Modified fuzzy point estimate method and its application to slope reliability analysis

A slope engineering system is a complex system, in which many uncertaintiesexist, including random uncertainties and fuzzy uncertainties. Traditionally, random uncertaintieswere often considered, while fuzzy uncertainties were ignored. Therefore, fuzzy-random methodsshould he proposed. A fuzzy point estimate method was proposed by Dodagoudar, that is, consideringthe effect of fuzzy-random factors, the fuzzy-random limit state function of slopes is changed torandom interval limit state function by the lambda level cutting, then the moments of the functioncan be obtained by the Rosenblueth's method, and the stability state of slopes can be evaluated bysynthesizing a group of moments. But in Dodagoudar's method, Rosenblueth's state function iscomposed of only two kinds of combinations of parameters rather than 2~n kinds of combinations. So amodified fuzzy point estimate method is proposed by the authors, and it is used in a slopereliability analysis.

Reliability analysis of slopes considering spatial variability of soil properties based on efficiently identified representative slip surfaces

Slope reliability analysis considering inherent spatial variability(ISV)of soil properties is timeconsuming when response surface method(RSM)is used,because of the"curse of dimensionality".This paper proposes an effective method for identification of representative slip surfaces(RSSs)of slopes with spatially varied soils within the framework of limit equilibrium method(LEM),which utilizes an adaptive K-means clustering approach.Then,an improved slope reliability analysis based on the RSSs and RSM considering soil spatial variability,in perspective of computation efficiency,is established.The detailed implementation procedure of the proposed method is well documented,and the ability of the method in identifying RSSs and estimating reliability is investigated via three slope examples.Results show that the proposed method can automatically identify the RSSs of slope with only one evaluation of the conventional deterministic slope stability model.The RSSs are invariant with the statistics of soil properties,which allows parametric studies that are often required in slope reliability analysis to be efficiently achieved with ease.It is also found that the proposed method provides comparable values of factor of safety(FS)and probability of failure(Pf)of slopes with those obtained from direct analysis and lite rature.

A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

The randomness of rock joint development is an important factor in the uncertainty of geotechnical engineering stability.In this study,a method is proposed to evaluate the reliability of intermittent jointed rock slope.The least squares support vector machine(LSSVM)evolved by a bacterial foraging optimization algorithm(BFOA)is used to establish a response surface model to express the mapping relationship between the intermittent joint parameters and the slope safety factor.The training samples are obtained from the numerical calculation based on the joint finite element method during this process.Considering the randomness of the intermittent joint parameters in the actual project,each parameter is evaluated at different locations on the site,and its distribution characteristics are counted.According to these statistical results,a large number of parameter combinations are obtained through Monte Carlo sampling.The trained machine learning mapping model is used to obtain the slope safety factor corresponding to each group,and these results are then used to obtain the slope reliability.When the research results were applied to slope disaster treatment along the Yalu River in China’s Jilin Province,it was found that the joint length and joint inclination angle both play key roles in rock slope stability,which should receive more attention in the slope treatment.In summary,this study establishes a method for evaluating the reliability of intermittent jointed rock slope based on an evolutionary SVM model,and its feasibility is verified by engineering application.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.