Academic literature on the topic 'Shallow landslides, susceptibility, numerical modelling'

The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence.

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

Abstract. Mountainous grassland slopes can be severely affected by soil erosion, among which shallow landslides are a crucial process, indicating instability of slopes. We determine the locations of shallow landslides across different sites to better understand regional differences and to identify their triggering causal factors. Ten sites across Switzerland located in the Alps (eight sites), in foothill regions (one site) and the Jura Mountains (one site) were selected for statistical evaluations. For the shallow-landslide inventory, we used aerial images (0.25 m) with a deep learning approach (U-Net) to map the locations of eroded sites. We used logistic regression with a group lasso variable selection method to identify important explanatory variables for predicting the mapped shallow landslides. The set of variables consists of traditional susceptibility modelling factors and climate-related factors to represent local as well as cross-regional conditions. This set of explanatory variables (predictors) are used to develop individual-site models (local evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow-landslide points simultaneously. While the local conditions of the 10 sites lead to different variable selections, consistently slope and aspect were selected as the essential explanatory variables of shallow-landslide susceptibility. Accuracy scores range between 70.2 % and 79.8 % for individual site models. The all-in-one model confirms these findings by selecting slope, aspect and roughness as the most important explanatory variables (accuracy = 72.3 %). Our findings suggest that traditional susceptibility variables describing geomorphological and geological conditions yield satisfactory results for all tested regions. However, for two sites with lower model accuracy, important processes may be under-represented with the available explanatory variables. The regression models for sites with an east–west-oriented valley axis performed slightly better than models for north–south-oriented valleys, which may be due to the influence of exposition-related processes. Additionally, model performance is higher for alpine sites, suggesting that core explanatory variables are understood for these areas.

AbstractSubmarine landslides are common at glaciated continental margins. The onset of large-scale landslides coincides with the initiation of Northern Hemisphere glaciations in the Quaternary. This implies that processes related to glacial cycling provide favourable conditions for submarine landslides at high-latitude margins. Potential processes include glacial deposition patterns and enhanced seismicity. It is also possible that advances and retreats of ice sheets, a highly dynamic process in geological terms, makes slopes discernible to failure by modifying the stress regime. Here, we quantify this effect using 2D finite element modelling of a glaciated continental margin. Different model runs investigate the pore-pressure development in homogeneous, as well as layered, slopes during glaciation when loaded by an ice stream with one or more ice advances. Ice streams cause significant variations in excess pore pressure in the very shallow sediment sequences at the continental shelf. However, lateral fluid flow is not efficient enough to increase pore pressures significantly at the slope, where large-scale submarine slides are observed. Hence, while ice-sheet dynamics appear to favour the occurrence of shallow slides close to the shelf edge, ice sheets seem to be irrelevant for the generation of large-scale submarine landslides at the continental slope.

Abstract. Topographic index-based hydrological models have gained wide use to describe the hydrological control on the triggering of rainfall-induced shallow landslides at the catchment scale. A common assumption in these models is that a spatially continuous water table occurs simultaneously across the catchment. However, during a rainfall event isolated patches of subsurface saturation form above an impeding layer and their hydrological connectivity is a necessary condition for lateral flow initiation at a point on the hillslope. Here, a new hydrological model is presented, which allows us to account for the concept of hydrological connectivity while keeping the simplicity of the topographic index approach. A dynamic topographic index is used to describe the transient lateral flow that is established at a hillslope element when the rainfall amount exceeds a threshold value allowing for (a) development of a perched water table above an impeding layer, and (b) hydrological connectivity between the hillslope element and its own upslope contributing area. A spatially variable soil depth is the main control of hydrological connectivity in the model. The hydrological model is coupled with the infinite slope stability model and with a scaling model for the rainfall frequency–duration relationship to determine the return period of the critical rainfall needed to cause instability on three catchments located in the Italian Alps, where a survey of soil depth spatial distribution is available. The model is compared with a quasi-dynamic model in which the dynamic nature of the hydrological connectivity is neglected. The results show a better performance of the new model in predicting observed shallow landslides, implying that soil depth spatial variability and connectivity bear a significant control on shallow landsliding.

Abstract. GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GIS-based, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C- and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (Pf) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a digital elevation model (DEM), ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign Pf to each ellipsoid. The model calculates for each pixel multiple values of FoS and Pf corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of Pf for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing makes it possible to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of Pf and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km2 Collazzone area in Umbria, central Italy.

Abstract. GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GIS-based, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C- and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (Pf) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a DEM, ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign Pf to each ellipsoid. The model calculates for each pixel multiple values of FoS and Pf corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of Pf for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing allows to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of Pf and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km2 Collazzone area in Umbria, central Italy.

The terrestrial laser scanner enables site remote sensing of the slopes in a simple and automated manner. Regular measurements with multiple scanner positioning might be applied in long term landslide monitoring. A detailed geological structural model allows for hazard assessment, and then for a slope stability assessment. Numerical model, along with the subsoil parameters, introduced into the Finite Element Method (FEM) software enables an estimation of landslide susceptibility and the possible displacements of the terrain in longer period of time, as well as, due to different loading cases. In this paper the formation of landslides and stability of the slope after the retaining structure establishment is analysed. Two analysed landslides have different origin and may be a threat even after stabilization works already maintained.

Due to high velocity, high frequency and the lack of warning signs, shallow landslides represent a major hazardous factor in mountain regions. Moreover, increasing urbanisation and climate changes triggering intense rainfall events make shallow landslides a source of widespread risk. The interest of the scientific community in this process has grown in the last three decades with the aim to perform robust shallow landslide hazard assessment at regional scale. Generally, these slope failures involve relatively small volumes of material sliding along with a planar shallow rupture surface. In the literature it is widely accepted that shallow landslides involve only slope deposit (or colluvium) and the sliding surface correspond to the discontinuity between bedrock and the overlying loose material. The fieldwork conducted in this thesis highlighted that often shallow landslides involve also the weathered and fractured portion of bedrock. In this framework, the implementation of shallow landslides susceptibility modelling should take into account the engineering geological properties of slope deposits, as well as of the underlying bedrock. In this thesis a fieldwork-based method is proposed to acquire, process and spatialize engineering geological properties of slope deposits and bedrock. The aims of this thesis were to compile a new multi-temporal shallow landslide inventory, characterize the engineering geological properties of slope deposits and bedrock, implement and compare shallow landslide susceptibility modelling by means a physically-based and a data-driven methods and explore the role of bedrock in shallow slope failures. The study area corresponds to a 242 km2 portion of the Garfagnana basin (Northern Apennines), a mountainous region where the elevation ranges between 150 and 2000 m a.s.l. characterized by an incised and rugged morphology with steep slopes (average 28° degrees) and a mean annual rainfall between 1500 and 2500 mm/year. From a geological point of view, the Garfagnana basin is a narrow intra-mountainous valley, interposed betweeen the Alpi Apuane metamorphic complex to the east and the sedimentary northern Apennine’s ridge to the west. The fieldwork and laboratory tasks carried out to map engineering geology characters of slope deposits consisted on a set of hundreds of field sampling points, with the acquisition of depth to the bedrock, geotechnical horizons, unit weight, as well as soil samples for lab analysis. The distribution of points was chosen by observing that engineering geology properties of slope deposits depend on both bedrock lithology and morphometric conditions. In order to obtain the map distribution of engineering geology parameters, we implemented a spatial analysis by clustering morphometric variables stratified as a function of bedrock lithological units. In order to investigate the engineering geology characteristics of the bedrock, a field survey aimed to classify rock masses was conducted. For each survey site, 200-400 Schmidt hammer rebound measures, bedding and joint data, GSI (Geological Strenght Index) and samples for laboratory analyses (unit weight and slake durability test) were collected. The field data were processed and spatially analyzed by means uni-variate and multi-variate cluster analysis in order to delineate domains with different bedrock geo-mechanical properties. The shallow landslide susceptibility analysis was performed using both data-driven, Information Value, and physically-based, a modified version of SHALSTAB model (PROBSS), methods. The numerical modelling faced three issues: a) the comparison of PROBSS and Information value (IV) in the prediction of shallow landslides involving SD; b) the training and cross-validation of IV models using shallow landslides involving bedrock or not; c) implementation of a physically-based model to predict involving bedrock shallow landslides. First of all, the results highlight that the field-based methods proposed here to evaluate engineering geological properties of slope deposits and bedrock are adequate for the implementation of regionalised physically-based susceptibility models. The comparison between PROBSS and IV highlights that the simplification of shallow landslides adopted by the infinite slope model which do not take into account the occurrence of a sliding surface located below the slope deposits / bedrock discontinuity, may affect the performance of physically-based susceptibility models. The accuracy of IV model is slightly better that PROBSS model. Having implemented two data-driven susceptibility models using two different training datasets highlighted the different characteristics that slope deposits and bedrock involving shallow landslides have, suggesting and demonstrating that the latter occur in conditions that the physically based model cannot predict. By placing the slip surface below the discontinuity between slope deposits and bedrock and providing shear strength parameters compatible with a weathered and fractured rock material, satisfactory accuracy result was obtained with PROBSS model.

The emphasis of this dissertation is on using numerical and plasticity based methods to study two main areas of offshore geotechnics. The first part of this dissertation focuses on the undrained behavior of deeply embedded anchor plates under combined shear and torsion. Plate anchors are increasingly being used instead of typical foundation systems to anchor offshore floating platforms to sustain uplift operating forces. However extreme loading cases would create general loading conditions involving six degrees of freedom. The focus of my research was to evaluate the bearing capacity of plate anchors under two-way horizontal and torsional loading and to study the decreasing effect of torsional moment on the horizontal bearing capacity of these foundations. The study takes advantage of several approaches: Numerical simulation (two and three dimensional finite element analysis) Evaluating and modification of the available plasticity solutions Developing equations for three degree-of-freedom yield locus surfaces The same methodology is applied to evaluate the response of shallow foundations for subsea infrastructure subjected to significant eccentric horizontal loads. The second part of this study focuses on offshore geohazards. Coastal communities and the offshore industry can be impacted directly by geohazards, such as submarine slope failures, or by tsunamis generated by the failed mass movements. This study aims at evaluating the triggering mechanisms of submarine landslide under cyclic wave and earthquake loading. A simple effective stress elasto-plastic model with a minimal number of parameters accounting for monotonic and cyclic response of fine-grained material is developed. The new constitutive soil model could be used to simulate case histories and conduct parametric study to evaluate the effect of slope inclination angle, the earthquake loading with different PGA, frequency content, and duration, as well as various deposition rates to simulate different over pressure levels. This study will generate more insight on the static and cyclic behavior of submarine slopes and influencing factors on their triggering mechanisms using more comprehensive and realistic modeling tools. Several objectives are defined: Developing an appropriate constitutive formulation, Evaluating the constitutive model and material parameters for available databases.