Estimation Of Slope Stability Research Articles

Estimation of road cut slope stability along the national and state highway of Garhwal Himalaya (India) applying slope mass rating and kinematic analysis

Slope stability assessment of different road cut slopes has been conducted along the National Highway in Garhwal Himalaya, Uttarakhand (NH-34, NH-134, NH-7, NH-107 and a state highway from Dharasu to Tilwara). Slope instability issues in the Himalayan terrain are radically increasing due to high anthropogenic activities and rapid growth of urban centers since past years. Road networks in mountainous areas are the main source of transportation and socio-economic development. In this concerned study, forty (40) road cut slopes have been selected. These sites are selected based on lithology and structure. Diverse slope stability quantification methods along with various modes of failures have been systematically analyzed. The entire region falls under the tectonically active belt or the vicinity of the active fault zones that make the slopes inherently unstable. The Geological Strength Index (GSI) and Rock Mass Rating (RMR) have been applied for determination of slope conditions. Slope mass stability has been evaluated using Slope Mass Rating (SMR) index. The RMR rating ranges from 26 to 87 and the SMR values of forty (40) roads cut slopes range from 10 to 91. The kinematic analysis shows that most of the probable slopes failures are susceptible under planar and wedge failure. This study will definitely help to reduce the loss of life and property and also helps the ongoing constructional processes along the road section.

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098.

Evaluation of buoyant action of groundwater and surface water interaction on pit slope stability using hydrogeological-stratigraphic-structural assessment within the Kawere catchment of the Nsuta mine

ABSTRACT In open-pit mining operations where activities are conducted below water table or close to surface water bodies, the mine is potentially faced with dewatering and slope stability challenges. Appropriate evaluation of slope stability to obtain adequate slope safety factor has continuously been the centre of geotechnical research locally and internationally. This paper evaluates the influence of buoyant action propagated by groundwater and surface (GW-SW) interaction on pit slope designed in multi-layered fractured rock with complex hydrology using Hydrogeological-Stratigraphic-Structural Assessment (HSSA) at the Nsuta Manganese Mine in Ghana). LEM analyses of the Pit C-western slope estimated an overall factor of safety (FOS) of 1.1, probabilities of failure (POF) of 7.5% and the reliability index (RI) of 1.4 in the slope stress field. However, when the groundwater was introduced, the slope FOS was reduced to 0.13, POF of 100.0% and (RI) of 0.00 indicating failure. FEM numerical model points to the location of maximum shear strain and total displacement in the argillite zone within the rock mass. The FEM seepage analysis model estimated groundwater pore pressure data along the critical slide surfaces from the crest of the slope to the bottom of the rock slope ranging between 250 kPa and 4250 kPa with an average pore pressure of 2250 kPa within the pit slope. This proposed HSSA approach used in the study provides innovative insights into the percentage failure contribution of the individual lithologies to the overall combined lithological susceptibility of the slope under stress and groundwater failure mechanisms, pointing out Ru ratio in pit slope stability estimation as inadequate.

Parametric Study on the Stability of Slopes Subjected to Earthquake Forces

For thousands of years, engineers have been grappling with slope instability as an issue in geotechnical and geological engineering. Earthquake loading has a great impact in slope stability. The present study is to investigate the impact of various soil properties and slope geometry on FOS in the presence as well as in the absence of seismic loading. Here, pseudo-static methods are used to analyses seismic slope stability under earthquake loading. From parametric studies, present work concluded that the FOS values start to decline drastically with earthquake impact. Moreover, various prediction models have been developed using Multiple Linear Regression (MLR) & Multiple Non-Linear Regression (MNLR) considering all the data. The input data of slope stability estimation consists of the values of soil parameter, slope parameter and earthquake loading parameters. As an output, MLR and MNLR predict FOS values as a function of these parameters. The model is then checked by comparing the results to another ten actual situations. It was found that the predicted FOS using MNLR gives satisfactory results.

A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.

Effects of flood wave shape on probabilistic slope stability of dikes under transient groundwater conditions

ABSTRACT The time-dependent response of pore water pressures during floods largely determines the safety against geotechnical failure of dikes, which is deemed to be highly dependent on the uncertain shape (duration, maximum height, etc.) of the flood discharge wave. This paper derives the uncertainty of flood wave shape from a database of precalculated hydrographs (GRADE) and evaluates the effect of shape variability on probabilistic safety estimates of slope stability, using a modelling chain consisting of a transient hydrological model (MODFLOW) and a probabilistic dike slope safety assessment (FORM). Accounting for flood wave uncertainty with transient groundwater flow generally leads to higher reliability estimates for slope stability, compared to the steady-state groundwater condition and other conservative assumptions, but to lower reliability estimates compared to a single design flood wave. Furthermore, the uncertainty of the flood wave shape can be as important as the uncertainty in geotechnical properties. For landside dike slope stability, the volume of the flood wave is the most important factor, while riverside slope stability depends mainly on the total water level drop after the peak. These two waveform characteristics are thus essential uncertainties to consider in probabilistic assessments of dike safety with transient groundwater conditions.

Strength Reduction Method in the Stability Assessment of Vegetated Slopes

Abstract The thoughtful design and mitigation of ecological slope stabilization measures rely heavily on the reliable assessment of the stability of vegetated slopes. This is a complex problem due to the many aspects of vegetation presence that must be taken into account. The numerical model should be able to consider mechanical root reinforcement and root water uptake, which can lead to soil desaturation. This paper presents the application of the strength reduction method to the Modified Cam-Clay model for unsaturated, root-reinforced soils, which allows for the quantitative estimation of slope stability. The technique is implemented in finite element software and tested using several numerical examples. Firstly, the sensitivity of the factor of safety to changes in root constitutive parameters is investigated. In the second example, the stability of the modelled slope is assessed under rainfall of a certain duration followed by progressively modifying soil strength parameters until failure occurs. Furthermore, slope stability is assessed for various durations of the rainfall period.

An effective method for real-time estimation of slope stability with numerical back analysis based on particle swarm optimization

Abstract The purpose of this article is to provide an effective approach to evaluate slope stability in real-time in a reservoir area, which is significant for carrying out risk management for landslide disaster prevention in various engineering practices. A comprehensive idea for stability estimation of bank slope under the influence of rainfall or the reservoir water level is presented in this work. Slope stability analysis and back analysis of soil parameters are both included based on numerical simulation. The mechanical parameters of the bank slope were first back-analyzed using particle swarm optimization (PSO), and real-time stability analysis with high accuracy and efficiency was then established based on multiple continuously monitored displacements. Two case studies were carried out in this study. The results show that (1) based on the real-time monitored displacement and numerical simulation, the mechanical parameters of the slope can be reasonably retrieved through PSO; and (2) based on the inverse mechanical parameters, the safety factors of the slope can be numerically obtained, so that the real-time estimation of slope stability can be realized.

Industrial safety monitoring in iron ore mining in the Kursk Magnetic Anomaly

The mining facilities of Lebedinsky GOK, Stoilensky GOK, Mikhailovsky GOK, Yakovlevsky GOK and KMAruda in the Kursk Magnetic Anomaly annually produce more than 170 Mt of iron ore, which is more than 60% of total iron ore production in Russia. Mining operations in KMA are the hazardous production facilities. High-rate mineral mining, as well as difficult geotechnical and hydrogeological conditions govern disequilibrium in the stress–strain behavior of rock mass. For preventing emergency situations at hazardous production facilities and in accordance with the federal legislation, it is required to carry out permanent geomechanical monitoring. Researchers of the Belgorod State University propose a geomechanical safety monitoring algorithm of iron ore mining in KMA. The monitoring of stability in mined-out voids in Gubkin Mine of KMAruda company is discussed. The recommendations are given for geomechanical monitoring using modern electron-optical and scanning systems. For the slope stability estimation of pitwall and dumps, the Belgorod State University has developed a package of programs OtkosKRUG, Otkos.log, Otkos1 and Slope Stability Calculator, which have passed the state certification. After updating for a few years, the programs include the recent achievement in programming, and enable calculation of safety parameters and stability factor (safety criterion) for slopes by the method of algebraic addition of forces, as is recommended by the Safety Rules for Pitwall and Dump Slopes in effect since 2021.

Week-ahead shallow landslide displacement prediction using chaotic models and robust LSTM

Time-series monitoring of landslide displacement is crucial for controlling the geo-risk associated with sudden landslide occurrence and slope failure. Accurate prediction is valuable for geohazard mitigation in advance of short-term displacement. In this research, a novel chaotic modeling framework is proposed to predict landslide displacement using a robust long short-term memory (LSTM) network. To facilitate the prediction framework, daily instant displacement is measured in three dimensions at 19 monitoring locations. Then, the chaotic characteristics are computed for data reconstruction purposes, and the reconstructed data are selected as inputs in the prediction model. Next, LSTM is applied as the prediction algorithm and is trained using reconstructed field data. A generic LSTM is often trained to minimize the mean square error (MSE) loss, which can be oversensitive to a few outliers. In this research, the pseudo-Huber loss is adopted as the loss function and is integrated with LSTM as an improvement over the MSE loss. The effectiveness and efficiency of the proposed framework have been validated by the benchmark LSTM and other machine learning algorithms. The computational results show that the proposed approach performed better than conventional LSTM and other machine learning algorithms. This framework may be valuable for engineers for practical landslide hazard estimation or rapid preliminary screening of slope stability.

Slope stability estimation using a Danger Level approach for monitoring landslide prone areas

Slope stability estimation using a Danger Level approach for monitoring landslide prone areas

Evaluation of failure of slopes with shaking-induced cracks in response to rainfall

Centrifuge model tests on slopes subject to shaking and rainfall have been performed to examine the response of slopes with shaking-induced cracks to subsequent rainfall and evaluate the corresponding landslide-triggering mechanisms. The failure pattern of the slope subject to shaking and then rainfall was found different from that of the slope subject to only rainfall. When shaking caused cracks on the slope shoulder and rupture line below, the mobilized soil slid along the slip surface that extended to the rupture line, the main crack became the crown of the undisturbed ground once the slope was subject to a subsequent rain event, and the progression of the landslide was related to the rainfall intensity. During the landslide caused by light rainfall, the main scarp kept exposing itself in the vertically downward direction while the ground behind the main crack in the crack-containing slope remained undisturbed. The detrimental effect of cracks on soil displacement was more evident when the slope was exposed to heavy post-shaking rainfall, resulting in a rapid and massive landslide. Additionally, the volume of displaced material of the landslide, the main scarp area on the upper edge, and the zone of accumulation were larger in the crack-containing slope subject to heavy rainfall, in comparison with those in the crack-free slope. The deformation pattern of slopes with shaking-induced cracks during rainfall was closely related to rainfall intensity and the factor of safety provided a preliminary estimation of slope stability during rainfall. Moreover, even when subjected to the same rainfall, the slopes with antecedent shaking-induced cracks displayed different levels of deformation. The slope that experienced larger shaking had greater deformation under the following rainfall, and the shaking-induced slope deformation also controlled the slip surface location. Finally, the velocity of rainfall-induced landslide could be greatly influenced by the prior shaking event alone. Despite being under light rainfall, the slope that has encountered intense previous shaking exhibited an instant landslide.

Stability analysis of a deep highwall slope using numerical modelling and statistical approach—a case study

For opencast working or pit design, understanding the stability of slope is a prime concern for design engineers prior to or during excavation of mineral. The factor of safety is one such index, which has been extensively used for determining the stability of slopes, underground pillars, tunnel structures etc. The numerical simulation of a deep highwall slope of Ramagundam Opencast-II Extension Project (RG OC-II Extn) has been done using finite difference-based software FLAC/SLOPE. Parametric analysis has been conducted by changing one parameter at a time (OPT) and keeping all other parameters fixed in order to understand the influence of various parameters, namely, density, angle of internal friction, cohesion, overall pit slope angle and depth on the stability of the highwall slope. It has been observed that all the parameters undertaken in this study have a strong and well-defined influence on the stability of the same. Kinematic analysis is also performed to illustrate the potential for various modes of highwall slope failures, namely, planar, wedge and toppling failures that may occur due to the presence of unfavourably oriented discontinuities. The analysis revealed that the highwall slope is not susceptible to structurally controlled failure. Subsequently, statistical analysis for building an empirical model for the prediction of factor of safety has been done considering the abovenamed parameters utilizing non-linear regression technique. The developed empirical formula can be used as a guiding tool for estimation of highwall slope stability at any mine in India possessing similar geo-mining conditions.

Slope stability estimation of the «earth bed – road dressing» system with disturbed structure

Off-standard soil-geological systems stability assessing issues under complex conditions is being discussed in this article. Modern methods and tools analytical review for assessing the embankment slope stability was performed here. Embankment work peculiarities in the areas of the permafrost soil spread were discussed in this paper. Special attention is paid to the embankments stability issue on a thawed foundation. The design features of the embankments on permafrost soils with the frozen soils preservation or partial thawing have been studied here. Two variations of sliding surface design-level static circuit are presented in this paper: base – earth bed and base – earth bed – road dressing. The main reasons that lead to the soil slopes stability loss of soil slopes are presented here. Based on the field research results, the embankment soil stress-related characteristics and the earth bed base were determined. Those results became the basis for embankment slope stability mathematical modeling and assessment. In software packages, GeoStab (demo) and Geo5 (educational license), based on the finite element method and the field research results, digital embankment models were built on a weak foundation. The «earth bed – road dressing» system stability calculation under the standard uniformly distributed load has been performed. Two system strengthening options are proposed to increase its stability: reinforcement with soil anchors and geosynthetic materials. The hypothesis sufficiency about the influence of the pavement road dressing state on the embankment stability was also tested here. Two models of the system and the base – earth bed – road dressing are discussed: without cracks on the track and with cracks on the track. It has been elucidated that coating imperfection affects the system stability only in the presence of medium or larger strength earthquakes. Recommendations for strengthening the embankment slopes operating in difficult soil-geological conditions were given in this article.

PROBABILISTIC ASSESSMENT OF ROCK SLOPE STABILITY IN OPEN PIT MINE CHAARAT USING THE GENERALIZED HOEK–BROWN CRITERION

The slope stability evaluation using the generalized Hoek–Brown criterion and regarding the scale effect has been implemented in terms of the Chaarat gold project. Furthermore, the probabilistic assessment and sensitivity analysis are performed. Slope failure probabilities are determined, and the slope stability factors are obtained as functions of the slope height and angle. The slope stability estimation based on classified approach considering the scale effect, including GSI rating and probabilistic analysis is tested in rock slopes. Slope stability is mainly governed by variability of the Geological Strength Index related with the scale effect.

Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm

In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines. For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules–GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters. The factor of safety was used as the model outcome. Artificial neural networks (ANN), support vector regression (SVR), and previously introduced models (such as FFA-SVR, ANN-PSO, ANN-ICA, ANN-GA, and ANN-ABC) were also developed for evaluating the proposed M5Rules–GA model. The evaluation of the model performance involved applying and computing the determination coefficient, variance account for, and root mean square error, as well as a general ranking and color scale. The results confirmed that the proposed M5Rules–GA model is a robust tool for analyzing slope stability. The other investigated models yielded less robust performance under the evaluation metrics.

Stability evaluations of three-layered soil slopes based on extreme learning neural network

ABSTRACT Slope stability analysis is one of the most critical and important topics for geotechnical engineers and thus various stability charts have been developed in order to provide quick first assessments of slope stability. However, general chart solutions are often found to be insufficient for purpose when variables to be considered increase. Motivated by that, this study aims to adopt an artificial neural network (ANN) with the extreme learning machine algorithm to develop a convenient and efficient tool for assessing three-layered soil slope stability. The neural network is trained using the obtained solutions from the finite element upper and lower bound limit analysis methods. The proposed tool in this study is capable of providing a quick first assessment of three-layered soil slope stability. The results showed that the slope stability estimation using the proposed ANN-based tool can still maintain good accuracy and convenience.

Integrated approach to slope stability estimation in deep open pit mines

Integrated approach to slope stability estimation in deep open pit mines

Probabilistic Characterization of Rock Mass from Limited Laboratory Tests and Field Data: Associated Reliability Analysis and Its Interpretation

Probabilistic methods are the most efficient methods to account for different types of uncertainties encountered in the estimated rock properties required for the stability analysis of rock slopes and tunnels. These methods require estimation of various parameters of probability distributions like mean, standard deviation (SD) and distributions types of rock properties, which requires large amount of data from laboratory and field investigations. However, in rock mechanics, the data available on rock properties for a project are often limited since the extents of projects are usually large and the test data are minimal due to cost constraints. Due to the unavailability of adequate test data, parameters (mean and SD) of probability distributions of rock properties themselves contain uncertainties. Since traditional reliability analysis uses these uncertain parameters (mean and SD) of probability distributions of rock properties, they may give incorrect estimation of the reliability of rock slope stability. This paper presents a method to overcome this limitation of traditional reliability analysis and outlines a new approach of rock mass characterization for the cases with limited data. This approach uses Sobol’s global sensitivity analysis and bootstrap method coupled with augmented radial basis function based response surface. This method is capable of handling the uncertainties in the parameters (mean and SD) of probability distributions of rock properties and can include their effect in the stability estimates of rock slopes. The proposed method is more practical and efficient, since it considers uncertainty in the statistical parameters of most commonly and easily available rock properties, i.e. uniaxial compressive strength and Geological Strength Index. Further, computational effort involved in the reliability analysis of rock slopes of large dimensions is comparatively smaller in this method. Present study also demonstrates this method through reliability analysis of a large rock slope of an open pit gold mine in Karnataka region of India. Results are compared with the results from traditional reliability analysis to highlight the advantages of the proposed method. It is observed that uncertainties in probability distribution type and its parameters (mean and SD) of rock properties have considerable effect on the estimated reliability index of the rock slope and hence traditional reliability methods based on the parameters of probability distributions estimated using limited data can make incorrect estimation of rock slope stability. Further, stability of the rock slope determined from proposed approach based on bootstrap method is represented by confidence interval of reliability index instead of a fixed value of reliability index as in traditional methods, providing more realistic estimates of rock slope stability.

Stability of the Railway Subgrade under Condition of Its Elements Damage and Severe Environment

Investigation of damages of the subgrade slope in combination with its overwetting and rolling stock loading, which significantly affects safety traffic of the railway transport, has been carried out. The complex method of slope stability estimation of the subgrade is developed, which includes calculation of loads and vibration action of rolling stock on the main site of the subgrade, as well as the dynamic model of vibrations propagation in the body of the subgrade embankment and the model of plastic deformations accumulation. Dynamic and nonlinear plastic models are based on a finite-element model of the cross section of the subgrade. The plastic model takes into account the characteristics of soil strength of the subgrade, depending on the area of the vibration load impact. The developed method allows to carry out the estimation of external and internal factors impact on occurrence of subgrade destruction, which is of practical value for safety state estimation of the railway transport.