Landslide Cases Research Articles

Investigação da relação chuva-deslizamentos no município de Mauá – SP para obtenção de limiares críticos deflagradores de deslizamentos

The Mauá city, Region of Grande ABC Paulista, is among the five municipalities of the São Paulo state with the most critical situation in relation to the number of records of geological accidents, which makes it vulnerable to intense and persistent rainfall. This study investigates the relationship between precipitation events and landslide occurrence in this municipality from the geological-geotechnical characterization of samples representative of the horizons most susceptible to the region's geodynamic processes, precipitation patterns and the frequency of landslides. It was found that rains of different intensities and durations can increase the number of landslides. In addition, the geological and geotechnical characteristics of the region's pedological horizons (residual soil from mica schist and granite) and anthropic interventions contribute to the magnitude of susceptibility to geodynamic and related processes. The curves of landslides obtained, for different criteria of daily and accumulated precipitation before the occurrence, showed that the greater the accumulated value of precipitation, the lower the intensity of daily precipitation necessary to trigger the landslides. The criteria indicated the daily total of 35 mm as the most restrictive, regardless of the value accumulated in days prior to the occurrences, which was validated with the cases of recent landslides occurred in the region, not considered in the calibration of the adjustment equation. In addition, in periods without rain in the previous days, the landslides will tend to occur with rainfall greater than 70 mm on the day of the event, an increase of this value may be observed with the increase in the number of previous dry days.

Implementation of a J-integral based Maximum Circumferential Tensile Stress theory in DDA for simulating crack propagation

Crack propagation is a very important research subject within the field of rock mechanics. In this study, a J-integral based Maximum Circumferential Tensile Stress theory was applied to the conventional Discontinuous Deformation Analysis (DDA) Method for simulating crack propagation. First, according to the relevant theories of fracture mechanics, a crack propagation theory suitable for DDA was derived by calculating J-integral in DDA simulation. Then, the crack propagation algorithm was embedded in the DDA algorithm with an accurate judgement of the propagation direction. After that, the numerical calculation results were compared with the experimental results to verify the correctness of the crack propagation algorithm. The validated method was finally applied to simulate a landslide case with a preexisting crack. The results showed that the proposed method was applicable to simulating crack propagations in practical cases.

Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

Reliability of an engineered slope considering the Regression Kriging (RK)-based conditional random field

Many attempts have been made to apply random field theory to the slope reliability analysis in recent decades. However, there are only a few studies that consider real landslide cases by incorporating actual soil data in the probabilistic slope stability analysis with spatially variable soils. In this paper, an engineered slope located in Hong Kong was investigated using the probabilistic approach considering the Regression Kriging (RK)-based conditional random field. The slope had been assessed and considered to be safe by classical deterministic slope stability analyses but failed eventually. In this study, both deterministic slope stability analyses and probabilistic slope stability analyses were conducted, and the comparison was made between the probabilistic approach adopting RK-based conditional random field and that adopting Ordinary Kriging (OK)-based approach. The results show that the deterministic factor of safety (FS) for a slope may not be an adequate indicator of the safety margin. In particular, a slope with a higher deterministic FS may not always represent a lower probability of failure under the framework of probabilistic assessment, where the spatial variability of soil properties is explicitly considered. Besides, the critical portion of the slope could not be found using the OK-based approach that considers a constant trend structure.

Ohuka landslide, New Zealand: a low angle bedding-controlled coastal landslide at Port Waikato, North Island, New Zealand

ABSTRACT Overseas, relict deep-seated coastal landslides have attracted significant analysis because of potential hazards caused by sea level rise and climate change. Despite the rapid growth in the Auckland region, regional coastal landslide hazards are poorly understood. We investigated the geomorphology and material properties of Ohuka coastal landslide on the southwestern coastline of the Auckland region, to better understand the failure mechanism. The site is formed on weak Neogene sediments of varying lithological properties, with very low angles of dip, exposed on both the seaward (west) and landward (east) flanks of Ohuka Hill. Displacement appears to have been rotational, along a low-angled basal shear surface, the Koheroa Clay Seam. The surface geomorphology consists of discontinuous uphill-facing scarplets indictive of rotational slump blocks, along the basal shear surface. However, the preservation of slump block surface topography is hindered due to the lack of a thick, strong caprock, and high annual rainfall (1400 mm), and erosion. The proposed failure mechanism and general geometry is consistent with correlations between graben width and depth to failure plane reported from bedding-controlled coastal landslides formed on low dip angles in southern England. This provides important context, given the paucity of New Zealand bedding-controlled coastal landslide case studies.

Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide

Power systems are stretched across thousands of miles of diverse territories, often in remote locations, to generate and transfer the energy to geographically dispersed customers. The system is therefore subjected to a wide range of natural hazards which could potentially damage critical system components and cause interruption of electricity supply in some areas. To improve system resilience against natural hazards, management frameworks are required to identify hazardous areas and prioritize reinforcement activities in order to take the most out of the limited resources. Landslide is a natural disaster that involves the breakup and downhill flow of rock, mud, water, and anything caught in the path. It is a phenomenon frequently occurred in some parts of the world that could result in the failure of power transmission networks. Consequently, in this paper, a novel approach has been proposed that quantifies the landslide hazard, its damage to power system components, and the impacts on the overall system performance to prioritize reinforcement activities and mitigate the landslide vulnerability. The proposed approach is applied to a real power system and the obtained results are discussed in detail.

The reason analysis of the overburden rock dumps deformation

Due to the deficiency of areas for dump facilities, mining enterprises form dumps of a large height, or locate them in places unfavorable from the stability point of view. In this regard, cases of the dump areas landslides have become more frequent. Herein there are examples of the actual deformations, causes of their occurrence, as well as preventive measures. Experience of the field surveying observations and analysis of the dump landslide causes confirm that accidents could be prevented by timely substantiating of safety criteria for the stress strain status of the dumps and creating methodology for surveying monitoring, depending on a type of the overburden rock.

Application of stochastic methods (genetic algorithm-tabu search) to identify the parameters of Mohr Coulomb model

The problem of the choice of the parameters of soil is a difficult task, she requires many experiences. To answer this question, methods of identification of parameters, based on the principle of inverse analysis are developed who consist in adjusting a numerical model on observed experimental data. The objective of this work is to apply the principle of inverse analysis by using stochastic optimization methods (genetic algorithm and the genetic algorithm hybrid with tabu search) to identify the parameters (G, φ) of the constitutive soil model Mohr-Coulomb, from a real case of landslide of the Ciloc city of Constantine in Algeria. The Analysis of the results obtained showed that the best sets of parameters (G, φ) which minimize the deviation between the numerical model and the experiment are found by the genetic algorithm method.

Modelling rainfall-induced landslides from initiation of instability to post-failure

This paper presents a model for rainfall-induced shallow landslides, revealing the failure mechanism from initiation of instability to final flow failure. The onset of slope failure occurs with a completely formed plastic shear band that is triggered by a rainfall-induced increase in pore pressure. The subsequent flow-like deformation is caused by the strain-softening effect associated with large strains. Due to the complex non-linearity in stress-seepage coupling and large deformation, modelling the entire progressive failure induced by rainfall is always a challenge. Therefore, the model in this paper uses a two-step strategy, i.e., coupled hydro-mechanical finite element (FE) analysis for step 1 and large deformation FE analysis for step 2, to realize the simulation of the entire failure process. An equivalent strength method is used to ensure the physical continuity between step 1 and step 2. A more reasonable evaluation of large deformation during post-failure can be achieved without a predefined slip surface. The model is applied to the analysis of two real rainfall-induced landslide cases. Numerical analysis demonstrates the ability of this model to evaluate the progressive variations in pore pressure, displacement, plastic strain, and factor of safety (FOS) with rainfall infiltration and simulate the final flow-like deformation.

A multidisciplinary approach for landslide residual risk assessment: the Pomarico landslide (Basilicata Region, Southern Italy) case study

On 25 and 29 January 2019, a large landslide destroyed an important part of the town of Pomarico (Basilicata Region, Southern Italy). Geological and geomorphological investigations provided a detailed description of the landslide features. Several geophysical surveys were carried out to deepen knowledge of the landslide and the residual risk assessment. Detailed electrical resistivity tomography (ERT), multichannel analysis of surface waves (MASW), and seismic refraction tomography (SRT) have been used to analyze geomorphological evidences of the failure and the potential kinematic evolution of the landslide scarp, a crucial factor to assess landslide residual risk. The joint analyses of the geophysical results, compared with geological and geomorphological data, allowed to obtain detailed information about the stratigraphic contact between clayey and sandy deposits in the crown area of the landslide, and to identify the post-failure stability condition changes in sands. The geophysical analyses confirmed the presence of multiple old degraded scarps developed over time and provided information on the decompression state of the different areas of the landslide crown. The results highlighted a subparallel stratification consisting of an anthropic surface carry-over material, which covers a layer of sands with silty intercalations, overlying clayey material that represents the bedrock of the investigated area. Furthermore, natural and anthropogenic caves, mainly developed in well-cemented layers of sands, were identified. This study emphasized how the integration of different geophysical methods constitutes a capable tool for characterizing landslides, contributing to assessing the landslide residual risk of the slope mass and evaluating the suitability of the methods in relation to the investigated landslide conditions.

Post-Failure Dynamics of Rainfall-Induced Landslide in Oltrepò Pavese

Prediction of landslide hazard risk at hill slope induced by intense rainfall requires the appropriate modeling of the interactions between soil and weather phenomena, leading to failure as well as a reliable prediction of post-failure dynamics. In the peculiar case of fast shallow landslides behaving like dense granular flows, a suitable modeling approach for large and rapid deformations is necessary to estimate potential related damage. The impact force exerted by the leading edge of the earth-flow on the downstream structure should be estimated for both damage prediction and design of effective protection measures. In this paper, a free open source 3D research code based on standard weakly compressible smoothed particle hydrodynamics (WCSPH) method is validated by modeling a full-scale rainfall-induced shallow landslide which occurred in Oltrepò Pavese (Northern Italy). The code allows resolving the vertical velocity gradients, potentially providing a more reliable representation of the landslide dynamics and impact force. Mechanical parameters are consistent with average soil characteristics, avoiding calibration analysis. The final landslide profile is compared with an experimental survey for model validation, showing good fit. Influence of uncertainties of geotechnical parameters on the landslide front velocity and impact force on the downstream wall is evaluated.

Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey)

This study presents a successful combination of different remote sensing data used in a long-term retrospective investigation of a large and destructive deep-seated, slow-moving landslide reactivated on 16 July 2015 in Devrek District (Zonguldak, Turkey). To this aim, Synthetic Aperture Radar (SAR) data were used for Interferometric SAR (InSAR) time-series analysis together with unmanned aerial vehicle (UAV) images and aerial photographs for digital photogrammetric analysis. The SAR dataset was divided into three sub-periods: 1) 1992–2001 for ERS-1 and ERS-2 satellites; 2) 2003–2010 for Envisat ASAR; and 3) 2014–2015 for Sentinel-1. Persistent Scatterers Interferometry (PSI) was applied for each sub-period. In total, 20 aerial photographs, dating from as early as 1944, were obtained, along with data from a UAV flight mission conducted on 23 June 2018. The historical aerial photographs revealed that the region has had a landslide problem since the 1940s. Between 1944 and 2018, a noticeable expansion of the settlement area towards the toe of the landslide was also observed. Aerial photographs (1984 and 2011) and UAV images (2018) were used to map landslide deformations using the M3C2 algorithm. Due to the high number of modelling errors, the 1984 and 2011 aerial photographs did not allow mapping of the landslide deformations. However, it was possible to determine them for the periods of 2011 and 2018. The M3C2 results between 2011 and 2018 were also compared to the PSI results, which were quite compatible with those obtained via photogrammetric methods. Moreover, two orthophotos belonging to 2011 and 2018 were used to reveal the horizontal displacement of buildings caused by the landslide. As a result, the complete investigation of the landslide performed in this study may serve to facilitate additional plans and strategies for prevention and mitigation of potential reactivations in the future.

Rainfall-Induced Landslides in Cameron Highland Area, Malaysia

Cameron Highland is classified as one of the landslide-prone areas in Malaysia due to its hilly landform. It has been discovered that the landslides in Cameron Highland were mainly triggered by the intense rainfall since the area encountered high amount of rainfall throughout the year. This study is carried out to evaluate the correlation between the rainfall intensity- duration (I-D) and the landslide occurrences in the Cameron Highland area. Twelve cases of landslides in the study area had been selected for conducting the analysis of rainfall intensity- duration (I-D) that triggers the landslides. The important variables from the analysis such as the maximum rainfall intensity (I) and the duration of rainfall series (D) have been applied to establish the empirical rainfall intensity-duration (I-D) threshold for Cameron Highland landslide areas. Based on the study, by utilising the logarithmic scale graph and applying a power-law model from the general equation of 1= <xDp, the empirical I-D threshold for Cameron Highland landslide was determined as I = 29.088D0075 (I = rainfall intensity in mm/hr and D = duration in hour). The empirical (I-D) threshold can be a functional mechanism for the Early Warning System (EWS) once it is further developed, that enable the relevant authority to prepare mitigation measures such as evacuation, spreading information to the civilian in order to prevent major losses and casualties due to the landslide events.

Landslide evolution assessment based on InSAR and real-time monitoring of a large reactivated landslide, Wenchuan, China

The upper Minjiang catchment is frequently affected by a significant number of large to giant landslides. In one such massive older landslide, approximately 2 million m3 of sediment was reactivated, threatening the safety of a pylon on the landslide and the safety of the only route that allows high-speed travel from Wenchuan to Maoxian. To trace the deformation evolution history and analyze the current deformation of the new landslide, we examined SAR data since 2007, carried out geological and UAV surveys, and designed and installed real-time monitoring equipment. This paper presents the main spatial and temporal characteristics of the landslide. We found that the landslide has experienced creep deformation in the past five years and its sensitivity to rainfall. The development process of the landslide from the formation of tensile crack at the trailing edge to the gradual pushing forward of the crack propagation was revealed. The innovation of this paper is to reveal the whole process of the spatial-temporal evolution from its beginning to large deformation of a real case of creep landslide, which can provide a very good reference for the analysis and monitoring of such creeping landslide evolution process. This representative case also shows how to combine remote sensing and in situ monitoring methods to carry out continuous monitoring and the establishment of a warning system for potential landslide sites, so as to trace the evolution history, derive activity maps and analyze development trend, which can significantly reduce landslide risk. Additional mitigation procedures can be put into place if continuous monitoring of the Minjiang River Canyon occurs, resulting in a reduction of direct losses. In particular, modeling of the slope deformation and failure, damming process, and prospective flooding can be done in advance. Therefore, it is expected that this report can strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods and in situ monitoring systems as an approach to reduce risk.

Pre- and post-failure dynamics of landslides in the Northern Apennines revealed by space-borne synthetic aperture radar interferometry (InSAR)

Landslides are common landscape features in the Northern Apennine mountain chain and cause frequent damages to human structures and infrastructure. Most landslides in the area can be classified as earthflows, where the clay-shales form the substrate, whereas complex landslides with flow and sliding components are common on the slopes where fine-grained turbidites form the substrate. Most of these landslides move periodically with contained velocities and, only after particular rainfall events, some of them accelerate abruptly. Space-borne synthetic aperture radar interferometry (InSAR) provides a particularly convenient way for studying the periods before and after failures. In this paper, we present InSAR-results derived from the Sentinel 1 satellite constellation for two landslide cases in the Northern Apennines. The first case is a complex landslide that is hosted on a pelitic flysch formation, whereas the second case is an earthflow located in chaotic clay shales. Both cases failed catastrophically and threatened or damaged important infrastructures. In the case of the complex landslide, we report spatially variations of the deformation field between repeated periods of acceleration. The data illustrate that the deformation initiated in the upper part of the slope and expanded over the whole landslide body afterward. In the case of the earthflow, we describe spatial and temporal kinematics during the period before a catastrophic failure in March 2018. We discuss the temporal deformation signal together with rainfall and snowmelt data from a nearby meteorological station. Deformation and precipitation data highlight that high total precipitation can be considered the trigger of the failure.

A new grey prediction model and its application to predicting landslide displacement

Landslide displacement prediction is an important part of reducing landslide hazard losses. The existing methods to predict landslide displacement are too complicated to be applied to engineering practice. The landslide displacement evolution and the grey model prediction mechanism show a good consistency. However, the existing grey prediction model also has some shortcomings including neglecting time term. A new grey prediction model called the background value optimization nonlinear grey prediction model (BNGM(1,1, t2)) is proposed to overcome the deficiencies. BNGM(1,1, t2) is a univariate prediction model that incorporates the influence of the time term. The integration method is used to determine the background value, and the minimum value method is employed to obtain a constant term of time response functions. Five accuracy test methods for BNGM(1,1, t2) are examined. BNGM(1,1, t2) can show better performances than other multivariate prediction models including the recursive discrete multivariate grey prediction model. BNGM(1,1, t2) is applied to four typical landslide case studies. The results indicate that the BNGM(1,1, t2) has the best prediction accuracy. The complexity of BNGM(1,1, t2) is lower than the nonlinear grey Bernoulli model, the Weibull–Bernoulli grey model, and the fractional accumulation nonlinear grey Bernoulli model. The comparison of comprehensive results demonstrates that the BNGM(1,1, t2)-based method has a wide application potential to predict landslide displacement.

Analisis Numerik Pengaruh Infiltrasi Hujan Terhadap Stabilitas Lereng Di Pangkalan, Sumatera Barat

The landslides event was triggered by rain infiltration is an annual occurrence in Indonesia, majority of landslide occur in rainy season. In this research, the case of landslide taken in Pangkalan Area, District of Limapuluh Kota, West Sumatera. The location of the case study is the main access of West Sumatra-Riau, so the landslide in the location is certainly causing close the road. Research phase is geology mapping, geotechnical analysis, and hydrological modeling. Hydrological modeling is done by numerical simulation using laboratory data. The modeling results show that the rain infiltration process resulted in the formation of positive water pressure zone at the foot of the slope, then spread towards the top of the slope. One day after the rainfall, the soil layer had been saturated. The soil layer will saturate the water, so that the slope stability will decrease and the landslide event will occur. To improve the slope stability, it can be done by preventing water from entering the permeable layer with the installation of the shotcrete layer, and draining the water from the slopes by the installation of horizontal drain.

Numerical simulation of the 1995 rainfall-induced Fei Tsui Road landslide in Hong Kong: new insights from hydro-mechanically coupled material point method

A rainfall-induced landslide case history in Hong Kong, i.e., the Fei Tsui Road landslide on 13 August 1995, is simulated in this study using a two-phase material point method (MPM). With the aid of hydro-mechanically coupled two-phase MPM, the entire process of the landslide case history is simulated, starting from rainfall infiltration, to advancement of wetting front, evolution of pore water pressure spatial distribution, triggering of multiple failure events, and post-failure large deformation of soils. The simulated results agree well with the field observation during the incident in 1995. Two separate failure events (i.e., one major failure occurred about 20 min after a minor failure) observed during the landslide are properly simulated by the two-phase MPM. The simulation also shows an accumulation of perched water that is consistent with a water table of less than 4 m observed during the incident. In addition, new insights into the failure mechanism are obtained from the simulation. The major failure event observed during the landslide is found to contain two different failure modes, one shallow failure and one deep failure. In the previous study, a weak layer of kaolinite-rich altered tuff was believed as a principal reason for the unusually large volume of sliding soil mass. However, the MPM parametric study in the manuscript shows that the weak layer only has a minor effect on the incident.

Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.

Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.

Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment

The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.