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In the process of rapid drawdown of reservoir water level, the seepage force in the slide mass is an important factor for the stability reduction and deformation increment of many landslides in the reservoir areas. It is feasible to improve the stability of seepage-induced landslide by employing a drainage well to reduce or eliminate the water head difference that generates the seepage force. In this paper, the Shuping landslide, a typical seepage-induced landslide in the Three Gorges Reservoir area of China, is taken as an example. A series of numerical simulations were carried out to figure out the seepage field, and the Morgenstein-Price method was adopted to calculate the landslide stability. Then the influence of horizontal location of the drainage well, drainage well depth, drainage mode on the landslide treatment effect, and the applicability of drainage well were analyzed. The results show that: (1) landslide stability increases obviously with the well depth in the slide mass, while the increment of landslide stability with the well depth is limited in the slide bed; (2) the sensitivity of the stability improvement with the depth is greater than that with the horizontal positions of the drainage wells in the slide mass; (3) the drainage well is suggested to be operated when the reservoir water falls rather than operates all the time; and (4) the drainage method is most suitable for landslides with low and medium permeability. These results provide deep insights into the treatment of seepage-induced landslides.

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]

Rainfall with heavy intensity and long duration is a main inducing factor that triggers landslides on anti-inclined rock slopes. Numerous interbedded anti-inclined rock (IAR) slopes in Southwest China frequently experience mountain collapses and landslides under heavy rainfall. Therefore, it is necessary to study the failure mode of IAR slopes during rainfall. In this study, the Linda landslide and Mari landslide, typical IAR landslides in the study area, were used as examples to build a physical model of IAR slopes under continuous rainfall conditions, and a self-made rainfall simulation system was used to mimic natural rainfall conditions. The multi-physics data of displacement, velocity, stress, strain, pore water pressure, and acoustic signals in model deformation and failure under rainfall conditions were obtained using multi-field monitoring technology. In addition, the materials' water content and mechanical parameters at different locations of the model were obtained after the experiment. The analysis results indicate that: in the first and middle periods of rainfall, the slope model experienced a shallow local deformation and a holistic toppling deformation; until the late rain, the failure of the IAR slope caused by rainfall finally presented a typical failure mode of multiple failures and collapses of regressive landslides. According to its multi-field evolution characteristics, the velocity and acoustic signals were highly consistent with the failure characteristics of the model, which could be used as landslide early warning indicators. In addition, it was observed that the shape of the multi-slip surfaces generated by the model was a concave double straight line extending from the slope toe to the slope surface and sharing the failure section at the slope toe. Finally, the deformation and failure process of the model could be speculated to be divided into five stages based on the deformation behavior and multi-field physical data feature: the initial interlayer dislocation stage, the deformation stage, the crack propagation stage, the retrogressive failure stage, and the holistic failure stage. The failure mode and deformation mechanism of IAR slopes under rainfall were revealed in the model test, which could provide appropriate guidance for the analysis and evaluation of similar landslides. Highlights: The failure of the interbedded anti-inclined rock slope model caused by rainfall presented a typical failure mode of multiple failures and collapses of regressive landslides. The velocity and acoustic signals are highly consistent with the failure characteristics of the model, and the shape of the multi-slip surfaces was a concave double straight line extending from the slope toe to the slope surface. According to its multi-field evolution characteristics, the deformation and failure process of the model under rainfall conditions can be divided into five stages. [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]

Block-flexure toppling failures in interbedded anti-inclined rock (IAR) slopes present significant risks to infrastructure and safety. Based on the Linda landslide field investigations, this study conducted a series of physical modelling tests encompassing 15 slope angle and bedding angle combinations. The aim of 15 sets of tests was to explore the deformation and failure mechanisms in IAR slopes. The observations during the deformation and failure stages elucidated how changes in slope and bedding angles influence rock mass deformation and failure characteristics. The findings highlight that slope angle critically impacts IAR slope stability, while bedding angle notably affects deformation depth, failure area, and failure surface configuration. IAR slopes with bedding angles less than 60° tend to exhibit shallow, single-surface failures with the linear failure surface, whereas bedding angles exceeding 70° lead to deep-seated, large-area, multi-surface failures characterised by double-folded failure surfaces. The magnitude of the failure inclination angle increases with slope and bedding angles, ranging from 0 to 35°. Furthermore, deformation and failure in IAR slopes involve the progressive extension of cracks from the slope crest to the interior, eventually reaching the slope toe. Finally, the block-flexure toppling process in IAR slopes can be divided into an incubation deformation stage and an accelerated deformation stage, and based on curve characteristics, different deformation modes of the IAR slopes can be further classified as stepwise cumulative accelerated deformation mode and leap deformation mode. Highlights: Fifteen cases of physical modelling tests covering various slope angles and bedding angles were conducted to replicate the deformation and failure mechanism in interbedded anti-inclined rock (IAR) slopes; Slope angle plays a pivotal role in determining the stability of IAR slopes, whereas bedding angle controls slope deformation depth, failure area, and failure surface characteristics; The IAR landslide is a progressive failure process that extends from the slope crest to the slope toe; The deformation process of IAR slopes can be categorised into the incubation deformation stage and the accelerated deformation stage. Additionally, the deformation mode can be further classified as either stepwise cumulative accelerated or leap deformation modes. [ABSTRACT FROM AUTHOR]

Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R2 increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R2 of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research. [ABSTRACT FROM AUTHOR]

Block-flexure toppling is the most common toppling mode occurring in interbedded anti-inclined slopes. With the development of hydropower projects in Southwest China, numerous deep-seated large-area toppling failures have become revealed along the Yalong River, seriously threatening the construction and operation of hydropower infrastructure. With the Mari landslide as the research focus, a geological field investigation, base friction physical model test, and numerical simulation test were carried out to study the evolutionary process of slope failure. The results demonstrate that the deep-seated toppling in the study area initiated and developed with the river downcutting process, and the landslide was the final result of the toppling deformation. The interbedded structure of layered rock masses, the steep slate foliations in the slope, and the cross joints in the metamorphic quartz sandstone perpendicular to the rock layer are the controlling factors of the toppling deformation. The landslide induced by toppling mainly developed in the highly deformed zone, where multiple failure surfaces developed, and the location and shape of the failure surfaces were controlled by the cross joints in the metamorphic quartz sandstone. The evolutionary process of the deep-seated large-area toppling in the study area mainly underwent four stages: the initial slope formation stage, the rock column bending and cracking stage, the failure surface penetration stage, and the failure stage. [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]

A simplified geomechanical model was proposed by considering three typical necking-type slopes; this model lays a foundation for the further investigation of the deformation behaviors of such slopes. Three physical models of necking-type slopes were built according to the geomechanical model with slope evolution stages. Finally, preliminary calculations related to the arching effect in the physical model were conducted. Three evolution stages of necking-type slopes, namely, the initial stage, compression stage, and failure stage, were presented based on the formation and disappearance of the arching effect within the slope. The specific parameters of the geomechanical model were given. In the setup of the tilting test, the failure angle of the necking-type slope model was calculated to be approximately 50° with a large lateral resistance coefficient. The proposed geomechanical model and physical models of necking-type slopes provide guidance for the establishment of geomechanical and physical models of landslides at specific sites. [ABSTRACT FROM AUTHOR]

Physical modelling is a useful method to examine how landslides deform and fail at the laboratory scale. Reliable and accurate measurement systems are required for landslide model tests. This study comprehensively evaluates the performance of a multismartphone measurement (MSM) system for landslide model tests. Two slope models with different particle sizes were selected to assess the trueness and precision of the MSM system based on photogrammetry techniques under varied conditions, including different smartphone qualities and phone configurations. Benchmark data from a 3D laser scanner and preprinted markers were employed in the entire-point tests and marker-point tests, respectively. In addition, the MSM system's application test for monitoring landslides is presented. The results show that the system can be applied to achieve both trueness and precision at the millimetre level and submillimetre level in the entire-point tests and marker-point tests, respectively. Best-practice settings of the MSM system based on assessment tests are suggested for landslide slope models. The deformation behaviour of the excavation-induced slope model in the application test was clearly illustrated through the marker-based MSM system. [ABSTRACT FROM AUTHOR]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. [ABSTRACT FROM AUTHOR]

For landslide displacement, interval predictions are generally more realistic and reliable compared with traditional point predictions. This paper presents a new interval prediction method for landslide displacement integrating dual-output least squares support vector machine (DO-LSSVM) and particle swarm optimization (PSO) algorithms. In this new method, the PSO algorithm is employed to optimize coefficients of the least squares support vector machine (LSSVM) model for obtaining point prediction results, and the interval prediction of the landslide displacement is made based on the dual-outputs obtained from the DO-LSSVM model. To assess the rationality of the predictions, three performance evaluation indicators, including the prediction interval coverage probability (PICP), normalized mean prediction interval width (NMPIW), and coverage width-based criterion (CWC), are established. Case studies of the Tanjiahe landslide and the Baishuihe landslide in the Three Gorges Reservoir region are then used to demonstrate the effectiveness of the proposed method in predicting the landslide displacement interval. The case study results demonstrate that this new method has the best overall performance compared with other existing methods, and this new method can provide accurate and reliable results for the medium- to long-term interval prediction of landslide displacement. [ABSTRACT FROM AUTHOR]

Landslide displacement prediction is a challenging and important subject in landslide research. To improve the prediction accuracy of and reduce disasters caused by landslides, we propose a selective ensemble deep bidirectional Random Vector Functional Link Network (sedb-RVFLN) for landslide displacement prediction in which each independent hidden layer is linked to a different output layer. In this paper, to reduce the number of hidden nodes without affecting the efficiency of network training, an incremental learning method is utilized to make some hidden nodes not randomly chosen. Moreover, we apply selected partial hidden layers instead of all hidden layers to construct a selective ensemble. The ensemble method adopted by sedb-RVFLN does not require training multiple independent networks, and the entire sedb-RVFLN only needs to be trained once. Finally, we conduct extensive experiments on real landslide datasets from the Huangdeng Hydropower Station in China to demonstrate the effectiveness of our model. [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]

Rock avalanches often cause many casualties and considerable property damage due to hypermobility. To better understand the propagation process of rock avalanches, the mechanical energy evolution was investigated based on numerical simulation method and field investigation. Taking the well-known Jiweishan landslide as an example, the kinetic, potential, and dissipation energy elements were extracted from the kinematics parameters, and effective collisions were determined by calculating the local peaks of velocity. The lower velocity regions were observed in the middle portion of the sliding mass. Blocks in these regions were the medium through which the energy was transferred from the back portion of sliding mass to the front portion, acting like the stationary spheres in Newton's Cradle. The entire sliding mass followed the law of conservation of energy, while the energy evolution of the individual blocks varied by their locations. Considering that more collisions occurred in the front portion of the sliding mass, blocks in this area received more energy during propagation, thus they traveled a longer distance. Furthermore, the energy transfer became more obvious with the increase in the volume of sliding mass, causing a larger travel distance for the rock avalanche. [ABSTRACT FROM AUTHOR]

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

Featured Application: The proposed decomposition-ensemble learning model can be efficiently used to enhance the prediction accuracy of landslide displacement prediction and can also be extended to other difficult forecasting tasks in the geosciences with extremely complex nonlinear data characteristics. As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of "decomposition and ensemble", a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction. [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 whose slide surface is gentle near the toe and relatively steep in the middle and rear part are common in the Three Gorges Reservoir area, China. The mass that overlies the steep part of the slide surface is termed the "driving section", and that which overlies the gentle part of the slide surface is termed the "resisting section". A driving–resisting model is presented to elucidate the deformation mechanism of reservoir landslides of this type, as exemplified by Shuping landslide. More than 13 years of field observations that include rainfall, reservoir level, and deformation show that the displacement velocity of Shuping landslide depends strongly on the reservoir level but only slightly on rainfall. Seepage modeling shows that the landslide was destabilized shortly after the reservoir was first impounded to 135 m, which initiated a period of steady deformation from 2003 to 2006 that was driven by buoyancy forces on the resisting section. Cyclical water level fluctuations in subsequent years also affected slope stability, with annual "jumps" in displacement coinciding with drawdown periods that produce outward seepage forces. In contrast, the inward seepage force that results from rising reservoir levels stabilizes the slope, as indicated by decreased displacement velocity. Corrective transfer of earth mass from the driving section to the resisting section successfully reduced the deformation of Shuping landslide and is a feasible treatment for huge reservoir landslides in similar geological settings. [ABSTRACT FROM AUTHOR]

For reservoir landslides with limited in situ monitoring data, improving the accuracy of an ensemble prediction model through historical displacement-related factor deconstruction and model optimization is challenging. Unfortunately, even though various support vector regression (SVR) based ensemble prediction models have been proposed for this purpose, the optimal combination of new algorithms and their degree of improved prediction accuracy is still unclear. Based on four evaluation indicators, this paper presents a comparative study of a typical landslide displacement prediction model containing four important nodes (landslide displacement decomposition, inducing factor frequency-component extraction, input parameter selection, and optimization algorithm selection) vital to prediction accuracy. The empirical mode decomposition (EMD) series model was adopted to decompose the landslide displacement and extract the frequency component of the factors that affect the landslide movement. Four trajectory similarity judgment models were used to select input variables for the SVR-based prediction model. Nine swarm intelligence (SI) algorithms were used to help optimize the landslide prediction model. A case study shows that the SVR-based ensemble landslide prediction model works well in predicting the displacement of a slow-moving landslide. The combination of EEMD-CEEMDAN-LCSS-PSO yields the best prediction performance, with MAPE, RMSE, MAE, and R2 values of 0.004, 2.38, 8.64, and 0.96, respectively. The findings of this study can impact further studies of landslide ensemble prediction model optimization with insights into the algorithm selection, parameter setting, evaluation measures, and experimental settings. • A comprehensively analyze of the deformation characteristics of the Outang landslide was conducted for a full understanding of the triggering factors. • The EMD series method, including EMD, EEMD, CEEMD, and CEEMDAN, was adopted and compared in landslide displacement decomposition and frequency component extraction of inducing factors. • GRD and trajectory similarity calculation methods, including DTW, LCSS, and EDR, were utilized and compared in the most relevant input parameter selection for the displacement prediction model. • Nine swarm intelligence algorithms, including PSO, ABC, BA, GWO, ALO, WOA, DA, GOA, and SSA, were applied and compared in SVR-based prediction model's optimization. [ABSTRACT FROM AUTHOR]

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

Previous studies about probabilistic back analysis for shear strength parameters of landslides generally adopted a fixed slip surface. This setting may lead to unreliable results due to the uncertainty of slip surface location speculated by limited observations. Based on Bayes' theorem, this paper proposes a probabilistic framework for the back analysis of landslides considering slip surface uncertainty. The posterior distributions of shear strength parameters in Bayesian inference are solved by Markov chain Monte Carlo simulation method. To improve computational efficiency, a response surface function based on extreme learning machine is constructed to approximate the relationship between shear strength parameters and the corresponding factor of safety and critical slip surface. A synthetic slope, for which the actual shear strength parameters and slip surface are known, is used to compare the proposed and traditional methods. The effects of measurement error of slip surface and prior distribution of shear strength parameters on probabilistic back analysis results are also investigated. Results show that the shear strength parameters obtained from traditional probabilistic back analyses neglecting slip surface uncertainty significantly deviate from actual values, and are greatly affected by prior mean of shear strength parameters. The proposed method performs better than traditional method and is less affected by the prior distributions of shear strength parameters, and the smaller the measurement error of slip surface, the higher the Bayesian back analysis accuracy. A practical landslide is applied to further verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Data preprocessing is an indispensable step for landslide displacement forecasting, which is an effective approach for predicting the deformation and failure behaviors of landslides. However, most studies focus on the construction of displacement forecast models and ignore the influence of data preprocessing on the forecasting results. Data normalization is an important part of data preprocessing; however, the selection of a data normalization method is subjective and arbitrary. In this study, four types of normalization methods for data preprocessing are presented, and these methods are applied in forecasting the displacement of bank landslides in the Three Gorges Reservoir area with various deformation mechanisms for comparison. The results demonstrate that (1) the selected normalization method substantially influences the forecast performance; (2) the normalization method is closely related to the selected forecasting model and is less dependent on the landslide deformation mechanism; and (3) the commonly used max–min normalization approach is not the optimal method, and the zero-mean normalization method is optimal for the particle swarm optimizer of support vector machine (PSO-SVM) method, while the logarithmic normalization method is optimal for the extreme learning machine method. The obtained results suggest that the data preprocessing methods must be carefully selected in landslide displacement forecasting. [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]

The Three Gorges Reservoir area, one of the most landslide-prone areas in China, is characterized by widely distributed deep-seated landslides exhibiting creep deformation due to rainfall and reservoir fluctuation. Thresholds, which are a key component for a reliable landslide early warning system, are still lacking for the prediction of movements of deep-seated reservoir landslides with creep deformation because information about reservoir fluctuation indicators is lacking, uncertainty is ignored, and binary output is provided. The risk threshold, defined as the tolerance criteria for risks that will lead to action, is an effective measure of the degree of uncertainty. In the present study, a hybrid approach utilizing kernel density estimation, a copula function, and the value at risk is proposed for the estimation of the risk threshold for the Baishuihe Landslide, a typical deep-seated landslide in the Three Gorges Reservoir area. Historical observations over approximately 15 years including rainfall, reservoir fluctuation, and landslide velocity were used to extract the risk threshold. A three-level risk threshold describing the minimum magnitudes of rainfall and reservoir fluctuation for changing the landslide movement state under three confidence levels was developed. A three-level risk response procedure, including risk responses in yellow alert, orange alert, and red alert, is proposed for risk management. Given its successful application, the present approach can be used to estimate the risk threshold for deep-seated landslides. [ABSTRACT FROM AUTHOR]

This paper presents a new limit equilibrium method based on analysis of landslides with multicircular slip surfaces in the Three Gorges Reservoir area, China. An important innovation of this method is that the rigid sliding mass is divided into numerous interrelated Spencer's circles sharing the same factor of stability, so that each of them possesses a real centre of rotation and an independent inclination of interslice forces. The analysis is accomplished by iterations to satisfy both force and moment equilibrium for each circle. Two real cases were then adopted to verify the effectiveness of the method in the analysis of both slope stability and the design forces on piles. Factors influencing the performance of the method were also investigated, which reveal that the concavity of the local slip surface near the slope toe has a major impact. The importance of the proximity between the actual and the fitted sliding surfaces was highlighted for ensuring accuracy of the method when extended to the real cases. [ABSTRACT FROM AUTHOR]

Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future landslide displacements. In this paper, under the framework of the original lower upper bound estimation method, a direct interval prediction approach is proposed for landslide displacements based on the least squares support vector machine (LSSVM) and differential search algorithms. Two LSSVM models are directly implemented to generate the interval of future displacements, and the optimal model parameters are derived by the differential search algorithm. The Baishuihe landslide and the Tanjiahe landslide located on the shoreline of the Three Gorges Reservoir, China, are used to test the proposed approach. Compared with other models, the proposed method performed best and presented the smallest coverage width-based criterion values of 0.8927 and 1.0562 at monitoring stations XD01 and ZG118 for the Baishuihe landslide, respectively, and 0.1316 and 0.1191 at monitoring stations ZG289 and ZG287 for the Tanjiahe landslide, respectively. The results indicate that the proposed approach can provide high-quality prediction intervals for landslide displacements in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

Landslides whose slide surface is gentle near the toe and relatively steep in the middle and rear part are common in the Three Gorges Reservoir area, China. The mass that overlies the steep part of the slide surface is termed the driving section and that which overlies the gentle part of the slide surface is termed the locking section. A driving-locking model is presented to elucidate the deformation mechanism of reservoir landslides of this type, as exemplified by Shuping landslide. More than 13 years of field observations that include rainfall, reservoir level and deformation show that the deformation velocity of Shuping landslide depends strongly on the reservoir level but only slightly on rainfall. Seepage modelling shows that the landslide was destabilized shortly after the reservoir was first impounded to 135 m, which initiated a period of steady deformation from 2003 to 2006 that was driven by buoyancy forces on the locking section. Cyclical water-level fluctuations in subsequent years also affected slope stability, with annual jumps in displacement coinciding with drawdown periods that produce outward seepage forces. In contrast, the inward seepage force that results from rising reservoir levels stabilizes the slope, as indicated by decreased deformation velocity. Corrective transfer of earth mass from the driving section to the locking section successfully reduced the deformation of Shuping landslide, and is a feasible treatment for huge reservoir landslides in similar geological settings. [ABSTRACT FROM AUTHOR]

Hydraulic parameters are key data for calculating groundwater level, which is critical for assessing reservoir landslide stability. However, the quantitative understanding the influence of hydraulic parameters on the groundwater level of a reservoir landslide remains limited. In this paper, we apply a novel global sensitivity analysis method, PAWN, to quantify the sensitivity of hydraulic properties. The Shuping landslide, which is a typical reservoir colluvial landslide located in the Three Gorges Reservoir area, China, is used as a study case. The hydraulic parameters are first sampled within their entire feasibility space by the Latin hypercube sampling method. These samples are then used as inputs into a nonintrusive finite element program to automatically compute the corresponding groundwater level outputs. Finally, sensitivity indices are calculated based on the input–output dataset via the PAWN method. The global sensitivity analysis results provide useful guidelines for site investigation and reservoir colluvial landslide model simplification and calibration. [ABSTRACT FROM AUTHOR]

Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement. [ABSTRACT FROM AUTHOR]

Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement. [ABSTRACT FROM AUTHOR]

Toppling deformations generally develop in interbedded anti-inclined rock slopes in the upper reaches of the Yalong River in southwest China. With the characteristics of large scales, strong deformation degrees and complex deformation modes, toppling bodies may eventually lead to instability failure in the form of landslides, which seriously threaten the construction and operation of hydropower stations in the basin. Taking three typical examples of toppling failures for interbedded anti-inclined rock slopes in the basin, the sensitivity analysis of 19 impact factors affecting the toppling deformation of this type of slope is obtained via the Grey correlation method and UDEC. The results show that the geometric parameters represented by slope angles are the most sensitive to toppling deformation, the mechanical parameters of joints are more sensitive to toppling deformation than the physical and mechanical parameters of intact rocks, and the sensitivities of the parameters of sandstone and slate gradually converge in the deformation process. [ABSTRACT FROM AUTHOR]

Due to complex geological structures and strong crustal activity, geological disasters occur frequently in the upper reaches of the Yalong River, Southwest China. Toppling failure on anti-dip rock slopes is a common phenomenon; in particular, after the Wenchuan earthquake in 2008, numerous landslides caused by toppling deformation occurred in reservoir areas, seriously threatening the construction and operation of large-scale water conservancy projects and hydropower projects. Taking three typical toppling slopes in the basin as research objects, the dynamic behaviour, failure evolution, and failure plane location of toppling failure under seismic waves were investigated by shaking table tests and UDEC simulations. The test results indicated that slope angle and stratum dip angle were both inversely proportional to the stability of the slope in the tested ranges. The failure evolution of toppling on anti-dip rock slopes could be divided into four stages: (1) the development stage of tensile cracks in the strata; (2) the formative stage of tensile cracks at the crest of the slope; (3) the formative stage of toppling zones; and (4) the failure stage. Two equations were proposed to reveal the relationships between the surface peak ground acceleration and the horizontal depth of the failure plane and displacement of the slope surface. The propagation and coalescence of discontinuous cross joint in the slope and the entire failure evolution simulated by the UDEC damage model agreed well with the shaking table test results. [ABSTRACT FROM AUTHOR]

Rapid long-runout landslides always cause extensive damage to lives and properties, and the deposit characteristics of landslides play an important role in revealing the evolution and transport mechanism. Several intensive fieldworks were conducted at the Jiweishan landslide region in Chongqing, China to investigate the deposit features, including the motion characteristics, retention of stratigraphic sequence, inverse grading, and thickness distribution, which later helped in the calibration and setting of the numerical modeling. Based on the discrete element method (DEM), the effects of simulation parameters (friction coefficient, volume, and fall height) on the maximum travel distance and deposit area of the landslide were assessed, taking account into the joints network developed in rock mass, respectively. Sensitivity analysis was performed according to numerical data, and the calculated relative sensitivity (RS) values suggested that all parameters had an impact on the travel distance of rapid long-runout landslides, and the sensitivity could be listed in the following order of most to least sensitive: volume (RS = 0.83) > fall height (RS = 0.23) > friction coefficient (RS = 0.14). [ABSTRACT FROM AUTHOR]

This paper presents a new framework for characterizing landslide deformation. Here, the deformation of a landslide is interpreted as a summation of three components: rigid deformation, within-mass deformation, and residual deformation. On the basis of the monitored data of the landslide deformation, these three components may be characterized separately: the rigid deformation is simulated by a summation of a trend term and a periodic term, the within-mass deformation is simulated by a high-order polynomial model, and the residual deformation is simulated by a conditional random field model. In particular, the characterization of the residual deformation, the third component of the landslide deformation, with the random field allows for a probabilistic assessment of the landslide deformation in the face of geological uncertainties. With the proposed framework, the evolution of landslide deformation in both geometric and time domains may be established, which allows for an assessment of the sliding mechanism of the landslide. Further, evolution in the geometric domain may allow for an assessment of the serviceability of infrastructures in the landslide area. To illustrate this new landslide deformation characterization framework, a case study of the Yu-Kai highway landslide in Guizhou, China is presented, through which the effectiveness of the proposed framework is demonstrated. [ABSTRACT FROM AUTHOR]

Summary: Though the technology of using stabilizing piles to prevent landsliding is not new, the design of such piles with a meaningful optimization framework has been rarely reported. In this paper, a multiobjective optimization‐based framework for design of stabilizing piles is presented, in which both reinforcement effectiveness and cost efficiency could be explicitly considered. The design parameters considered in the proposed design framework are the pile parameters, including pile diameter, spacing, length, and position, and the design objectives considered are the reinforcement effectiveness and cost efficiency. The design of stabilizing piles is then implemented as a multiobjective optimization problem. In that the desire to maximize the reinforcement effectiveness and that to maximize the cost efficiency are two conflicting objectives, the output of this multiobjective optimization will be a Pareto front that depicts a trade‐off between these two design objectives. With the obtained Pareto front, an informed decision regarding the design of stabilizing piles is reached. The effectiveness and significance of the proposed multiobjective optimization‐based design framework for stabilizing piles are demonstrated through two illustrative examples: one is the design of stabilizing piles in a one‐layer earth slope and the other the design of stabilizing piles in a two‐layer earth slope. Further, parametric analyses are conducted to investigate the influences of the pile design parameters on the stability of reinforced slopes. [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 time-dependent soil creep behavior affects the strength and deformation of the shear zone in landslides. Limited studies have been carried out on the undisturbed soil samples from the deep shear zone due to the difficulties in obtaining them. This problem is investigated using a case study of the well-known Huangtupo landslide, which is the largest landslide in the Three Gorges Reservoir area of China. The creep properties of the undisturbed shear zone were studied using triaxial creep testing. Two creep stages (the primary stage and the attenuation secondary creep stage) were observed, which were characterized by a creep rate that approached zero, instantaneous deformation and the development with a constant speed. The long-term shear strength of the shear zone was calculated based on the test results. The creep stages and long-term shear strength are significantly different from those of direct creep tests of reconstituted shear zones due to the influence of the confining pressure and coarse particles. The Computerized Tomography (CT) scanning technique was also applied, and the results showed clear rotation of some coarse particles in the middle parts of the samples, which was reflected by their shapes. The changes in particle position were more significant at the bottom of the sample. Based on these results, an empirical constitutive model was proposed to describe the relations among stress, strain and time, and this model fits the experimental data well. The inhomogeneity of the undisturbed soil samples leads to different creep properties from those in ideal theoretical models; therefore, new stochastic constitutive models should be explored and developed for further studies. [ABSTRACT FROM AUTHOR]

In recent years, numerous landslide catastrophes have occurred, generating considerable financial losses and other tolls. The deformational and mechanical properties of sliding zone soil would be in primary significance to landslide research, as the sliding zone basically controls the initiation and mobility of the landslide. An in situ triaxial test was carried out on a sample from the sliding zone of the Huangtupo 1# landslide, a subdivision of the Huangtupo landslide in the Three Gorges area of China. The test results indicate that (a) the sliding zone exhibits low compressibility due to the high rock content (54.3%) and long-time consolidation by the overlying soil mass; (b) only decaying creep occurs without abrupt failure, and a constitutive equation with both linear and nonlinear viscoplastic terms is deduced to accurately fit the test data; (c) the surface with an orientation of 35° presents anisotropic traits in terms of displacement, possibly due to cracks that formed at similar orientations within the sample cube; and (d) the creep behavior of the landslide may be closely related to the properties of the sliding zone soil. When a similar stress magnitude to that of the in situ stress environment is applied to the sample, the sliding zone soil behavior matches the landslide deformative behavior. The test results indicate that the Huangtupo 1# landslide will continue to creep, as interpreted from the deformation traits and structural properties of the sample. However, unavoidable limitations of the test and extreme external factors, namely unexpected rainfall and water fluctuation, cannot be ignored. [ABSTRACT FROM AUTHOR]

Saturated hydraulic conductivity (Ks) is spatially variable in accumulation landslide sites that exert significant effort onto landslide seepage and deformation behavior. To better understand spatial variability and the effect of Ks on the slide mass of an accumulation landslide, this study introduced the surface nuclear magnetic resonance (SNMR) technology to study a representative reservoir accumulation landslide field in the Three Gorges Reservoir area (TGRA), the Baishuihe landslide, to obtain a series of relative reliable spatial measurements of Ks effectively on the basis of calibration in terms of the field tests measurements. The estimated Ks values were distributed log-normally for the overall landslide mass site with a wide range of 3.00 × 10−6∼7.80 × 10−3 cm/s, which reaches about 3 orders of magnitude. Variogram analysis indicated that the Ks values have the range (A) of 295.89 m and 65.56 m for the overall site and major cross-sectional analysis, respectively. A finite-element seepage-stress analysis associated with a Kriging-interpolated spatial Ks variable calculation model based on the best-fitted theoretical variogram was subsequently performed to study the seepage and deformation behavior of the landslide. The available monitored data and simulated results of the finite-element seepage-stress analysis indicated that the Baishuihe landslide is a progressive landslide, and the main factor influencing the deformation is rainfall and reservoir water fluctuation. This study provides an unconventional framework for studying the heterogeneous geomaterial and contributes to a better understanding of the spatial variation of the hydraulic property of accumulation reservoir landslides at a field scale. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

A field investigation of the Huangtupo landslide in the Three Gorges Reservoir area, China, indicated that the particle-size distribution—characterized by an index termed the fractal dimension—varied along the main sliding direction: the fractal dimension tended to increase in this direction. A preliminary study of the spatial variability of the slip zone based upon the fractal dimension was performed. Remolded soil samples with various fractal dimensions obtained from the Huangtupo landslide were prepared for laboratory testing that was carried out to determine the effect of the fractal dimension on the micro and macro behavior of the soils. The microstructure of each soil sample was determined using scanning electron microscopy, while its macromechanical properties were evaluated via consolidated undrained triaxial tests. The results showed that there were close relationships of the fractal dimension with the microstructure, stress-strain behavior, and shear strength. The test results were also consistent with the results of the field investigation of the Huangtupo landslide. Thus, the fractal dimension can be used as an indicator of the spatial variability of the slip zone of a landslide. [ABSTRACT FROM AUTHOR]

The response of the seepage field to the reservoir water level change greatly depends on the saturated hydraulic conductivity of reservoir landslides, which plays a critical role in their evolution processes. Currently, in situ tests and laboratory tests are the main approaches to obtain hydraulic conductivity. However, both of these methods have obvious drawbacks (e.g., size and location limitations); thus, the test results cannot be directly adopted for solving many geotechnical engineering problems that are of great inhomogeneity. To overcome these drawbacks, a back-analysis approach is proposed in this paper to obtain the saturated hydraulic conductivity based on groundwater observation. This approach includes three main steps: (1) develop a saturated-unsaturated seepage model based on field investigation; (2) use the Generalized Regression Neural Networks method to establish a non-linear mapping relation between saturated hydraulic conductivities and groundwater levels; and (3) employ the Genetic Algorithm method to search for the optimum solution for hydraulic conductivity under which the calculated groundwater levels in the seepage model fit best with the observational groundwater levels. Furthermore, this approach is applied to back analyze the hydraulic conductivities of the riverside slump I# in the Huangtupo landslide, which is volumetrically the largest reservoir landslide in the Three Gorges Reservoir area in China. The observational groundwater levels that are used to back analyze the hydraulic conductivities are the result of a response of the whole landslide to the reservoir water level change. Consequently, this approach overcomes the aforementioned limitations in the tests, and the results provide more reliable references for studying reservoir landslides. [ABSTRACT FROM AUTHOR]

Previous determinations of the optimal stabilizing pile location have generally been based on the idealized assumption of arc-shaped sliding surfaces. This assumption, however, may be inconsistent with the actual situation of most common irregular shapes, such as the step shape; as a result, determinations based on this assumption tend to involve considerable error. This paper presents a strategy, termed the local safety partitioning (LSP) methodology, for accurately determining optimal pile locations for step-shaped configurations. Instead of assuming the sliding surface to be arc-shaped, this strategy considers the actual sliding shapes. A new index termed the local safety factor is introduced for measuring the stabilities of local landslide masses, and this method employs the Swedish slice method to calculate the value of the index. The LSP methodology consists of four procedures: slicing the landslide mass into blocks, calculating the local safety factor of each sliced block, dividing the landslide mass into local partitions based on the previous step, and identifying the optimum pile position using previously identified partitions. The performance of the presented methodology is illustrated using the Jinle No. 2 landslide case for well-rounded demonstration and using the Yancun landslide case for additional validation. The results indicate that analysis of a reinforced landslide employing the LSP methodology acquires the largest safety factor and smallest deflection, shear force and bending moment on the pile body compared with any other case from a series of positions that incorporate traditional positions for arc-shaped sliding surfaces. The presented methodology provides a simple but accurate determination of the optimal stabilizing pile location for stepped sliding surfaces, although it may involve errors and unexpected limitations when applied to arc shapes and peculiar scenarios. [ABSTRACT FROM AUTHOR]

There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect. [ABSTRACT FROM AUTHOR]

The physical model test of landslides is important for studying landslide structural damage, and parameter measurement is key in this process. To meet the measurement requirements for deep displacement in landslide physical models, an automatic flexible inclinometer probe with good coupling and large deformation capacity was designed. The flexible inclinometer probe consists of several gravity acceleration sensing units that are protected and positioned by silicon encapsulation, all the units are connected to a 485-comunication bus. By sensing the two-axis tilt angle, the direction and magnitude of the displacement for a measurement unit can be calculated, then the overall displacement is accumulated according to all units, integrated from bottom to top in turn. In the conversion from angle to displacement, two spline interpolation methods are introduced to correct and resample the data; one is to interpolate the displacement after conversion, and the other is to interpolate the angle before conversion; compared with the result read from checkered paper, the latter is proved to have a better effect, with an additional condition that the displacement curve move up half the length of the unit. The flexible inclinometer is verified with respect to its principle and arrangement by a laboratory physical model test, and the test results are highly consistent with the actual deformation of the landslide model. [ABSTRACT FROM AUTHOR]

The Jiweishan rockslide of June 5, 2009 in China provides an important opportunity to elucidate the kinetic characteristics of high-speed, long-runout debris flows. A 2D discrete element model whose mechanical parameters were calibrated using basic field data was used to simulate the kinetic behavior of this catastrophic landslide. The model output shows that the Jiweishan debris flow lasted about 3 min, released a gravitational potential energy of about 6 × 10^13 J with collisions and friction dissipating approximately equal amounts of energy, and had a maximum fragment velocity of 60–70 m/s, almost twice the highest velocity of the overall slide mass (35 m/s). Notable simulated characteristics include the high velocity and energy of the slide material, the preservation of the original positional order of the slide blocks, the inverse vertical grading of blocks, and the downslope sorting of the slide deposits. Field observations that verify these features include uprooted trees in the frontal collision area of the air-blast wave, downslope reduction of average clast size, and undamaged plants atop huge blocks that prove their lack of downslope tumbling. The secondary acceleration effect and force chains derived from the numerical model help explain these deposit features and the long-distance transport. Our back-analyzed frictions of the motion path in the PFC model provide a reference for analyzing and predicting the motion of similar geological hazards. [ABSTRACT FROM AUTHOR]

Jurassic strata prone to slope failure are widely distributed in the Three Gorges Reservoir region. The limit equilibrium method is generally used to analyze the stability of rock slopes that have a single failure plane. However, the stability of a stratified rock mass cannot be accurately estimated by this method because different bedding planes have variable shear strength parameters. A modified limit equilibrium method is presented with variable water pressure and shear strength used to estimate the stability coefficient of a sloping mass of stratified rock and to identify the potential sliding surface. Furthermore, an S-curve model is used to define the spatial variations of the shear strength parameters c and ϕ of the bedding plane and the tensile strength of the rock mass. This model can also describe the variation of strength parameters with distance from the slope surface, which depends on the reservoir water level. Also, it is used to evaluate the stability of the Qianjiangping landslide, located at Shazhenxi Town, Zigui County, Three Gorges Reservoir area, China. The results show the most probable sliding surface is the interface between a slightly weathered layer and subjacent bedrock. When reservoir water rises above the elevation of the slide mass toe, the stability coefficient of the slope declines sharply. When the reservoir water level is static at 135 m, the stability coefficient decreases gradually as the phreatic line changes as a result of heavy rainfall. [ABSTRACT FROM AUTHOR]

The deformation and failure mechanism of landslides caused by reservoir water operations is a significant problem that is being addressed by researchers. The Xiaochatou Landslide in the Wudongde Reservoir area is taken as an example for the performance of centrifugal model tests under gravel conditions with the rapid drawdown of the reservoir water level. The data concerning the displacement and pore water pressure during this process show that the Xiaochatou Landslide, which is a complex landslide containing a shallow slide mass, middle slide mass and deep slide mass, is extremely sensitive to the rapid drawdown of the water level. The centrifuge tests show that the shallow slide mass will fail and that the middle slide mass will suffer a large displacement after a rapid decrease in the reservoir water level. Furthermore, the Xiaochatou Landslide exhibits the characteristics of retrogressive failure. [ABSTRACT FROM AUTHOR]