Landslide Simulation Research Articles

Accelerated Movements of Xiaomojiu Landslide Observed with SBAS-InSAR and Three-Dimensional Measurements, Upper Jinsha River, Eastern Tibet

Many ancient landslides in the upper reaches of the Jinsha River seriously threaten the safety of residents on both sides of the river. The river erosion and groundwater infiltration have greatly reduced the stability of the ancient landslides along the Jinsha River and revived many large landslides. Studying their deformation characteristics and mechanisms and predicting possible failure processes are significant to the safety of residents and hydropower projects. We used SBAS-InSAR and three-dimensional decomposition techniques in our study. Our results showed that the trailing edge and middle part of the landslide have rapidly deformed. The maximum vertical annual displacement rate was 12 cm/a period from July 2017 to July 2019. Correlation analysis showed that creep deformation is closely related to the river damming of the Baige landslide events and that the rising river level was an important factor in the resurrection and accelerated destruction of the Xiaomojiu landslide. As a result, we predicted the possible failure process of the Xiaomojiu landslide, which might have lasted 80 s and eventually formed a landslide deposit with a height of about 150 m, a length of approximately 1500 m, and an average width of 450 m. Our results provide data references for displacement monitoring and instability risk simulation of large landslides along the Jinsha River.

Three-dimensional simulations of large-scale long run-out landslides with a GPU-accelerated elasto-plastic SPH model

Smoothed particle hydrodynamics (SPH) is now becoming more and more welcome in modeling large deformation, soil failure and landslide in geotechnical and geological community. Aiming to solve the low efficiency problem in modeling large-scale long run-out landslide, this study implemented a GPU-accelerated SPH model based on the Drucker–Prager yield criterion with a non-associated flow rule on the open source platform DualSPHysics (v4.0). Two typical three-dimensional (3D) cases with simple boundary conditions were simulated to validate and demonstrate the high effectiveness and capacity of the GPU based elasto-plastic SPH model in modelling large deformation, failure, runout and deposit in a unified framework. Furthermore, we used the GPU-based elasto-plastic SPH model to simulate the Yigong avalanche and Shenzhen landslide to reproduce complete landslide dynamic processes and deposit detail, where a comparable deposit distribution with field survey was found. It is concluded that the implemented GPU-based elasto-plastic SPH model is able to simulate large scale long runout landslides on complex terrain to provide full process and deposit, which could be helpful for hazard assessment and mitigation.

Capture and Prediction of Rainfall-Induced Landslide Warning Signals Using an Attention-Based Temporal Convolutional Neural Network and Entropy Weight Methods.

The capture and prediction of rainfall-induced landslide warning signals is the premise for the implementation of landslide warning measures. An attention-fusion entropy weight method (En-Attn) for capturing warning features is proposed. An attention-based temporal convolutional neural network (ATCN) is used to predict the warning signals. Specifically, the sensor data are analyzed using Pearson correlation analysis after obtaining data from the sensors on rainfall, moisture content, displacement, and soil stress. The comprehensive evaluation score is obtained offline using multiple entropy weight methods. Then, the attention mechanism is used to weight and sum different entropy values to obtain the final landslide hazard degree (LHD). The LHD realizes the warning signal capture of the sensor data. The prediction process adopts a model built by ATCN and uses a sliding window for online dynamic prediction. The input is the landslide sensor data at the last moment, and the output is the LHD at the future moment. The effectiveness of the method is verified by two datasets obtained from the rainfall-induced landslide simulation experiment.

Use of low-cost accelerometers for landslides monitoring: results from a flume experiment

Early Warning Systems (EWS) are non-structural measures for landslides disaster prevention. They are based on the detection of impending failure signals. The results of a landslide simulation experiment where accelerometers were used to identify pre-failure signals are presented in this paper. Landslide was simulated in a tilting flume filled with sandy soil. During the experiment, the flume was fixed at 30° inclination and water percolated through the soil until it slid. Accelerometers were embedded into the soil and recorded acceleration data from the beginning of the experiment until failure. Acceleration data were analyzed in time domain aiming at estimating translational velocity of the movement. Angular variation was also estimated from acceleration data. The experiment was recorded with a camera and pictures were used for Particle Image Velocimetry (PIV) analysis, in order to validate the estimated translational velocity. Results showed that accelerometers can identify prefailure signals before any macroscopic movement could indicate impending failure in fast to very fast landslides, showing their potential to be used in EWS. Validation of estimated velocities was not always possible due to PIV setup constraints and the velocity of the mass movement simulated. In fact, the estimated translational velocities seem to be unreliable. On the other hand, the results suggest that acceleration data and angular position variation trend and rate can be incorporated into EWS.

Dynamic stability assessment of reservoir colluvial landslide using a hypoplastic clay constitutive model considering the effects of drying-wetting cycles on the hydro-fluctuation belt

The hydro-fluctuation belt of reservoir landslides undergoes repeated drying-wetting (D–W) cycles due to the annual fluctuation of water level in the Three Gorges Reservoir Area. Mechanical properties of the hydro-fluctuation belt subjected to D-W cycles should be considered for the evolution of landslide stability affected by seasonal rainfall and periodic reservoir water fluctuations. In addition, a reliable constitutive model essential for evaluating landslide stability should be employed to describe the mechanical property of soil. In this study, taking the Tangjiao landslide as an example, a series of drained triaxial and oedometer tests were conducted to study the effects of D-W cycles on the shearing and compression behaviors of the sliding mass soil. Then, the time-varying parameters of a hypoplastic clay constitutive model were calibrated based on the soil experiments under different D-W cycles. Eventually, the hydro-mechanical coupled simulation analyses were performed to study long-term deformations of the landslide front part influenced by actual rainfall and reservoir water fluctuations. Moreover, the evolution of landslide stability was analyzed using the vector-sum method based on stress fields with different time steps. The test results show that the D-W cycle significantly affects the mechanical property of soil. The hypoplastic model predicts well the shearing and compression behaviors of soil under different D-W cycles. The numerical results indicate that the soil weakening in the hydro-fluctuation belt increases the deformation magnitude of the toe slope and gradually decreases the landslide stability. This study provides some references for the dynamic stability assessment and long-term hydro-mechanical simulation of reservoir landslides.

Finite Volume Non-hydrostatic Pressure Model for the Simulation of Landslides

A Finite Volume (FV) numerical scheme previously designed for the hydrostatic Shallow Water Equations (SWE) is extended to compute non-hydrostatic pressure (NHP) in order to simulate dispersive waves. These waves are usually generated by landslides and are of importance in coastal alarm systems. The model is validated in 1D comparing with laboratory measurements and the results highlight the importance of including the NHP terms in the model to achieve a good agreement between numerical and experimental results.

Isabel Echeverribar - Finite Volume Non-Hydrostatic Pressure (NHP) Model for the Simulation of Landslides

Finite Volume Non-Hydrostatic Pressure (NHP) Model for the Simulation of Landslides

Influence of DEM resolution on landslide simulation performance based on the Scoops3D model

Using the physical deterministic model to analyze landslide stability has become a hotspot of landslide disasters research all over the world. The Digital Elevation Model (DEM) resolution has a great influence on the simulation effect of 3 D physical models. However, few researchers have studied the prediction performance of the 3 D models under different DEM resolutions. Therefore, based on the 3 D model Scoops3D, the spatial distribution of landslides was simulated and predicted under five different DEMs resolutions (2.5 m, 5 m, 10 m, 20 m, and 30 m). The optimal parameters of the model were obtained through field investigations and laboratory experiments, and then, the simulation results were compared with the actual landslides distribution. Receiver operating characteristic (ROC) analysis and %LRclass index were used to quantitatively evaluate the prediction performance of the 3 D model under five different DEMs resolutions. The results show that Scoops3D has good performance in landslide spatial distribution prediction. In addition, we also found that the simulation results of high-resolution DEM were not ideal, while the prediction results of medium resolution DEMs (i.e., 5 m and 10 m) were more accurate. Therefore, this study provide a reference to select the most suitable DEM resolution for landslide stability analysis.

SIMULASI PENGUKURAN LONGSOR PADA KELERENGAN DAN KEDALAMAN BIDANG GELINCIR YANG BERBEDA

Landslide is one of the geological disasters that often occurs in Indonesia. Landslides can be caused by several factors; there are human factors and natural factors. In principle, landslides occur when the resisting force is smaller than the driving force. Landslides on a small and large scale can occur over time and have a fairly severe impact. Therefore, this study simulated the effect of slope and depth of slip surface on the duration and volume of landslides, which are simulated on two soils with different texture classes. Landslide simulations were conducted at Agro Techno Park and The Experimental Gardens Faculty of Agriculture. The simulation used eight treatments and ten replications consisting of three factors there are soil texture, slope and depth of slickenside. Landslide simulation was conducted using an acrylic box with a size of 100 cm x 50 cm x 75 cm with rain intensity 70 mm hour-1. The variables measured are landslides duration, landslides volume, soil texture and bulk density. Data analysis used an independent sample t-test. The result showed slope and slickenside have a significant effect on landslides duration and landslides volume. The greater the slope, the landslide occurs faster on landslides duration and the greater the volume of the landslide. The deeper slickenside has a longer landslides duration, but the volume of the landslide is larger. It showed that the greater slope has a smaller influence on the depth of slickenside and soil texture on the landslides duration and landslides volume.

HUBUNGAN KEMIRINGAN LERENG DAN PERSENTASE BATUAN PERMUKAAN TERHADAP LONGSOR BERDASARKAN HASIL SIMULASI

Landslide is the process of moving slope-forming material that moves out of the slope. Landslides cause adverse impacts, such as damage to residences, public facilities, death tolls, and damage to agricultural land. Factors for landslides are influenced by disturbing slope stability due to steep slopes, high rainfall intensity, and movement of soil, rock, or a mixture of both. The research was conducted at Agro Techno Park of Brawijaya University (silt loam texture soil) and the experimental field of Faculty of Agriculture Brawijaya University (clay texture soil). The landslide simulation tool used acrylic (100 cm x 50 cm x 75 cm), with an aluminum plate as a base for adjusting the slope. Artificial rain was set to be stable at an intensity of 70 mm hour-1. This study used a combination of three factors, namely soil texture (C: silt loam and J: clay); slope (L1: 40° and L2: 50°); and surface rock (B1: 25% and B2: 50%), so that there were eight treatments with ten repetitions. The landslide duration and volume were measured. The data obtained were then statistically analyzed through the normality test and the unpaired t-test. The results show that increasing slope gradient from 40° to 50° on silty loam texture with similar rock content at the soil surface resulted in 6 times faster and two times larger landslide volume. At a very steep slope, the effect of soil texture and % rocks at the surface would be smaller on landslide duration but became more significant on landslide volume. Generally, the landslide factors can be ranked from the most significant effect as follows: slope gradient, soil texture, and rocks coverage at the surface.

SIMULASI PENDUGAAN LONGSOR: PENGARUH INTENSITAS HUJAN PADA TANAH DENGAN TEKSTUR DAN KANDUNGAN BAHAN ORGANIK YANG BERBEDA

Natural factors and human actions can cause landslides. Natural factors include high rainfall intensity and mountainous areas or hills with steep slopes. Human actions that can cause landslides are land conversion and inappropriate land management. This study aimed to explore the effect of rain intensity on soils with different textures and organic matter content. The landslide simulation study was conducted at the Agro Techno Park and Experimental Field, Faculty of Agriculture, Brawijaya University. This study used an acrylic box (size 100 cm x 50 cm x 75 cm) and aluminium plate as a base, which was adjusted to a slope of 50. There were eight treatment combinations consisting of three factors: soil texture (silty and clayey), rainfall intensity (70 mm hour-1 and 120 mm hour-1 ), and organic matter application (without and with organic matter application). The variables measured were landslide duration and volume. The results showed that the duration and volume of landslide materials are largerly influenced by rainfall intensity. Adding organic matter to the soil at any rainfall intensity may decrease landslide duration and volume by half compared to without organic matter application. Among the three factors examined (soil texture, organic matter application and rainfall intensity), rainfall intensity seems to be the most determining factor in landslide duration and volume.

INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN

Abstract. Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.

A phase-change approach to landslide simulations: Coupling finite strain elastoplastic TLSPH with non-Newtonian IISPH

The present work shows a novel phase-change concept for simulating landslides using the smoothed particles hydrodynamics (SPH) method. The idea is to initiate the simulation of a slope stability problem with a Solid Mechanics-based SPH, modeling the soil as an elastoplastic material at finite strain. Next, if a particle exceeds a certain level of plastic strain, such particle changes its phase to a fluid state with non-Newtonian rheology, which is then solved with a Fluid Dynamics-based SPH method. We use the total Lagrangian SPH (TLSPH) method to solve the Solid Mechanics phase to avoid problems related to particle distribution (such as the tensile instability), while the implicit incompressible SPH (IISPH) to solve the Fluid Dynamics part to avoid the restriction on time increment in relation to high values of viscosity. The coupling between the two phases is treated as a conventional fluid–solid interaction (FSI) problem. We verified the proposed TLSPH method with the triaxial compression problem and demonstrated the robustness of the proposed phase-change TLSPH–IISPH coupled method in the simulation of the Aso landslide. Specifically, it may be the first time to simulate the Aso landslide from its initiation to its propagation in a single numerical simulation.

A new GPU-accelerated coupled discrete element and depth-averaged model for simulation of flow-like landslides

Flow-like landslides are a common type of natural hazards that may impose a great risk to people and their properties. Different models have been reported for simulating flow-like landslides, all of which possess limitations due to the underlying assumptions and simplifications. Harnessing the advantages of two types of prevailing modelling approaches, a new coupled model is developed which adopts a discrete element method (DEM) model to simulate the complex collapsing process in the source area and a depth-averaged model (DAM) to predict the predominantly convective movement in the runout and deposition zones. The coupled model is finally implemented on the NVIDIA CUDA programming platform to achieve GPU high-performance computing. Two laboratory tests are considered to validate the model and a field-scale landslide event is simulated to verify its applicability in real-world conditions. Satisfactory results confirm that the coupled model is able to reproduce the dynamic process of real-world flow-like landslides.

Extended DDA with rotation remedies and cohesive crack model for simulation of the dynamic seismic landslide

The failure of earthquake-induced landslides generally involves four stages: tensile cracking, breaking of the substrate, shearing failure, and deposition of the sliding mass, which poses a challenge for high-accuracy simulation of such landslides. To resolve this issue, this paper presents an integrated algorithm by introducing a bilinear cohesive contact model into the proposed discontinuous deformation analysis (DDA) method. In the proposed DDA, the displacement mode of the original DDA is replaced by the multiplicative decomposition of the displacement to improve the distortion and false volume expansion of blocks with large rotations. The timely introduction of the bilinear cohesive contact model can better describe the peak-post softening behavior of rocks consisting of blocks. Finally, the extended algorithm is formulated and implemented in the source program of the DDA and then verified through typical examples. The results indicate that the proposed method can facilitate the generation of tensile cracks and effectively eliminate distortion and false volume expansion of blocks, demonstrating consistent results with the test or field investigation.

Comparisons of Dynamic Landslide Models on GIS Platforms

Numerical simulation is one of the methods to assess landslide movement processes, which is beneficial for engineering design and urban planning. With the development of computer technology, GIS has gradually become the mainstream platform for landslide simulation due to data availability and algorithm integrability. However, the dynamic processes of landslides are complicated, which makes integration difficult on GIS platforms. Some assumptions are applied to simplify these dynamic processes and solve this problem. Generally, there are two main types of numerical models on GIS platforms: models based on the Eulerian description and models based on the Lagrangian description. Case studies show that Eulerian models are suitable for flow-like movement, and Lagrangian models are suitable for discrete rigid bodies movement. Different models face different problems: the Eulerian-based models show numerical diffusion and oscillation, and the Lagrangian-based model needs to consider complicated shear and collision processes. In addition, the 3-D model can describe more details in the z-direction, while the depth-averaged model can obtain a reasonable range of motion, depth, and speed quickly. From the view of numerical simulation, inappropriate models, assumptions, and numerical schemes will produce errors. The landslide type refers to several forms of mass wasting associated with a wide range of ground movements, which guides establishing dynamic models and numerical schemes on GIS platforms and helps us obtain results accurately.

Forensic analysis and numerical simulation of a catastrophic landslide of dissolved and fractured rock slope subject to underground mining

Forensic analysis and numerical simulation of a catastrophic landslide of dissolved and fractured rock slope subject to underground mining

Scenario simulation of the geohazard dynamic process of large-scale landslides: a case study of the Xiaomojiu landslide along the Jinsha River

Large-scale landslides often cause severe damage due to their long run-out distances and having disaster chain effects. Scenario simulation has been adopted in the current work in order to analyze the Xiaomojiu landslide dynamic processes. The landslide characteristics and topography data are obtained via field investigations, whereas high-resolution topographic data (0.17 m) are obtained using an Unmanned Aerial Vehicle. The landslide sliding velocity, deposition characteristics, and flood outburst after a landslide dam failure were obtained using Particle Flow Code (PFC-3D) which introduced the changeable friction coefficient and the HEC-RAS software. The results showed that: 1. The landslide presents a scallop shape with a length of 1110 m, an average width of 950 m, and an area of 1.05 × 106 m2. The average thickness and volume of the sliding body are approximately 50 m and 5.45 × 107 m3. The InSAR (Interferometric Synthetic Aperture Radar) deformation analysis showed that the Xiaomojiu landslide has a maximum annual displacement rate of 60 mm/y and a maximum accumulation deformation of 180 mm since November 25, 2017. 2. The failure process of the Xiaomojiu landslide lasted for 65 s with a maximum velocity of 78.2 m/s. According to the landslide simulation results, the deposited area is approximately 2023 m long, 900 m wide, and has a maximum height of approximately 149 m. 3. A landslide-dammed lake with an elevation of 2940 m and a storage capacity of 4.13 × 109 m3 is formed after the landslide blocks the Jinsha River, and the maximum peak flow rate of the breach is 12051.7 m3/s, 43,451.4 m3/s, 148,635.6 m3/s, and 304,544.7 m3/s for the landslide-dammed failure degrees of 15%, 25%, 50%, and 75%, respectively. These results provide a reference for the risk analysis and mitigation of the landslide.

An improved multi-phase weakly-compressible SPH model for modeling various landslides

A novel weakly compressible multi-phase SPH method is developed for modeling various landslides. In this model, the multi-phase system of fluid and granular materials is regarded as a continuum with multi-viscosity and multi-density. The mechanical behavior of the granular materials is described by the μ(I) rheological model and the dynamic effective pressure. An incremental diffusive term is developed to eliminate the non-physical fluctuation in the pressure field of multi-phase flow. A dynamic calculation method of effective pressure is introduced to overcome the additional calculation and error caused by the traditional method in searching for the phase interface. A friction boundary condition called the momentum method is introduced to consider the influence of basal friction on the evolution of granular landslides. The Particle Shifting Technique (PST) of the single-phase WCSPH is introduced into the multi-phase WCSPH by the two-step shifting procedure to ensure the uniform distribution of particles in each phase and phase interface during the simulation process. The effectiveness of the incremental diffusive term is first validated using a benchmark case of the two phases hydrostatic stratified column. Then the model is applied to the numerical simulation of dry granular landslide, sub-aerial granular landslide, submerged granular landslide, and rigid landslide. The calculated results are compared with experimental results and previous numerical models to verify and evaluate the performance of the current model. Proven that this model is well consistent with experimental results and has higher accuracy than previous numerical models.

Landslide Risk Prediction Model Using an Attention-Based Temporal Convolutional Network Connected to a Recurrent Neural Network

Landslide risk assessment is an important component of the landslide research field. For the problem of landslide assessment indicators, we utilize the TOPSIS-Entropy method to assess the risk situation of landslide occurrences, which is easy to obtain directly from sensor data. By using the TOPSIS-Entropy method in landslide datasets, the instability margins of landslide risk are obtained, reflecting the current instability probability of the landslide body. For the landslide prediction issue, deep neural networks are used to predict the corresponding landslide instability margins (LIMs). Attention mechanism-based (Attn) temporal convolutional networks (TCN) connected with recurrent neural network (RNN) models for landslide risk prediction are proposed, including TCN-Attn-RNN and RNN-Attn-TCN, which both use an encoder–decoder architecture. The encoder in the first model uses the temporal convolutional network (TCN), and the decoder uses a neural network with an RNN architecture, including long short-term memory (LSTM) networks, gated recurrent units (GRUs), and their derivative algorithms. In the second model, the encoder uses a neural network with an RNN architecture, and the decoder uses a TCN. Combining the TOPSIS-Entropy method with TCN-Attn-RNN and RNN-Attn-TCN, reliable prediction models of landslide risk are proposed. By building a landslide simulation platform, we obtained landslide data. Compared to their counterparts, the proposed prediction models of landslide risk instability margins have better predictive effects.