Infinite Slope Stability Model Research Articles

확률론적 해석기법을 이용한 보은지역의 사면재해 안정성분석

본 연구는 사면재해 취약성 평가를 위해 사면모델 중 하나인 무한사면 해석모델을 적용하였다. 그러나 무한사면 해석모델은 광역적인 연구지역에 적용하는데 있어서 데이터획득 및 처리과정에 어려움이 있고 데이터 획득과정에서 불가피하게 불확실성이 개입되는 문제가 있다. 이러한 불확실성을 최소화하기 위해 확률론적 해석기법인 몬테카를로 시뮬레이션을 적용하였으며, 광역적인 연구지역에 무한사면 해석모델을 적용하기 위하여 GIS를 활용한 무한사면 안정해석법으로 파괴확률을 획득하였다. 연구지역으로는 사면재해가 집중적으로 발생한 보은지역을 선정하였고 사면의 기하학적인 특성과 점착력 및 내부마찰각 등의 강도정수를 획득하였다. 또한 불확실성의 효과를 평가하기 위해 강도정수의 변동계수를 10%에서 30%로 고려하였고 이러한 과정을 통하여 확률론적 해석기법은 자료의 불확실성을 감쇠시킬 수 있다는 결과를 도출하였다. In this study the infinite slope model, one of the physical landslide models has been suggested to evaluate the susceptibility of the landslide. However, applying the infinite slope model in regional study area can be difficult or impossible because of the difficulties in obtaining and processing of large spatial data sets. With limited site investigation data, uncertainties were inevitably involved with. Therefore, the probabilistic analysis method such as Monte Carlo simulation and the GIS based infinite slope stability model have been used to evaluate the probability of failure. The proposed approach has been applied to practical example. The study area in Boeun area been selected since the area has been experienced tremendous amount of landslide occurrence. The geometric characteristics of the slope and the mechanical properties of soils like to friction angle and cohesion were obtained. In addition, coefficient of variation (COV) values in the uncertain parameters were varied from 10% to 30% in order to evaluate the effect of the uncertainty. The analysis results showed that the probabilistic analysis method can reduce the effect of uncertainty involved in input parameters.

Rainfall infiltration: infinite slope model for landslides triggering by rainstorm

Shallow slope failure due to heavy rainfall during rainstorm and typhoon is common in mountain areas. Among the models used for analyzing the slope stability, the rainwater infiltration model integrated with slope stability model can be an effective way to evaluate the stability of slopes during rainstorm. This paper will propose an integrated Green-Ampt infiltration model and infinite slope stability model for the analysis of shallow type slope failure. To verify the suitability of the proposed model, seven landslide cases occurred in Italy and Hong Kong are adopted in this paper. The results indicate that the proposed model can be used to distinguish failed and not-yet failed slopes. In addition, the proposed model can be used as the first approximation for estimating the occurrence time of a rainfall-induced shallow landslide and its depth of sliding.

Impacts of Unsaturated Zone Soil Moisture and Groundwater Table on Slope Instability

The combined effect of soil moisture in unsaturated soil layers and pore-water pressure in saturated soil layers is critical to predict landslides. An improved infinite slope stability model, that directly includes unsaturated zone soil moisture and groundwater, is derived and used to analyze the factor of safety’s sensitivity to unsaturated zone soil moisture. This sensitivity, the change in the factor of safety with respect to variable unsaturated zone soil moisture, was studied at local and regional scales using an active landslide region as a case study. Factors of safety have the greatest sensitivity to unsaturated zone soil moisture dynamics for shallow soil layers (<2 m) and comparatively deep groundwater tables (1 m). For an identical groundwater table, the factor of safety for a 1 m thick soil mantle was four times more sensitive to soil moisture changes than a 3-m thick soil. At a regional scale, the number of unstable areas increases nonlinearly with increasing unsaturated zone soil moisture and with moderately wet slopes exhibiting the greatest sensitivity.

Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km 2 was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region. The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.

DEM-based deterministic landslide hazard analysis in the Lesser Himalaya of Nepal

In Nepal, people live in widely spread settlements in the fragile Himalayan terrains, and suffer more from landslides than from any other type of natural disaster. The small-scale rainfall-triggered landslides in the Lesser Himalaya of Nepal are generally shallow (about 0.5 to 2.5 m) and are triggered by changes in the physical property of soil layers during rainfall. The relation between landslides and slope hydrology has received little attention in Himalayan landslide research. Thus, this paper deals with the probability of slope failure during extreme rainfall events by considering a digital elevation model (DEM)-based hydrological model for soil saturation depth and an infinite slope stability model. Deterministic distributed analysis in a geographic information system (GIS) was carried out to calculate the probability of slope failure. A simple method of error propagation was used to calculate the variance of the safety factors and the probability of failure. When normally distributed failure probability values were checked against existing landslides, it was found that more than 50% of the pixels of existing landslides coincided with a high calculated probability of failure. Although the deterministic distributed analysis has certain drawbacks, as described by previous researchers, this study concluded that the calculated failure probability can be utilised to predict the probability of slope failure in Himalayan terrain during extreme rainfall events.

Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

Modelling landslide hazard, soil redistribution and sediment yield of landslides on the Ugandan footslopes of Mount Elgon

In this study, the LAPSUS-LS landslide model, together with a digital terrain analysis of topographic attributes, is used as a spatially explicit tool to simulate recent shallow landslides in Manjiya County on the Ugandan slopes of Mount Elgon. Manjiya County is a densely populated mountainous area where landslides have been reported since the beginning of the twentieth century. To better understand the causal factors of landsliding, 81 recent landslides have been mapped and investigated. Through statistical analysis it was shown that steep concave slopes, high rainfall, soil properties and layering as well as human interference were the main factors responsible for landslides in the study area. LAPSUS-LS is used to construct a landslide hazard map, and to confirm or reject the main factors for landsliding in the area. The model is specifically designed for the analysis of shallow landslide hazard by combining a steady state hydrologic model with a deterministic infinite slope stability model. In addition, soil redistribution algorithms can be applied, whereby erosion and sedimentation by landsliding can be visualized and quantified by applying a threshold critical rainfall scenario. The model is tested in the Manjiya study area for its ability to delineate zones that are prone to shallow landsliding in general and to group the recent landslides into a specific landslide hazard category. The digital terrain analysis confirms most of the causal topographic factors for shallow landsliding in the study area. In general, shallow landslides occur at a relatively large distance from the water divide, on the transition between steep concave and more gentle convex slope positions, which points to concentration of (sub)surface flow as the main hydrological triggering mechanism. In addition, LAPSUS-LS is capable to group the recent shallow landslides in a specific landslide hazard class (critical rainfall values of 0.03–0.05 m day − 1 ). By constructing a landslide hazard map and simulating future landslide scenarios with the model, slopes in Manjiya County can be identified as inherently unstable and volumes of soil redistribution can yield four times higher than currently observed. More than half of this quantity can end up in the stream network, possibly damming rivers and causing major damage to infrastructure or siltation and pollution of streams. The combination of a high population density, land shortage and a high vulnerability to landslides will likely continue to create a major sustainability problem.

Root tensile strength relationships and their slope stability implications of three shrub species in the Northern Apennines (Italy)

The role of root strength is important in stabilising steep hillslopes which are seasonally affected by storm-induced shallow landslides. In the Italian Apennines, steep (25–40°) slopes underlain by mudstone are generally stable if they are covered by shrubs whose roots anchor into the soil mantle. To quantify the mechanical reinforcement of roots to soil, the root tensile breaking force and the root tensile strength of three autochthonous shrub species commonly growing on stiff clay soils of the Northern Italian Apennines, Rosa canina (L.), Inula viscosa (L.) and Spartium junceum (L.), were measured by means of field and laboratory tests. For each test approximately 150 root specimens were used. The tensile force increases with increasing root diameter following a second-order polynomial regression curve. The tensile strength decreases with increasing root diameter following a power law curve. The field in situ tensile force required to break a root is always smaller than that obtained from laboratory tests for the same root diameter, although their difference becomes negligible if the root diameter is smaller than 5 mm. The influence of root tensile strength on soil shear strength was verified based on the infinite slope stability model. The root reinforcement was calculated using the number and mean diameter of roots. The factor of safety was calculated for three different soil thickness values (0.1, 0.3, and 0.6 m) and topographic slopes between 10° and 45°. The factor of safety for the combination of 0.6 m soil thickness, slopes smaller than 30°, and vegetation of I. viscosa (L.) or S. junceum (L.) is always larger than 1. If a slope is steeper, the factor of safety may be smaller than 1 for I. viscosa (L.), although it is still larger than 1 for S. junceum (L.). In the stiff clayey areas of the Northern Italian Apennines, I. viscosa (L.) mainly colonizes fan/cone/taluses and stabilises these zones up to a topographic gradient < 30° for a soil 0.6 m thick. S. junceum (L.) colonizes not only fan/cone/taluses but also headwalls and cliffs and, for a 0.6 m thick soil, it stabilises these areas up to 45°. The effectiveness of this reinforcement, however, depends strongly on the frequency of soil and seasonal grass vegetation removal due to shallow landsliding before the entrance of the shrub species.

A comparative analysis of terrain stability models for predicting shallow landslides in colluvial soils

Most of the slopes of the hilly areas of the Apennines are composed of colluvial soils originating from the weathering of the bedrock and down slope transportation. Shallow slides affect this superficial cover, depend largely on the surface topography and are a recurrent problem. SINMAP and SHALSTAB are terrain stability models that combine steady state hydrology assumptions with the infinite slope stability model to quantify shallow slope stability. They have a similar physical basis but they use different indices to quantify instability. The purposes of this study are to test and compare the approaches of SINMAP and SHALSTAB models for slope stability analysis and to compare the results of these analyses with the locations of the shallow landslides that occurred on November 2002 in an area of the Oltrepo Pavese (Northern Apennines). The territory of S. Giuletta, characterized by clayey–silty colluvial soils, represents the test site. The Digital Elevation Model was constructed from a 1:5000 scale contour map and was used to estimate the slope of the terrain as well as the potential soil moisture conditions. In situ and laboratory tests provided the basis for measuring values for soil hydraulic and geotechnical parameters (moisture content, soil suction, Atterberg limits, methylene blue dye adsorption, hydraulic conductivity). Soil thickness was extracted from a soil database. An inventory of landslide from interpretation of aerial photographs and field surveys was used to document sites of instability (mostly soil slips) and to provide a test of model performance by comparing observed landslide locations with model predictions. The study discusses the practical advantages and limitations of the two models in connection with the geological characteristics of the studied area, which could be representative of similar geological contexts in the Apennines.

Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China

The Xiaojiang watershed in Southwest China has high landslide hazard and has been given the title of “the Museum of Geohazards in China”. However, the available information on landslides in the Xiaojiang watershed is still limited. We constructed the essential spatial database of landslides using the GIS techniques. The quantitative relationships between landslides and factors affecting landslides are established by the Certainty Factor model (CF). The affecting factors such as lithology, structure, slope angle, slope aspect, elevation and off-fault distance are recognized. By applying CF value integration and landslide zonation, the most significant affecting factors are selected. The widespread landslide activities in the Xiaojiang watershed are caused and triggered by heavy rain. A promising approach to modeling the spatial distribution of rainfall-triggered landslide is combining the mechanistic infinite slope stability model with a hydrological model. A model modified from Stability INdex Mapping (SINMAP) is used to prepare the landslide susceptibility maps for different rainfall conditions. Information on these maps could be useful for explaining the known existing landslide, making emergency decisions and relieving the efforts on the avoidance and mitigation of future landslide hazards.

Blue clay slope angle in relation to some geological and geotechnical characteristics, Italy.

Observation of blue clay slopes outcropping along the Italian peninsula (Apennine foredeep and other inland areas) promptedinvestigation into several factors whichgovern their actual slope angle. Though characterized by good gradient, slope angle may differ from slope to slope, as well as within certain limited areas. The study attempts to connect structural order (dip strata) and shear strength of the soil to explain slope angle. In many cases it is possible to distinguish (i) slope angle controlled by substratum and, thus, characterized by shear strength higher than the residual condition,and (ii) slope angle controlled by colluvium cover,characterized by shear strength reduced to residual. Following an infinite slope stability model approach, the above conditions are connected to the half residual friction angle value, ϕ R ′/2, which splits the slopes intotwo main types: cover-controlled, characterized by β≤ϕ R ′/2 and completely or in part substratum-controlled, characterized by β&gt;ϕ R ′/2. Slope angle distribution, deduced from slope map,can help us to understand the relative role of footslope removal processes and to distinguish slope type, substratum-controlled or colluvium-controlled. This study cannot embrace all the factors and processes leading to a blue clay slope angle, and local geomorphological processes may complicate the outline proposed, though it does provide a useful first approach.

Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach

Spatial probabilistic modeling of slope failure using a combined Geographic Information System (GIS), infinite-slope stability model and Monte Carlo simulation approach is proposed and applied in the landslide-prone area of Sasebo city, southern Japan. A digital elevation model (DEM) for the study area has been created at a scale of 1/2500. Calculated results of slope angle and slope aspect derived from the DEM are discussed. Through the spatial interpolation of the identified stream network, the thickness distribution of the colluvium above Tertiary strata is determined with precision. Finally, by integrating an infinite-slope stability model and Monte Carlo simulation with GIS, and applying spatial processing, a slope failure probability distribution map is obtained for the case of both low and high water levels.

Modeling Slope Stability in Honduras

Hurricane Mitch (1998) was the deadliest storm to strike the western hemisphere in over 200 yr, and landslides were a significant component of the storm impact, inflicting direct damage on steep lands and triggering mudslides that devastated valleys below. The stability index mapping (SINMAP) model was applied to a 46.1‐km2 agricultural area in Central Honduras to predict the spatial distribution of shallow debris slides based on the infinite slope stability model and a steady‐state hydrology module. The region was surveyed for geology and depth of bedrock, and 63 soil samples were collected on which bulk density, saturated shear strength, angle of soil friction, and saturated hydraulic conductivity were measured. A digital elevation model (DEM) was developed from digitized elevation contours. The natural variability of soil properties was accounted for in a set of model simulations based on parameter distributions, and results were presented in a distributed probability map (DPM) for slope failure that, in turn, was used to identify the spatial scale of variability captured by the model. Ripley's K‐function for distribution of a spatial point process was applied to mapped landslides and to simulated data based on the DPM. Observed landslide locations were more tightly clustered than those predicted by the physically based model, indicating that fine‐scale physical phenomena not captured by the model likely play a role in slope failure. Aggregation of observed landslides on a scale of 150 m made the comparison with model predictions more consistent. Both parameter variability and inventory scale are important considerations in the evaluation of a slope stability model.

Assessment of shallow landsliding by using a physically based model of hillslope stability

AbstractA model for the simulation of shallow landsliding triggered by heavy rainstorms is analysed and discussed. The model is applied in two mountainous catchments in the Dolomites (Eastern Italian Alps): the Cordon catchment (5 km2) and the Vauz catchment (1·9 km2), where field surveys provided a description of hydraulic and geotechnical properties of soils and an inventory of landslide scars is available. The stability mapping procedure, which is similar to that proposed by Montgomery and Dietrich (1994 Water Resources Research 30: 1153), combines steady‐state hydrologic concepts with the infinite slope stability model. The model provides an estimate of the spatial distribution of the critical rainfall, which is the minimum steady‐state rainfall predicted to cause instability. The comparison of the landslides observed in the study basins with model predictions shows that the distribution of critical rainfall obtained from the model provides a surrogate for failure initiation probability as a function of topographic location. Copyright © 2002 John Wiley &amp; Sons, Ltd.

Modelling uncertainty in natural resource analysis using fuzzy sets and Monte Carlo simulation: slope stability prediction

The techniques of fuzzy logic and Monte Carlo simulation are combined to address two incompatible types of uncertainty present in most natural resource data: thematic classification uncertainty and variance in unclassified continuously distributed data. The resultant model of uncertainty is applied to an infinite slope stability model using data from Louise Island, British Columbia. Results are summarized so as to answer forestry decision support queries. The proposed model of uncertainty in resource data analysis is found to have utility in combining different types of uncertainty, and efficiently utilizing available metadata. Integration of uncertainty data models with visualization tools is considered a necessary prerequisite to effective implementation in decision support systems.

A process‐based model for colluvial soil depth and shallow landsliding using digital elevation data

AbstractA model is proposed for predicting the spatial variation in colluvial soil depth, the results of which are used in a separate model to examine the effects of root strength and vertically varying saturated conductivity on slope stability. The soil depth model solves for the mass balance between soil production from underlying bedrock and the divergence of diffusive soil transport. This model is applied using high‐resolution digital elevation data of a well‐studied site in northern California and the evolving soil depth is solved using a finite difference model under varying initial conditions. The field data support an exponential decline of soil production with increasing soil depth and a diffusivity of about 50 cm2/yr. The predicted pattern of thick and thin colluvium corresponds well with field observations. Soil thickness on ridges rapidly obtain an equilibrium depth, which suggests that detailed field observations relating soil depth to local topographic curvature could further test this model. Bedrock emerges where the curvature causes divergent transport to exceed the soil production rate, hence the spatial pattern of bedrock outcrops places constraints on the production law.The infinite slope stability model uses the predicted soil depth to estimate the effects of root cohesion and vertically varying saturated conductivity. Low cohesion soils overlying low conductivity bedrock are shown to be least stable. The model may be most useful in analyses of slope instability associated with vegetation changes from either land use or climate change, although practical applications may be limited by the need to assign values to several spatially varying parameters. Although both the soil depth and slope stability models offer local mechanistic predictions that can be applied to large areas, representation of the finest scale valleys in the digital terrain model significantly influences local model predictions. This argues for preserving fine‐scale topographic detail and using relatively fine grid sizes even in analyses of large catchments.

A theoretical model of the effects of timber harvesting on slope stability

An infinite slope stability model is proposed which incorporates changes in root cohesion and vegetation surcharge through several timber management cycles along with the stochastic influence of rainfall on pore water pressure. Recovery of rooting strength and tree surcharge following timber harvest are simulated by a sigmoid relationship, while root deterioration of harvested vegetation is described by an exponential decay function. The effects of long‐term timber management on probability of failure are simulated by overlaying the impacts of a prior vegetation removal on a more recent removal. For each year the critical pore water pressure (ucrit) needed to trigger slope failure is computed. An empirical function relating piezometric level to antecedent rainfall, storm intensity, and total precipitation is presented to assess the probability of occurrence of ucrit, based on historical rainfall records for a site in coastal Alaska. Simulations of probability of failure indicate that alternate thinnings and clear‐cuts and clear‐cuts alone produce less stable conditions than shelterwood harvesting systems and partial cuts. Repeated harvesting cycles with progressively shorter rotations, reduced regeneration potential of new vegetation, and destruction of understory vegetation during logging or site preparation can all increase the probability of failure. These analyses can provide land managers with options for acceptable vegetation management strategies on potentially unstable hillslopes.

Sedimentology, geotechnical properties and stability of the surficial sediments on the continental slope, southwestern Flemish pass

Abstract This study describes the sedimentology and geotechnical properties of the surficial sediments on the continental slope, southwestern Flemish Pass. The continental slope has a gradient ranging from 0.06° to 2.3°. The sediments are of late Wisconsinan to Holocene in age and are characterized by marked subsurface grain‐size textural changes. These textural changes reflect differing sedimentation styles, e.g., terrigenous slope sedimentation, catastrophic debris flows, turbidities, and ice‐rafted detritus. Geotechnical analysis include microscopy, X‐ray analysis, water and carbonate contents, index properties, clay mineral analysis, undrained shear strength, and consolidation tests. The stability of the slope sediments is examined in terms of the infinite slope stability model using the above mentioned geotechnical data and the sea floor gradient. The traditional model has been improved to take into consideration the effective pressure and the liquefaction potential of the sediments under earthquake loading. With an average maximum slope gradient of 2.3° on the flanks of the Pass, the deeper, lower normally consolidated sediments of the soil profile will remain stable under a seismic horizontal acceleration of up to 0.20 g. The stability of the upper sediments needs further investigation and more refined testing.