoto map (DOM) and digital elevation model (DEM) derived from Dajiang Pro4 UAV aerial images, coupled with field investigations, this paper describes the geometric features of this slope failure and analyzes its possible inducing factors. Results show that this landslide has a circular chair shape with a sliding direction of 355°. The total projected area of the landslide is 11,800 m2, in which the source is 9880 m2, and the main scarp is 1995 m2. The volume of the source material and loose deposit debris are about 10 × 104 m3 and 12 × 104 m3, respectively. Our research suggests that the inducing factors of this event may include the low strength and structure of the loess under a wet condition, special landforms of the slope, and human activities such as water discharge from a nearby bath center building and coal mining. [ABSTRACT FROM AUTHOR]