Influence of joint spacing and rock characteristics on the toppling stability of cut rock slope through a simplified limit equilibrium method

Toppling failure of rock mass/soil slope is an important geological and environmental problem.Clarifying its failure mechanism under different conditions has great significance in engineering.The toppling failure of a cutting slope occurred in a hydropower station in Kyushu,Japan illustrates that the joint characteristic played a significant role in the occurrence of rock slope tipping failure.Thus,in order to consider the mechanical properties of jointed rock mass and the influence of geometric conditions,a simplified analytical approach based on the limit equilibrium method for modeling the flexural toppling of cut rock slopes is proposed to consider the influence of the mechanical properties and geometry condition of jointed rock mass.The theoretical solution is compared with the numerical solution taking Kyushu Hydropower Station in Japan as one case,and it is found that the theoretical solution obtained by the simplified analysis method is consistent with the numerical analytical solution,thus verifying the accuracy of the simplified method.Meanwhile,the Goodman-Bray approach conventionally used in engineering practice is improved according to the analytical results.The results show that the allowable slope angle may be obtained by the improved Goodman-Bray approach considering the joint spacing,the joint frictional angle and the tensile strength of rock mass together.

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.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Infiltration,runoff,and slope stability behaviors of infinite slope with macropores based on an improved Green–Ampt model

Infiltration–runoff–slope instability mechanism of macropore slope under heavy rainfall is unclear.This paper studied its instability mechanism with an improved Green–Ampt(GA)model considering the dual-porosity(i.e.,matrix and macropore)and ponding condition,and proposed the infiltration equations,infiltration–runoff coupled model,and safety factor calculation method.Results show that the infiltration processes of macropore slope can be divided into three stages,and the proposed model is rational by a comparative analysis.The wetting front depth of the traditional unsaturated slope is 17.2%larger than that of the macropore slope in the early rainfall stage and 27%smaller than that of the macropore slope in the late rainfall stage.Then,macropores benefit the slope stability in the early rainfall but not in the latter.Macropore flow does not occur initially but becomes pronounced with increasing rainfall duration.The equal depth of the wetting front in the two domains is regarded as the onset criteria of macropore flow.Parameter analysis shows that macropore flow is delayed by increasing proportion of macropore domain(ω_(f)),whereas promoted by increasing ratio of saturated permeability coefficients between the two domains(μ).The increasing trend of ponding depth is sharp at first and then grows slowly.Finally,when rainfall duration is less than 3 h,ωf andμhave no significant effect on the safety factor,whereas it decreases with increasingωf and increases with increasingμunder longer duration(≥3 h).With the increase ofω_(f),the slope maximum instability time advances by 10.5 h,and with the increase ofμ,the slope maximum instability time delays by 3.1 h.

Stability analysis of loose accumulation slopes under rainfall:case study of a high‑speed railway in Southwest China

The high and steep slopes along a high-speed railway in the mountainous area of Southwest China are mostly composed of loose accumulations of debris with large internal pores and poor stability,which can easily induce adverse geological disasters under rainfall conditions.To ensure the smooth construction of the high-speed railway and the subsequent safe operation,it is necessary to master the stability evolution process of the loose accumulation slope under rainfall.This article simulates rainfall using the finite element analysis software’s hydromechanical coupling module.The slope stability under various rainfall situations is calculated and analysed based on the strength reduction method.To validate the simulation results,a field monitoring system is established to study the deformation characteristics of the slope under rainfall.The results show that rainfall duration is the key factor affecting slope stability.Given a constant amount of rainfall,the stability of the slope decreases with increasing duration of rainfall.Moreover,when the amount and duration of rainfall are constant,continuous rainfall has a greater impact on slope stability than intermittent rainfall.The setting of the field retaining structures has a significant role in improving slope stability.The field monitoring data show that the slope is in the initial deformation stage and has good stability,which verifies the rationality of the numerical simulation method.The research results can provide some references for understanding the influence of rainfall on the stability of loose accumulation slopes along high-speed railways and establishing a monitoring system.

Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite,widely distributed across the globe,has conventionally been a focus of research in underground engineering,the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence.This is primarily due to their unique properties,including low strength and loose structure.Current methods for evaluating slope stability,such as basic quality(BQ)and slope stability probability classification(SSPC),do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite.To address this challenge,an analysis of the applicability of the limit equilibrium method(LEM),BQ,and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan,Southwest China.However,conflicting results were obtained.Therefore,this paper introduces a novel method,SMRDDS,to provide rapid and accurate assessment of disintegrated dolomite slope stability.This method incorporates parameters such as disintegrated grade,joint state,groundwater conditions,and excavation methods.The findings reveal that six slopes exhibit stability,while two are considered partially unstable.Notably,the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods.However,due to the limited research on disintegrated dolomite slopes,the results of the SMRDDS method tend to be conservative as a safety precaution.In conclusion,the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field.This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

A Calculation Method of Double Strength Reduction for Layered Slope Based on the Reduction of Water Content Intensity

The calculation of the factor of safety(FOS)is an important means of slope evaluation.This paper proposed an improved double strength reductionmethod(DRM)to analyze the safety of layered slopes.The physical properties of different soil layers of the slopes are different,so the single coefficient strength reduction method(SRM)is not enough to reflect the actual critical state of the slopes.Considering that the water content of the soil in the natural state is the main factor for the strength of the soil,the attenuation law of shear strength of clayey soil changing with water content is fitted.This paper also establishes the functional relationship between different reduction coefficients.Then,a USDFLD subroutine is programmed using the secondary development function of finite element software.Controlling the relationship between field variables and calculation time realizes double strength reduction applicable to the layered slope.Finally,by comparing the calculation results of different examples,it is proved that the stress and displacement distribution of the critical slope state obtained by the improved method is more realistic,and the calculated safety factor is more reliable.The newly proposedmethod considers the difference of intensity attenuation between different soil layers under natural conditions and avoids the disadvantage of the strength reduction method with uniform parameters,which provides a new idea and method for stability analysis of layered and complex slopes.

A vector sum analysis method for stability evolution of expansive soil slope considering shear zone damage softening

Slope stability analysis is a classical mechanical problem in geotechnical engineering and engineering geology.It is of great significance to study the stability evolution of expansive soil slopes for engineering construction in expansive soil areas.Most of the existing studies evaluate the slope stability by analyzing the limit equilibrium state of the slope,and the analysis method for the stability evolution considering the damage softening of the shear zone is lacking.In this study,the large deformation shear mechanical behavior of expansive soil was investigated by ring shear test.The damage softening characteristic of expansive soil in the shear zone was analyzed,and a shear damage model reflecting the damage softening behavior of expansive soil was derived based on the damage theory.Finally,by skillfully combining the vector sum method and the shear damage model,an analysis method for the stability evolution of the expansive soil slope considering the shear zone damage softening was proposed.The results show that the shear zone subjected to large displacement shear deformation exhibits an obvious damage softening phenomenon.The damage variable equation based on the logistic function can be well used to describe the shear damage characteristics of expansive soil,and the proposed shear damage model is in good agreement with the ring shear test results.The vector sum method considering the damage softening behavior of the shear zone can be well applied to analyze the stability evolution characteristics of the expansive soil slope.The stability factor of the expansive soil slope decreases with the increase of shear displacement,showing an obvious progressive failure behavior.

Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

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.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Prediction of the instability probability for rainfall induced landslides:the effect of morphological differences in geomorphology within mapping units

Slope units is an effective mapping unit for rainfall landslides prediction at regional scale.At present,slope units extracted by hydrology and morphological method report very different morphological feature and boundaries.In order to investigate the effect of morphological difference on the prediction performance,this paper presents a general landslide probability analysis model for slope units.Monte Carlo method was used to describe the spatial uncertainties of soil mechanical parameters within slope units,and random search technique was performed to obtain the minimum safety factor;transient hydrological processes simulation was used to provide key hydrological parameters required by the model,thereby achieving landslide prediction driven by quantitative precipitation estimation and forecasting data.The prediction performance of conventional slope units(CSUs)and homogeneous slope units(HSUs)were analyzed in three case studies from Fengjie County,China.The results indicate that the mean missing alarm rate of CSUs and HSUs are 31.4% and 10.6%,respectively.Receiver Operating Characteristics(ROC)analysis also reveals that HSUs is capable of improving the overall prediction performance,and may be used further for rainfall-induced landslide prediction at regional scale.

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.

Theoretical study on stability evolution of soft and hard interbedded bedding reservoir slopes

Soft and hard interbedded bedding rock slopes,which is prone to failure,are widely distributed in the Three Gorges Reservoir,China.Limit equilibrium method(LEM)is commonly used to analyze the stability of bedding rock slopes that have a single failure plane.However,this method cannot accurately estimate the stability of soft and hard interbedded bedding reservoir slopes because the strength parameters of a soft and hard interbedded rock mass vary spatially along the bedding plane and deteriorate with time due to periodic fluctuations of reservoir level.A modified LEM is proposed to evaluate the stability evolution of soft and hard interbedded bedding reservoir slopes considering the spatial variation and temporal deterioration of shear strength parameters of rock masses and bedding planes.In the modified LEM,the S-curve model is used to define the spatial variation of shear strength parameters,and general deterioration equations of shear strength parameters with the increasing number of wettingdrying cycles(WDC)are proposed to describe the temporal deterioration.Also,this method is applied to evaluate the stability evolution of a soft and hard interbedded bedding reservoir slope,located at the Three Gorges Reservoir.The results show that neglecting the spatial variation and temporal deterioration of shear strength parameters may overestimate slope stability.Finally,the modified LEM provides useful guidance to reasonably evaluate the long-term stability of soft and hard interbedded bedding reservoir slopes in reservoir area.

Novel protection systems for the improvement in soil and water stability of expansive soil slopes

To improve the soil and water stability of expansive soil slopes and reduce the probability of slope failure,novel protection systems based on polymer waterproof coatings(PWC)were used in this study.Herein,three groups of expansive soil slope model tests were designed to investigate the effects of polyester nonwovens and PWC(P-PWC)composite protection system,three-dimensional vegetation network and PWC(T-PWC)composite protection system,and nonprotection on the soil and water behavior in the slopes under precipitation–evaporation cycles.The results showed that the moisture change of P-PWC and T-PWC composite protected slopes was significantly smaller than that of bare slope,which reduced the sensitivity of slope moisture to environmental changes and improved its stability.The soil temperature of the slope protected by the P-PWC and T-PWC systems at a depth of 70 cm increased by 5.6℃ and 2.7℃,respectively.Using PWC composite protection systems exhibited better thermal storage performance,which could increase the utilization of shallow geothermal resources.Moreover,the maximum average crack widths of the bare slopes were 7.89 and 3.17 times those of the P-PWC and TPWC protected slopes,respectively,and the maximum average crack depths were 6.87 and 3 times those of the P-PWC and T-PWC protected slopes,separately.The PPWC protection system weakened the influence of hydro–thermal coupling on the slopes,inhibited the development of cracks on the slopes,and reduced the soil erosion.The maximum soil erosion of slopes protected by P-PWC and T-PWC systems was 332 and 164 times lower than that of bare slope,respectively.The P-PWC and T-PWC protection systems achieved excellent"anti-seepage and moisture retention"and anti-erosion effects,thus improving the soil and water stability of slopes.These findings can provide important guiding reference for controlling rainwater infiltration and soil erosion in expansive soil slope projects.

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.

Comprehensive Analysis Method of Slope Stability Based on the Limit Equilibrium and Finite Element Methods and Its Application

To study the safety and stability of large slopes, taking the right side slope of the Yuxi’an tunnel of the Yuchu Expressway Bridge in Yunnan Province as an example, limit equilibrium and finite element analysis were applied to engineering examples to calculate the stability coefficient of the slope before and after excavation in the natural state. After comparative analysis, it was concluded that the former had a clear mechanical model and concept, which could quickly provide stability results;the latter could accurately determine the sliding surface of the slope and simulate the stress state changes of the rock and soil mass. The stability coefficients calculated by the two methods were within the stable range, but their values were different. On this basis, combined with the calculation principles, advantages and disadvantages of the two methods, a comprehensive analysis method of slope stability based on the limit equilibrium and finite element methods was proposed, and the rationality of the stability coefficient calculated by this method was judged for a slope case.

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.