Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.

Earth slope reliability analysis under seismic loadings using neural network

A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.

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.

New algorithm of mine slope reliability based on limiting state hyper-plane and its engineering application

Due to the influence of joint fissure, mining intensity, designed slope angle, underground water and rainfall, the failure process of mine slope project is extremely complicated. The current safety factor calculation method has certain limitations, and it would be difficult to obtain the reliability index when the performance function of reliability analysis is implicit or has high order terms. Therefore, with the help of the logistic equation of chaos theory, a new algorithm of mine slope reliability based on limiting state hyper-plane is proposed. It is shown that by using this new reliability algorithm the calculation of partial derivative of performance function is avoided, and it has the advantages of being simple and easy to program. The new algorithm is suitable for calculating the reliability index of complex performance function containing high order terms. Furthermore, the limiting state hyper-plane models of both simplified Bishop's and Janbu's method adaptive to slope project are obtained, and have achieved satisfactory effect in the study of mine slope stability in Dexing copper open pit.

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.