Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-BasedModels

Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.

Critical Assessment of Slope Stability: A Case Study on the Toffo-Lalo Road Project

This article systematically delves into a comprehensive analysis of the latest and most advanced techniques for the assessment of slope stability. It particularly focuses on strategies aimed at enhancing slope stability in road construction. In addition to this analysis, the article presents an illustrative case study centered on the Toffo-Lalo Road Project. The core objective of this paper is to scrutinize the stability of large embankments in road construction, with a specific emphasis on the development and asphalt overlay of the Toffo-Lalo road. This scrutiny is conducted through the utilization of stability calculation software, GEOSTUDIO2018, specifically its SLOPE/W module. Within this framework, a detailed model of the cutbank located at KP1+750-2+250 was meticulously developed. This model takes into account the physical-mechanical characteristics of the soil at the site, as well as the topographic layout. Its attributes include a cohesion value of 11.3 Kpa, a density of 16.57 KN/m3, and a friction angle of 27˚. The modeling results, employing the Morgenstern-Price method—an approach renowned for its adherence to equilibrium conditions and provision of precise results—conclude that the safety coefficient (Fs = 1.429) prior to any reinforcement signifies a critical state of slope stability. To address this, the article explores the implementation of reinforcement techniques, particularly focusing on rigid inclusions like nailing and piles. The modeling exercises reveal a noteworthy enhancement in the safety coefficient (Fs) post-reinforcement. Furthermore, the article undertakes a parametric study to optimize the reinforcement strategies. This analysis highlights that anchoring at 0˚ downward relative to the horizontal plane and employing a pile angle of 90˚ represent the most favorable approaches. These measures yield safety coefficients of 3.60 and 2.34, respectively, indicating substantially improved slope stability.

Strategies for Advancing Road Construction Slope Stability: Unveiling Innovative Techniques for Managing Unstable Terrain

This comprehensive review paper explores various aspects of geotechnical engineering, with a focus on the management of unstable terrains, numerical methods for solving complex soil and consolidation problems, rheological analysis of suspensions and muddy soils, and stability analysis of slopes. It begins by examining the unique physicochemical properties of cohesive sediments, including cohesion and specific surface area. The temporal evolution of deposit concentration and average bed concentration in unstable terrains is discussed, along with settling behavior of isolated particles and hindered settling using empirical equations. Key sedimentation theories, such as Kynch’s theory, and geotechnical consolidation theories, including Terzaghi’s consolidation equation and Gibson’s theory, are presented. The investigation interrelates these theories and principles to offer a holistic view of managing unstable terrains. It also addresses the challenges associated with experimental determination of constitutive relationships and presents alternative simplification methods proposed by researchers. Additionally, it delves into numerical methods for solving nonlinear partial differential equations governing soil behavior, emphasizing the need for numerical frameworks and discussing various techniques and associated challenges. The rheological analysis section covers material flow behavior, rheological behavior models, and the rheological properties of water and cohesive sediment mixtures. Fundamental geotechnical calculations, constitutive laws, and failure criteria are explained, highlighting their relevance in geotechnical engineering applications. This paper provides a multidimensional perspective on geotechnical engineering, offering valuable insights into soil properties, consolidation processes, numerical methods, rheological analysis, and slope stability assessment for professionals in the field.

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.

Finite element analyses of slope stability problems using non-associated plasticity

In recent years, finite element analyses have increasingly been utilized for slope stability problems. In comparison to limit equilibrium methods, numerical analyses do not require any definition of the failure mechanism a priori and enable the determination of the safety level more accurately. The paper compares the performances of strength reduction finite element analysis(SRFEA) with finite element limit analysis(FELA), whereby the focus is related to non-associated plasticity. Displacement-based finite element analyses using a strength reduction technique suffer from numerical instabilities when using non-associated plasticity, especially when dealing with high friction angles but moderate dilatancy angles. The FELA on the other hand provides rigorous upper and lower bounds of the factor of safety(FoS) but is restricted to associated flow rules. Suggestions to overcome this problem, proposed by Davis(1968), lead to conservative FoSs; therefore, an enhanced procedure has been investigated. When using the modified approach, both the SRFEA and the FELA provide very similar results. Further studies highlight the advantages of using an adaptive mesh refinement to determine FoSs. Additionally, it is shown that the initial stress field does not affect the FoS when using a Mohr-Coulomb failure criterion.

Slope stability analysis under seismic load by vector sum analysis method

The vibration characteristics and dynamic responses of rock and soil under seismic load can be estimated with dynamic finite element method （DFEM）. Combining with the DFEM, the vector sum analysis method （VSAM） is employed in seismic stability analysis of a slope in this paper. Different from other conventional methods, the VSAM is proposed based on the vector characteristic of force and current stress state of the slope. The dynamic stress state of the slope at any moment under seismic load can he obtained by the DFEM, thus the factor of safety of the slope at any moment during earthquake can be easily obtained with the VSAM in consideration of the DFEM. Then, the global stability of the slope can be estimated on the basis of time-history curve of factor of safety and reliability theory. The VSAM is applied to a homogeneous slope under seismic load. The factor of safety of the slope is 1.30 under gravity only and the dynamic factor of safety under seismic load is 1.21. The calculating results show that the dynamic characteristics and stability state of the slope with input ground motion can be actually analyzed. It is believed that the VSAM is a feasible and practical approach to estimate the dynamic stability of slopes under seismic load.

Influence of excavation schemes on slope stability: A DEM study

Slope failure due to improper excavation is one of common engineering disasters in China.To explore the failure mechanism of soil slope induced by toe excavation,especially to investigate the influence of excavation unloading path and rate on slope stability,a numerical slope model was built via particle flow code PFC2 D.The development of crack and strain during excavation were obtained and used to evaluate the deformation characteristics.Furthermore,excavation types representing different unloading paths and rates were compared in terms of crack number and strain level.Results indicate that crack number and strain level induced by horizontal column excavation are much greater than those of vertical column excavation and oblique excavation.The crack number and strain level increase with excavation unloading rate.Besides,the feasibility of taking the average strain of slope surface and the average value of maximum strain along monitoring lines to represent the global deformation characteristics were discussed.This study can provide a theoretical guidance for slope monitoring and preliminary optimal selection of excavation scheme in the design and construction of slope engineering.

Assessment of rock slope stability by probabilistic-based Slope Stability Probability Classification method along highway cut slopes in Adilcevaz-Bitlis(Turkey)

Rock slope stability is of great concern along highway routes as stability problems on cut slopes may cause fatal events as well as loss of property.In rock slope engineering,stability evaluations are commonly performed by means of analytical or numerical analyses,principally considering the factor of safety concept.As a matter of fact,the probabilistic assessment of slope stability is progressively getting popularity due to difficulties in assigning the most appropriate values to design parameters in analytical or numerical methods.Additionally,the effect of heterogeneities in rock masses and discontinuities on the analysis results is minimized through the probabilistic concept.In this study,slope stability of high and steep sedimentary rock cut slopes along a state highway in AdilcevazBitlis(Turkey) was evaluated on the basis of probabilistic approach using the Slope Stability Probability Classification(SSPC) system.The probabilistic assessment indicates major slope stability problems because of discontinuity controlled and discontinuity orientation independent mass movements.Almost all studied cut slopes suffer from orientation-independent stability problems with very low stability probabilities.Additionally,the probability of planar and toppling failures issignificantly high with respect to the SSPC system.The stability problems along the investigated rock slopes were also verified by field reconnaissance.Remedial measures such as slope re-design and reinforcement at the studied locations should be taken to prevent hazardous events along the highway.On the other hand,the probabilistic approach may be a useful tool during rock slope engineering to overcome numerous uncertainties when probabilistic and analytic results are compared.

Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest(RF)and extreme gradient boosting(XGBoost).As an illustration,the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County,Chongqing,China.For comparison,the predictive performance of RF,XGBoost,support vector machine(SVM),and logistic regression(LR)is systematically investigated based on the well-established confusion matrix,which contains the known indices of recall rate,precision,and accuracy.Furthermore,the feature importance of the 12 influencing variables is also explored.Results show that the accuracy of the XGBoost and RF for both the training and testing data is superior to that of SVM and LR,revealing the superiority of the ensemble learning models(i.e.XGBoost and RF)in the slope stability prediction of Yunyang County.Among the 12 influencing factors,the profile shape is the most important one.The proposed ensemble learning-based method offers a promising way to rationally capture the slope status.It can be extended to the prediction of slope stability of other landslide-prone areas of interest.

Opposition-Based Firefly Algorithm for Earth Slope Stability Evaluation

This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.

Application of rock mass classification systems to rock slope stability assessment:A case study

The stability of rock slopes is considered crucial to public safety in highways passing through rock cuts, as well as to personnel and equipment safety in open pit mines. Slope instability and failures occur due to many factors such as adverse slope geometries, geological discontinuities, weak or weathered slope materials as well as severe weather conditions. External loads like heavy precipitation and seismicity could play a significant role in slope failure. In this paper, several rock mass classification systems developed for rock slope stability assessment are evaluated against known rock slope conditions in a region of Saudi Arabia, where slopes located in rugged terrains with complex geometry serve as highway road cuts. Selected empirical methods have been applied to 22 rock cuts that are selected based on their failure mechanisms and slope materials. The stability conditions are identified, and the results of each rock slope classification system are compared. The paper also highlights the limitations of the empirical classification methods used in the study and proposes future research directions.

Effects of vertical seismic acceleration on 3D slope stability

The conventional pseudo-static approach often neglects the effect of the vertical＇ seismic acceleration on the stability of a slope, but some analyses under plane-strain （2D） conditions show a significant effect on the slope stability. The purpose of this study is to investigate the effect of the vertical acceleration on the safety of three-dimensional （3D） slopes. In the strict framework of limit analysis, a 3D kinematically admissible rotational failure mechanism is adopted here for 3D homogeneous slopes in frictional/cohesive soils. A set of stability charts is presented in a wide range of parameters for 3D slopes under combined horizontal and vertical seismic loading conditions. Accounting for the effects of the vertical seismic acceleration, the difference in safety factors for 3D slopes can exceed 10%, which will significantly overestimate the safety of the 3D slopes.

AN APPLICATION OF EXTENSION THEORY TO THE EVALUATION OF SLOPE STABILITY

The slope stability is affected by multiple factors. Thecomprehensive evaluation of the slope stability is a complicateproblem. The principles of extension theory have been used in thispaper to establish the matter-element model for the evaluation ofslope stability. The quantitative evaluated results have been giventhrough the calculation of the matter-element dependent degree.

Rapid and robust slope failure appraisal using aerial photogrammetry and 3D slope stability models

Slope failures are an inevitable aspect of economic pit slope designs in the mining industry.Large open pit guidelines and industry standards accept up to 30%of benches in open pits to collapse provided that they are controlled and that no personnel are at risk.Rigorous ground control measures including real time monitoring systems at TARP(trigger-action-response-plan)protocols are widely utilized to prevent personnel from being exposed to slope failure risks.Technology and computing capability are rapidly evolving.Aerial photogrammetry techniques using UAV(unmanned aerial vehicle)enable geotechnical engineers and engineering geologists to work faster and more safely by removing themselves from potential line-of-fire near unstable slopes.Slope stability modelling software using limit equilibrium(LE)and finite element(FE)methods in three dimensions(3D)is also becoming more accessible,user-friendly and faster to operate.These key components enable geotechnical engineers to undertake site investigations,develop geotechnical models and assess slope stability faster and in more detail with less exposure to fall of ground hazards in the field.This paper describes the rapid and robust process utilized at BHP Limited for appraising a slope failure at an iron ore mine site in the Pilbara region of Western Australia using a combination of UAV photogrammetry and 3D slope stability models in less than a shift(i.e.less than 12 h).

Probabilistic analysis of embankment slope stability in frozen ground regions based on random finite element method

Prediction on the coupled thermal-hydraulic fields of embankment and cutting slopes is essential to the assessment on evolution of melting zone and natural permafrost table, which is usually a key factor for permafrost embankment design in frozen ground regions. The prediction may be further complicated due to the inherent uncertainties of material properties. Hence, stochastic analyses should be conducted. Firstly, Karhunen-Loeve expansion is applied to attain the random fields for hydraulic and thermal conductions. Next, the mixed-form modified Richards equation for mass transfer （i.e., mass equation） and the heat transport equation for heat transient flow in a variably saturated frozen soil are combined into one equation with temperature unknown. Furthermore, the finite element formulation for the coupled thermal-hydraulic fields is derived. Based on the random fields, the stochastic finite element analyses on stability of embankment are carried out. Numerical results show that stochastic analyses of embankment stability may provide a more rational picture for the distribution of factors of safety （FOS）, which is definitely useful for embankment design in frozen ground regions.

Comprehensive evaluation of high-steep slope stability and optimal high-steep slope design by 3D physical modeling

High-steep slope stability and its optimal excavation design in Shuichang open pit iron mine were analyzed based on a large 3D physical simulation technique. An optimal excavation scheme with a relatively steeper slope angle was successfully implemented at the northwest wall between Nos. 4 and 5 exploration lines of Shuichang Iron Mine, taking into account the 3D scale effect. The phys-ico-mechanical properties of rock materials were obtained by laboratory tests conducted on sample cores from exploration drilling directly from the iron mine. A porous rock-like composite material was formed for the model, and the mechanical parameters of the material were assessed experimentally;specifically, the effect of water on the sample was quantitatively determined. We adopted an experimental setup using stiff modular applied static loading to carry out a visual excavation of the slope at a random depth. The setup was equipped with acous-tic emission （AE） sensors, and the experiments were monitored by crack optical acquirement, ground penetrating radar, and close-field pho-togrammetry to investigate the mechanisms of rock-mass destabilization in the high-steep slope. For the complex study area, the model re-sults indicated a clear correlation between the model＇s destabilization resulting from slope excavation and the collected monitoring informa-tion. During the model simulation, the overall angle of the slope increased by 1-6 degrees in different sections. Dramatically, the modeled excavation scheme saved over 80 million tons of rock from extraction, generating enormous economic and ecological benefits.

Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability

The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking. Thus, this work aims to develop a procedure for connecting the sand friction angle and the loading orientation. All principal stress rotation tests in the literatures were processed via an artificial neural network. Then, with sensitivity analysis, the effect of intrinsic soil properties,consolidation history, and test sample characteristics on enhancing anisotropy was examined. The results imply that decreasing the grain size of the soil increases the effect of anisotropy on soil shear strength. In addition, increasing the angularity of grains increases the anisotropy effect in the sample. The stability of a sandy slope was also examined by considering the anisotropy in shear strength parameters. If the anisotropy effect is neglected, slope safety is overestimated by 5%-25%. This deviation is more apparent in flatter slopes than in steeper ones. However, the critical slip surface in the most slopes is the same in isotropic and anisotropic conditions.