Slope Stability Prediction Research Articles

Multi-sensor couplers and waveguides for efficient detection of acoustic emission behavior of snow

The efficient detection of acoustic emission (AE) activity from snow having porous and fragile character is hindered by the fact that the AE signals have small amplitudes and are typically attenuated within a short distance from the source. We therefore tested seven different types of highly sensitive resonant AE sensors and a multi-channel AE system in a wide frequency range of 1kHz–400kHz to evaluate the performance of multi-sensor coupler and waveguides with varying displacement rates. The AE generated during small fracturing of a natural snowpack, caused by a ram penetrometer, were detected using a cylindrical waveguide and a detection range of up to 16m with detection efficiency of 38dBAE were observed in snow. The AE activities produced by the snowpack were continuously recorded using a 2D-arrestor for AE in relation with the natural melt-freeze process. Prominent AE activity was observed near the phase-transition temperature of snow. Furthermore, spectral analysis of the AE signals generated by snow was carried out using the Short-time Fast Fourier Transform (ST-FFT) method. The AE behavior of snow was observed at different states of stress by varying from low to high, corresponding to different frequency ranges and grain sizes and an empirical relation was established for peak AE rates as a function of displacement rate. Apart from it, an attempt was made to monitor the real time failure of snow sample vis-à-vis AE energy with the help of an AE system and a high-speed camera. Our results are quite encouraging towards application of AE technique in the direction of slope stability evaluation and AE-based non-destructive evaluation of snow under various physical processes. The network of acoustic arrestors and waveguides can be crucial towards prediction of slope stability in view of avalanche release.

Prediction of Seepage and Slope Stability in a Flume Test and an Experimental Field Case

As a participant to round robin test, Middle East Technical University (METU) team performed infiltration and slope stability analyses to simulate a laboratory flume infiltration test and a field experiment, and calculated, for both cases, changes in the suction distribution and in slope stability due to rainfall and evaporation. The overall methodology, the details of the numerical model, calibrations to assess accurate soil properties and estimation procedure for both cases are explained and presented in this study. Use of separate wetting and drying soil hydraulic properties are found to be necessary for proper infiltration simulations. For field cases, it is concluded that evaporation and its duration also plays a critical role and must be considered in numerical analyses.

Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya, India

In order to generate early warning for landslides, it is necessary to address the spatial and temporal aspects of slope failure. The present study deals with the temporal dimension of slope failures taking into account the most widespread and frequent triggering factor, i.e. rainfall, along the National Highway-58 from Rishikesh to Mana in the Garhwal Himalaya, India. Using the post-processed three-hourly rainfall intensity and duration values from the Tropical Rainfall Measuring Mission-based Multi-satellite Precipitation Analysis and the time-tagged landslide records along this route, an intensity–duration (I–D)-based threshold has been derived as I = 58.7D−1.12 for the rainfall-triggered landslides. The validation of the I–D threshold has shown 81.6 % accuracy for landslides which occurred in 2005 and 2006. From this result, it can be inferred that landslides in the study area can be initiated by continuous rainfall of over 12 h with about 4-mm/h intensity. Using the mean annual precipitation, a normalized intensity–duration relation of NI = 0.0612D−1.17 has also been derived. In order to account for the influence of the antecedent rainfall in slope failure initiation, the daily, 3-day cumulative, and 15- and 30-day antecedent rainfall values associated with landslides had been subjected to binary logistic regression using landslide as the dichotomous dependent variable. The logistic regression retained the daily, 3-day cumulative and 30-day antecedent rainfall values as significant predictors influencing slope failure. This model has been validated through receiver operating characteristic curve analysis using a set of samples which had not been used in the model building; an accuracy of 95.1 % has been obtained. Cross-validation of I–D-based thresholding and antecedent rainfall-based probability estimation with slope failure initiation shows 81.9 % conformity between the two in correctly predicting slope stability. Using the I–D-based threshold and the antecedent rainfall-based regression model, early warning can be generated for moderate to high landslide-susceptible areas (which can be delineated using spatial integration of preconditioning factors). Temporal predictions where both the methods converge indicate higher chances of slope failures for areas predisposed to instability due to unfavourable geo-environmental and topographic parameters and qualify for enhanced slope failure warning. This method can be verified for further rainfall seasons and can also be refined progressively with finer resolutions (spatial and temporal) of rainfall intensity and multiple rain gauge stations covering a larger spatial extent.

Support vector classifier analysis of slope

This article examines the potential of support vector machine (SVM) in stability status prediction of slope. Support vector machine achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model rather than the minimizing the error on the training data only. This study uses SVM as a classification tool. In this article, the input data for slope stability prediction consist of values of geotechnical and geometric properties of slope. The accuracy of the SVM is 100% for this problem. The developed SVM gives also an equation for prediction of status of slope. This study shows that SVM has the potential to be a useful and practical tool for prediction of slope stability.

The Saturated - Unsaturated Seepage Analysis of Hualianshu Landslide on the Condition of Rainfall

With the saturated - unsaturated seepage theory, Hualianshu landslide is seepage numerical simulated by the finite element method .The changes of Hualianshu landslide seepage are subject to the impact of rainfall and reservoir water level's changes.The formation and variation of the slope seepage field under rainfall infiltration have been come to, providing a basis for analysis of slope stability and landslide prediction.

Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope

A key issue in assessment of rainfall-induced slope failure is a reliable evaluation of pore water pressure distribution and its variations during rainstorm, which in turn requires accurate estimation of soil hydraulic parameters. In this study, the uncertainties of soil hydraulic parameters and their effects on slope stability prediction are evaluated, within the Bayesian framework, using the field measured temporal pore-water pressure data. The probabilistic back analysis and parameter uncertainty estimation is conducted using the Markov Chain Monte Carlo simulation. A case study of a natural terrain site is presented to illustrate the proposed method. The 95% total uncertainty bounds for the calibration period are relatively narrow, indicating an overall good performance of the infiltration model for the calibration period. The posterior uncertainty bounds of slope safety factors are much narrower than the prior ones, implying that the reduction of uncertainty in soil hydraulic parameters significantly reduces the uncertainty of slope stability.

Soil Physical Properties as Predictors of Soil Strength Indices: Trinidad Case Study

Characterizing soil engineering properties and analyzing their spatial pattern has a key role in managing soils for dif- ferent land uses. A study was conducted to generate two soil engineering properties; shear strength (SS) and friction angle (FA) both related to slope stability from the database of soil agricultural indices. A total of 30 soils were analyzed in two batches of 15 for physicochemical and engineering properties. The first batch was subjected to correlation and regression analysis among properties, whilst the second was used to validate model predictions. Soil friction angle showed strong significant correlations with clay and sand percent. Further stepwise regression resulted in these two properties being the only predictors of peak and residual friction angle. None of the tested properties explained shear strength distribution among the soils. The validated model predicted friction angles for the larger database, which showed non-significant temporal differences from the present dataset used in this study. Spatially distribution of both peak and residual friction angles varied across Trinidad, higher friction angles being associated with higher slopes. Combination of this data with other spatial land attributes would greatly improve land management and slope stability prediction.

Fuzzy Logic System for Slope Stability Prediction

The main goal of this research is to predict the stability of slopes using fuzzy logic system. GeoStudio, a commercially available software was used to compute safety factors for various designs of slope. The general formulation of the software could analyze slope stability using various methods of analysis i.e. Morgenstern-Price, Janbu, Bishop and Ordinary to calculate the safety factors. After analyzing, fuzzy logic was used to predict the slope stability. Fuzzy logic is based on natural language and conceptually easy to understand, flexible, tolerant of imprecise data and able to model nonlinear functions of arbitrary complexity. Several important parameters such as height of slope, unit weight of slope material, angle of slope, coefficient of cohesion and internal angle of friction were used as the input parameters, while the factor of safety was the output parameter. A model to test the stability of the slope was generated from the calculated data. This model presented a relationship between input parameters and stability of the slopes. Results showed that the prediction using fuzzy logic was accurate and close to the target data.

MÔ HÌNH THẤM NƯỚC MƯA PHỤC VỤ PHÂN TÍCH ỔN ĐỊNH SƯỜN DỐC KHU VỰC THỊ TRẤN CỐC PÀI - HUYỆN XÍN MẦN - TỈNH HÀ GIANG

Rainwater infiltration modelling for slope stability analysis in Coc Pai town-Xin Man distict-Ha Giang provinceRainfall plays an important role in formation of landslides by substantially changing the physico-mechanicalproperties of the soil such as soil weight, cohesion, friction angle, pore water pressure etc. For a given soil, thoseparameters are functions of soil moisture. Raiwater infiltration makes the change in soil moisture in space, especialliy in the depth and in time. One specific rainfall event shall give a specific rainwater infiltration and moisture distribution and therfore creates a specific distribution of physico-mechanical properties. Numerical moisture movement under given rainfall event whould give a precise moisture ditribution in soil and therefore can effectively serve the slope stability analysis and prediction. Rainwater infiltration and soil moisture movement modeling for the peak rainfall event in 2008 inCoc Pai town, Xin Man district, Ha Giang province has been carried out, which provides an important input for effectiveslope stability analysis and prediction of the area.

Infiltration and instability in dune erosion

[1] Forecasting dune erosion prior to a storm or over longer periods requires knowledge of the fluid forces on the dune sediments. To improve our predictive capability for this process, we propose a new model in which dune slumping occurs when water, which infiltrates horizontally into the dune, increases the overburden sufficiently to destabilize the dune. Horizontal infiltration is driven by suction of water from swash into the dune via capillary action and is a surprisingly strong process with rapid time scales. Because the elevated pore water concentrations increase the apparent cohesion of the wetted sediments, we also propose that the entire volume of wetted sand slumps as a unit when the dune becomes unstable and erosion can be modeled based on the force balance on a sliding block. Several versions of this model were tested, including a numerical infiltration model, a simplified infiltration equation, and an equation based on offshore wave forcing, rather than known forcing at the dune. The model was tested using data from a large-scale laboratory experiment with a storm hydrograph to investigate the time dependence of dune erosion. Predicting slope stability using a numerical infiltration model with known forcing explained 72% of the observed variance in erosion rate, while a simplified stability and infiltration model explained 58%. Error statistics suggest that we captured the majority of the physics controlling dune erosion in this laboratory experiment and that the simplified model will be useful as a forecasting tool.

Numerical modelling of combined erosion and weathering of slopes in weak rock

ABSTRACTDue to various decay processes associated with weathering, the stability of artificial slopes in weak rocks may be affected well within their envisaged engineering lifetime. Conceptually, the decay following the initial stress release after excavation can be described as a process seeking equilibrium between weathering and erosion. The extent to which such an equilibrium is actually reached influences the outcome of the weathering‐erosion decay process as well as the effects that the decay has on the geotechnical properties of the exposed rock mass, and thus ultimately the stability of slopes affected by erosion and weathering. This paper combines two conceptual models for erosion and weathering, and derives a numerical model which predicts the resulting slope development. This can help to predict the development of a slope profile excavated in a weak rock in time, and can be extended with the addition of strength parameters to the weathering profile to enable prediction of slope stability as a function of time. Copyright © 2011 John Wiley & Sons, Ltd.

Relevance vector machine applied to slope stability analysis

SUMMARYThis paper examines the potential of relevance vector machine (RVM) in slope stability analysis. The nonlinear relationship between slope stability and its influence factors is presented by the relevance vector learning mechanism based on a kernel‐based Bayesian framework. The six input variables used for the RVM for the prediction of stability slope are density (γ), friction angle (C), friction coefficient (ϕ), slope angle (ϕr), slope height (H), and pore water pressure (ru). Comparison of RVM with some other methods is also presented. RVM has been used to compute the error bar. The results presented in this paper clearly highlight that the RVM is a robust tool for the prediction of slope stability. The experimental results show the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.

Root‐soil mechanical interactions during pullout and failure of root bundles

Roots play a major role in reinforcing and stabilizing steep hillslopes. Most studies in slope stability implement root reinforcement as an apparent cohesion by upscaling the behavior of static individual roots. Recent studies, however, have shown that much better predictions of slope stability can be made if the progressive failure of bundles of roots are considered. The characteristics of progressive failure depend on interactions between soil deformation and root bundle geometric and mechanical properties. We present a detailed model for the quantitative description of the mechanical behavior of a bundle of roots under strain‐controlled mechanical forcing. The Root Bundle Model explicitly considers typical values of root‐size spatial distribution (number and dimension of roots), geometric factors (diameter‐length proportion, tortuosity, and branching characteristics), and mechanical characteristics (tensile strength and Young's modulus) and interactions under various soil conditions (soil type, confining pressure, and soil moisture). We provide systematic analyses of the roles of these factors on the mechanical response of the bundle and explore the relative importance of various parameters to the macroscopic root‐soil mechanical response. We distinguish between increased strength imparted by small roots at small deformations and the resilience imparted by larger roots to the growth of large tensile cracks, showing that the maximal reinforcement of fine roots is reached within the first 5 cm of displacement whereas a root of 20 mm diameter may reach its maximal pullout force after 10 cm displacement. The model reproduces the gradual straining and ultimate residual failure behavior of root systems often observed in hillslopes, with progressive growth of tension cracks improving estimations of root reinforcement when considering the effects of root distribution and the variation of the pullout force as a function of displacement. These results enhance understanding of root reinforcement mechanisms and enable more realistic implementation of root reinforcement modeling for stability calculations of vegetated slopes and for guiding ongoing experimental efforts to gather critical root‐soil mechanical information.

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model’s validity.

Parameters to predict slope stability—Soil water and root profiles

A survey was conducted on five slopes of differing slope integrity along the North–South Expressway, Malaysia, to study the effects of vegetation on slope stability. Results showed significant effects of vegetation cover on slope stability in two extreme slopes, type A and type E, which represented stable and failed slopes, respectively. The highest root length density (RLD) was observed in type A slope due to the high density of vegetation cover, which eventually resulted in a lower soil water content (SWC). In contrast, the low biomass density of type E slope possessed a low RLD but high SWC, characteristics of a failed slope. Some correlations were also observed: RLD is positively related to shear strength and SWC is negatively related to both soil shear strength and soil penetrability. Thus, the study suggests that SWC and RLD could be used as indicators of slope stability and has the potential to predict slope failure in the future.

Processes dominating macro-fabric generation in periglacial colluvium

Mass wasting processes in a periglacial environment are considered to result from a combination of frost creep and gelifluction. The relative importance of these processes is often difficult to determine, but appears to be related to factors such as slope angle, soil moisture content, soil texture, and vegetation cover. Further, the importance of any one of these factors can vary from year to year, thereby making long term predictions of slope stability or reconstructions of behaviour history difficult. This study uses two-dimensional macro-fabric analysis as a recorder of the dominant mode of mass-wasting on a slope. Clast trend and plunge data were collected from 68 sites at Eagle Summit, Alaska, and examined in two separate dimensions: the plane of the slope to represent the stress-field driven by gravity; and in a vertical plane trending down the slope to represent the stress-field created by frost heaving pressures. Circular correlation tests indicate the importance of the downslope stress-field on clast position in the plane of the slope. In the vertical plane clast plunge varies according to slope. On gentle slopes, clasts tend to be steeply dipping, on moderate slopes they are parallel to the plane of the slope, and on steep slopes they are imbricated. These observations correspond to modeled experiments of slope deformation under periglacial conditions, and suggest that at the scale of the slope, mass-wasting processes are influenced by varying intensities of frost heave and gravity transfer that are conditioned by slope angle.

A study of slope stability prediction using neural networks

The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory.

Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability

The interactions between factors that affect slope instability are complex, multi-factorial, and often difficult to describe mathematically, imposing a challenge for prediction using traditional methods. The power of the ANN and Grey Systems approaches lies in employing the behaviour of the system rather than knowledge of explicit relations. Published data has been used to illustrate the application of these techniques to predicting the state of slope stability. This has been developed into a tool for analysing and predicting future ground movement based on geotechnical properties and historical behaviour.

Testing the potential of geochemical techniques for identifying hydrological systems within landslides in partly weathered marls

This paper's objective is to determine how useful geochemistry can be in landslide investigations. More specifically, what additional information can be gained by analysing the cation exchange capacity (CEC) and cation composition in respect to the hydrological system of a landslide area in clayey material. Two cores from the Boulc–Mondorès landslide (France) and one core from the Alvera landslide (Italy) were analysed. The NH 4Ac and NaCl laboratory techniques are tested. The geochemical results are compared with the core descriptions and interpreted with respect to their usefulness. Both analysis techniques give identical results for CEC, and are plausible on the basis of the available clay content information. The determination of the exchangeable cations was more difficult, since part of the marls dissolved. With the ammonium-acetate method more of the marls are dissolved than with the sodium-chloride method. The NaCl method is preferred for the determination of the cation fractions at the complex, be it that this method has the disadvantage that the sodium fraction cannot be determined. To overcome this problem, it is recommended to try other displacement fluids. In the Boulc–Mondorès example, the subsurface information that can be extracted from CEC analyses was presented. In the Boulc–Mondorès cores deviant intervals of CEC could be identified. These are interpreted as weathered layers (and preferential flow paths) that may develop or have already developed into slip surfaces. The major problem of the CEC analyses was to explain the origin of the differences found in the core samples. Both Alvera and Boulc–Mondorès examples show transitions in cation composition with depth. It was shown that the exchangeable caution fractions can be useful in locating boundaries between water types, especially the boundary between the superficial, rain-fed hydrological system and the lower, regional groundwater system. This information may be important for landslide interventions since the hydrological system and the origin of the water need to be known in detail. It is also plausible that long-term predictions of slope stability may be improved by knowledge of the hydrogeochemical evolution of clayey landslides. From the analysis, it is concluded that geochemistry is a potentially valuable technique for landslide research, but it is recognized that a lot of work still has to be done before the technique can be applied in engineering practice.

Probabilistic Slope Stability Analysis with Stochastic Soil Hydraulic Conductivity

The effects of stochastic hydraulic conductivity on the slope stability of an embankment dam are investigated using a combination of random field simulation, seepage analysis, and slope stability analysis. The hydraulic conductivity distribution is treated as a spatially stationary random field following a lognormal distri- bution. The turning band method is used to generate the spatial variability of the saturated hydraulic conductivity Ks in the domain. Different standard deviations of log hydraulic conductivity are investigated. For each sln Ks value of various realizations of hydraulic conductivity were generated and combined with a numerical s , ln Ks model to simulate water flow in an earth dam with variable Ks. The first-order second-moment reliability index b was employed to characterize the influence of the variability of Ks, and hence, pore-water pressures, on the stability of the downstream slope. A linear relationship between and the standard deviation of the factor sln Ks of safety sF was obtained from the simulation results. A relationship between b and , in which every 0.1 sln Ks increment of results in a decrease of 1.0 in b, is deduced based on the simulation results. Results of a sln Ks Shapiro-Wilk test for goodness of fit indicate that the factor of safety can be assumed to be normally or lognormally distributed when the saturated hydraulic conductivity follows a lognormal distribution and is sln Ks small (#0.5). When is large (>0.5), neither normal nor lognormal distributions provide a reasonable s ln Ks approximation of the factor of safety. Simulation results show that neither standard deviation nor coefficient of variation of the factor of safety is constant when only the variability of hydraulic conductivity is considered. While the results presented are directly applicable only to the particular earth dam geometry and boundary conditions studied, the methodology is general and may be extended to embankments with different boundary conditions. INTRODUCTION AND BACKGROUND Natural soils are highly variable and heterogeneous (Lamb 1966; Peck 1967; Vanmarcke 1977a; DeGroot and Baecher 1993). In practice, methods used for the analysis of slope sta- bility typically are deterministic, with soil properties charac- terized as constants for a given soil layer and each specified layer assumed to be homogeneous. The variability of soil properties presumably is taken into account indirectly for de- sign purposes by selecting a factor of safety, which is chosen based on experience and/or some design criteria (Griffiths and Fenton 1993; Duncan 1996). Many authors have pointed out the limitations of deterministic methods because they do not account explicitly for variability and uncertainty related to soil parameters (Vanmarcke 1977b; Oka and Wu 1990; Soulie et al. 1990; Kulhawy et al. 1991). Methods are needed to quan- tify uncertainties associated with soil properties and to include them in slope stability analysis (Anderson et al. 1982; Whit- tlestone et al. 1995). By incorporating uncertainties quantita- tively, reliability analysis can enhance engineering judgment and facilitate improved decision making (Li and Lumb 1987). Many water-retaining structures in North America are em- bankment dams (Fenton and Griffiths 1996) and the prediction of dam slope stability is of great importance. Hydraulic con- ductivity of the embankment soils is used to calculate all of the hydraulic parameters associated with dam performance, in- cluding seepage rates and amounts, pore-water pressures, and therefore, the state of effective stress. It follows that uncer- tainty associated with the spatial variability of hydraulic con- ductivity leads to uncertainty in estimating pore-water pres-