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Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Analyzing and quantifying the deformation process of landslides is of paramount importance in facilitating landslide early warning. As such, this study is committed to proposing a universal phenomenological model for deformation stages division and early warning of landslides based on the kinematic features. First, five landslide deformation patterns were classified based on the creep theory, and suggestions for stage division of each deformation pattern are presented. Then, the statistical characteristics of landslide velocity were analyzed, and a probability-based deformation stage division method was proposed. Finally, the Comprehensive Standardized Deformation Index (CSDI) model, which includes the calculation of the CSDI M - M (Min-Max normalization) and CSDI M (Mean normalization) was proposed and verified in 24 landslides worldwide. The results show that, except for the oscillating pattern, the CSDI M - M is feasible in the stages division of all deformation patterns with a strong correspondence with the actual state of the landslides. The CSDI M is a reliable landslide warning criterion and threshold determination method, as it is effective in the early warning of imminent landslides with a low false alarm rate. The CSDI model provides new insight into the division of landslide deformation stages and landslide risk assessment. [ABSTRACT FROM AUTHOR]

The evaluation of landslide runout behavior plays a vital role in the risk assessment of landslides. In this study, the runout behavior of a landslide that occurred on December 10, 2019, in the Three Gorges Reservoir region, China, known as the Kamenziwan landslide, is studied with the discrete element method. A field survey is first conducted to study the geological characteristics and failure mechanism of this landslide. The survey results indicate that this landslide is possibly caused by the long-term fluctuation of the reservoir water level. Then, a three-dimensional (3-D) numerical model of this landslide is constructed on the basis of the terrain data obtained before and after the landslide occurrence. The critical input parameters to the numerical model, in terms of the contact micro-parameters among particles, are calibrated with the machine learning method and uniaxial compression tests. To depict the effectiveness of the built numerical model, the simulation results are compared with the video snapshots taken by a local resident. Finally, the runout behavior and the energy transformation of the landslide are analyzed based on the validated numerical model. [ABSTRACT FROM AUTHOR]

Data from in situ monitoring of displacement is of fundamental importance, not only in hazard assessments, but also for understanding the deformation mechanisms of landslides. However, acquiring reliable and continuous displacement data from complex landslides is often a problem because of the high cost of instruments and maintenance. Among more than 250 dangerous landslides monitored in the Chinese Three Gorges reservoir area, the most systematic and complete monitoring data have been obtained from the Shuping landslide. A ten-year record of GPS displacements measured in eight different locations, plus rainfall and reservoir level data, are utilised to identify the movement characteristics and causal factors of the Shuping landslide. The cumulative displacement curves show a series of abrupt increases that occur every year and are separated by periods of steady, slower deformations. The maximum cumulative displacement that occurred during the ten-year monitoring period was 4.7 m at site SP-2. The maximum displacement rate was 35 mm/day, which was observed on June 13, 2012 at site ZG86. Causal factors of the Shuping landslide are identified through qualitative comparisons of the monitoring data and cross-examined by grey correlation degree analysis. The results show that reservoir water level drawdown is the main factor accelerating the movement of the Shuping landslide, with heavy rainfall playing a subordinate role. The proposed use of field data and analytical methods has the potential for providing procedures and solutions for the interpretation of landslide monitoring data. [ABSTRACT FROM AUTHOR]

The mechanical properties of stabilizing piles are directly affected by the spatial structure of the bedrock of landslides. To effectively reinforce landslides with a sandwich-type bedrock, the interaction mechanism between manually excavated stabilizing piles and the different types of bedrock must be investigated. The research presents experimental models of manually excavated stabilizing piles for two kinds of sandwich-type bedrock structures, namely hard rock–dominant interlayered bedrock (HDIB) and weak rock–dominant interlayered bedrock (WDIB). Six physical models with different bedrock structure types and dip angles θ were prepared to explore the influence of these two parameters on the pile-bedrock interaction mechanism. The mechanical properties of the pile were investigated based on the monitoring results of the earth pressure distributions against the piles and the bending moment distributions. The embedded section of the pile experiences a rotation subjected to lateral soil movement. The center point of the rotation in the HDIB is at a greater elevation than in the WDIB. The earth pressure in the HDIB appears to increase with dip angle θ but is similar to each other in the WDIB. The pile embedded in the HDIB exhibits flexible behavior, whereas the pile embedded in the WDIB exhibits rigid behavior. With the increase of dip angle θ in the WDIB material, the bending moment steadily rises followed by fluctuation. The maximum bending moment of the embedded section increases with θ. These results provide a basis for improving the design of manually excavated stabilizing piles in sandwich-type bedrock. [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]

The Huangtupo riverside slope, which extends an area of 1.35 × 10 m and has a total volume of 6.934 × 10 m, is one of the most noted large-scale landslides with great concern about the safety of immigrants in the Three Gorges reservoir area of China. During the normal operation, the reservoir water level fluctuates periodically. Water level fluctuation will soften the strength of the geologic materials on the banks, induce underground water fluctuation, and in turn affect the slope stability. On the basis of the thorough engineering geological survey, the analysis of engineering geological conditions and deformation mechanism is conducted primarily. Then, the Huangtupo landslide is divided into four minor landslides, all of which have typical three-dimensional characteristics. To these landslides, a three-dimensional rigorous limit equilibrium method is proposed, which can accommodate failure surfaces of any shape, and satisfies all the equilibrium conditions. In terms of the strength parameters of geologic materials, the stability evolution of the four minor landslides at the Huangtupo landslide under periodical water level fluctuation is investigated in detail. From the results, the 3D safety factors turned out to be somewhat bigger than the 2D results even though they displayed the same trends. The differences between 2D and 3D results are about 2.92-6.11 %. The maximum reduction in safety factors with the rapid down of water level is 0.11. It is suggested that the safety margin of the two minor landslides near the riverside is insufficient and might become instable under periodical water level fluctuation. [ABSTRACT FROM AUTHOR]

A rapid and long-traveling landslide was triggered by a monsoon rainfall of a total of 446.5 mm for 3 days in the Aranayake area, Sri Lanka. The landslide destroyed 75 houses and killed 127 persons along the course of this landslide. The mechanism of this landslide was investigated by a joint team of the International Consortium on Landslides (ICL) and the National Building Research Organization (NBRO) of the Government of Sri Lanka in 2017 and 2018; the investigation was part of the WCoE and IPL projects. Physical properties of soil samples taken from the source area of the Aranayake landslide were examined through a series of undrained ring-shear tests, and the measured properties were used in a numerical method, LS-RAPID, to simulate initiations and motions of landslides in this area. The authors then tried to first reproduce a landslide, which was considered, from available pieces of evidence, to have most likely happened in the past, then, to assess the risk of a potential future landslide that is expected to occur close to the 2016 Aranayake landslide. Two possible sliding surfaces were assumed to develop through the soil mass, one along the boundary between the weathered surface soil and the jointed bedrock and another along a potential aquiclude inside the soil. This attempt to estimate movements of both past and future landslides based on the numerical reproduction of the 2016 Aranayake landslide hazard will help develop landslide hazard assessment technologies. [ABSTRACT FROM AUTHOR]

Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements. [ABSTRACT FROM AUTHOR]

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior. [ABSTRACT FROM AUTHOR]