Reliability Analysis Of Slope Stability Research Articles

Reliability analysis of slope stability by neural network (NN), principal component analysis (PCA), and transfer learning (TL) 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. PCA–NN–TL) 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 PCA–NN–TL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Contribution to the Reliability Analysis of Slope Stability Against Sliding

Contribution to the Reliability Analysis of Slope Stability Against Sliding

Influence of copula approaches on reliability analysis of slope stability using random adaptive finite element limit analysis

AbstractRecently, random field copulas have been deployed to characterise the dependent structures of spatially variable soil parameters. This study expands on this concept by implementing finite element limit analysis (FELA) with adaptive meshing alongside the copula approach to perform reliability assessment of a geotechnical problem. A hypothetical slope with drained and undrained soil conditions was used as an example. First, adaptive FELA was carried out on the hypothetical slope. Second, single and multiple dependency soil parameter structures were represented by several selected copulas (Gaussian, Frank and Clayton). Finally, random adaptive FELA was implemented to explore the effects of copula selection on slope stability and reliability analyses. Results show that considerably different slope failure probabilities are produced by different copula approaches, and that failure probabilities reduce for simulating multiple dependency structures. In addition, failure probability exhibits different tendencies between drained and undrained soil conditions as soil parameter cross‐correlations become stronger. Failure probabilities are also different when performing lower and upper bound (UB) random adaptive FELA with each of the selected copulas.

Reliability Analysis of Slope Stability Considering Uncertainty in Water Table Level

AbstractGeotechnical analysis and design are usually based on traditional methods and empirical relationships. However, various challenges, such as the inability to model all parameters involved an...

Reliability analysis of slope stability considering temporal variations of rock mass properties

Temporal variation of rock mass properties, especially the strength degradation due to drying-wetting cycles as well as the acidic wetting fluid (rainfall or reservoir water) is crucial to stability of reservoir rock slopes. ... | Find, read and cite all the research you need on Tech Science Press

Adaptive Monte Carlo simulation method for system reliability analysis of slope stability based on limit equilibrium methods

A slope fails if it slides along any slip surface. Proper assessment of slope failure risk in geological and geotechnical engineering needs to account, rationally, for a large, or even unlimited, number of circular and/or non-circular potential slip surfaces, which can be modeled as components of a series system. Nevertheless, the computational efforts for incorporating a large number of potential slip surfaces into slope system reliability analysis might be prohibitively expensive, particularly if non-circular slip surfaces are considered. This paper develops an efficient direct Monte Carlo simulation (MCS) method, called adaptive MCS, for slope system reliability analysis based on limit equilibrium methods (LEM), in which a novel sample manipulation strategy is proposed in lieu of brute-force exploration of failure samples amongst direct MCS samples. Equations are derived for the proposed approach and are illustrated using two slope examples considering circular and non-circular potential slip surfaces, respectively. Results show that the proposed approach provides proper estimates of slope system failure probability considering a large number of circular and/or non-circular potential slip surfaces. It significantly improves the computational efficiency of LEM-based slope system reliability analysis compared with brute-force exploration of failure samples.

Reliability analysis of Unsaturated Slope Stability Considering Spatial Variability in Hydraulic Parameters

Reliability analysis of slope stability provides a rational framework for slope safety evaluation, which plays a key role in geotechnical decision making for landslide disaster prevention. However, previous studies mainly focused on the slope reliability analysis accounting for uncertainties in shear strength parameters. How to perform reliability analysis of unsaturated slope stability considering the spatial variability of hydraulic parameters remains an open question. This paper aims to systematically investigate the effects of spatial variability of hydraulic parameters on the unsaturated slope reliability. The random fields of hydraulic parameters are derived from the random void ratio field, instead of direct generation, for considering the spatial variability of hydraulic parameters in a rational manner. It is found that the coefficient of variation (COV) and scale of fluctuation (SOF) of void ratio affects the unsaturated slope failure probability significantly. Additionally, the failure probability is sensitive to the selection of auto-correlation function (ACF) in the unsaturated slope reliability analysis.

A Numerical Investigation of the Effect of Isotropic Spatially Variable Tensile Strength on Slope Stability

The impact of uncertainty on the reliability of slope design is often significant. Spatial variability of soil properties has been recognized as an important factor influencing the reliability of the slope. This paper aims to investigate the influence of isotropic spatially variable tensile strength on the reliability of reinforced slope. For slope safety assessment, random finite element method represents a rigorous tool to combine spatially variable tensile strength into reliability analysis of slope stability. Numerical results showed that the spatial autocorrelation distance and the coefficient of variation of tensile strength for isotropic random field have a large influence on the probability of failure and on the normal forces when the isotropic autocorrelation distance is small in combination with a higher value of coefficient of variation of tensile strength.

System reliability analysis of slopes based on the method of slices using sequential compounding method

Reliability analysis of slope stability using limit equilibrium approaches without considering the correlation between slices can lead to obtaining unrealistic reliability indices. In this paper, considering the method of slices and by selecting stochastic soil parameters, the most critical failure surface is determined by the particle swarm optimization algorithm. The procedure is continued up to 2,000 iterations for an arbitrary slope. Then, for each critical failure surface, the reliability index is determined considering the slices as parallel components of a system with cross-correlation. Through this procedure, the effects of safety margin against the instability of each slice can be implemented to analyze the slope stability. The system reliability index of the slope is finally determined by combining the reliability indices of the critical surfaces based on their corresponding correlation by the Sequential Compounding Method (SCM) as a powerful technique for reducing the time and computational cost. The results are verified by Monte Carlo Simulation (MCS); however, this method is quite time-consuming for this purpose.

Reliability analysis of slope stability in earthen dams following rapid drawdown

This work was dedicated to assessing reliability of slope stability in earthen dams following rapid drawdown. Modeling of the problem physics was based on saturated-unsaturated seepage flow through the nonlinear Richards equation, under the assumption of two-dimensional approximation in steady state and transient state. The modeling of seepage taking place inside the earth dam infill materials included soil parameters, the negative pore water pressure-hydraulic conductivity relationship as well as the negative pore water pressure-volumetric water content. Slope stability was analyzed by using safety factor as evaluated by the Bishop's simplified method. Solution of the governing equations in terms of pore water pressure and fluid flow velocity was performed by using the modules SEEP/W and SLOPE/W of the Finite Element based software package GeoStudio. The drawdown phenomenon was then analyzed and its influence on dam stability assessed by using surface response method and Monte Carlo simulation. A design formula was proposed in order to bound the probability of failure associated to slope stability.

Two-Dimensional Random Field Conditioned by CPT Data: Case Study

The presented geotechnical random field (GRF) algorithm is utilized to realize the soil inherent variability based on in situ site investigation results. This paper aims at investigating the effect of cone penetration test (CPT) borehole arrangements in horizontal plane on slope stability reliability analysis considering stochastic fields of soil design parameters via the conventional stationary along with the non-stationary random field adopted in the GRF approach. An advantage of the GRF algorithm is that it yields conservative results in comparison to the conventional stationary random field theory; therefore, using non-stationary random field underestimates slope stability reliability. The current study tried to highlight the importance of the availability of in situ profiles of soil strength parameters when stability analysis of geotechnical structures is concerned. Slope stability was chosen to interpret the role of the CPT profiles in the adjacent site in determining the factor of safety of the slope against shear failure. Furthermore, the results of the case under study showed that when the geometric dimensions of the slope are too small compared to the horizontal correlation length, more CPT borehole data availability leads only to very slightly conservative results in comparison to the case with single available CPT profile.

A hybrid Monte Carlo-Simulated Annealing approach for reliability analysis of slope stability considering the uncertainty in water table level

Slope stability analysis is one of the most challenging problems in geotechnical engineering. Due to the uncertainty involved in the problem, probabilistic methods are recently utilized to estimate the probability of slope failure. In this study, the slope stability is presented as an optimization problem and the Simulated Annealing (SA) method is used to identify the slip surface with the minimum safety factor. The SA algorithm is then integrated with the Monte Carlo sampling method to calculate the probability of slope failure. Next, the groundwater level is added to the problem formulation as a random variable to model the uncertainty for cases where the groundwater table is not accurately determined. The results of the analysis showed that for slopes with low safety factors, the uncertainty of the groundwater table worsens the condition and increases the failure probability. Moreover, the parametric study showed that the insufficient understanding of groundwater level distribution and the assumption of uniform distribution further increases the failure probability. The results of this study are presented in the form of a probability curve against the groundwater table. This presentation has practical applications in real projects.

Reliability analysis of slope stability under seismic condition during a given exposure time

Evaluating the failure probability of a slope under the seismic condition during a given exposure time is important for performance-based assessment of slope stability. In this paper, a two-stage method is suggested to study the seismic stability of a slope during a given exposure time. In the first stage, the exceedance probability of the horizontal pseudo-static acceleration is evaluated. In the second stage, the vulnerability curve of the slope, which shows the relationship between the horizontal pseudo-static acceleration and the failure probability of the slope, is established. The failure probability of the slope during a given exposure time is then assessed by combining the exceedance probability curve of the horizontal pseudo-static acceleration and the vulnerability curve of the slope. Examples investigated show that the reliability of a slope under the seismic condition is controlled by multiple slip surfaces. A slope may have different failure probabilities during the same exposure time when it is at different locations because of different levels of ground shaking. Event at the same site, different slopes may have different failure probability because of the difference in factors like slope geometries and geological conditions. The method suggested in this paper can be used to quantify the effect of the above factors on the reliability of a slope.

System reliability analysis of slope stability using generalized Subset Simulation

Slope failure mechanisms (e.g., why and where slope failure occurs) are usually unknown prior to slope stability analysis. Several possible failure scenarios (e.g., slope sliding along different slip surfaces) can be assumed, leading to a number of scenario failure events of slope stability. How to account rationally for various scenario failure events in slope stability reliability analysis and how to identify key failure events that have significant contributions to slope failure are critical questions in slope engineering. In this study, these questions are resolved by developing an efficient computer-based simulation method for slope system reliability analysis. The proposed approach decomposes a slope system failure event into a series of scenario failure events representing possible failure scenarios and calculates their occurrence probabilities by a single run of an advanced Monte Carlo simulation (MCS) method, called generalized Subset Simulation (GSS). Using GSS results, representative failure events (RFEs) that are considered relatively independent are identified from scenario failure events using probabilistic network evaluation technique. Their relative contributions are assessed quantitatively, based on which key failure events are determined. The proposed approach is illustrated using a soil slope example and a rock slope example. It is shown that the proposed approach provides proper estimates of occurrence probabilities of slope system failure event and scenario failure events by a single GSS run, which avoids repeatedly performing simulations for each failure event. Compared with direct MCS, the proposed approach significantly improves computational efficiency, particularly for failure events with small failure probabilities. Key failure events of slope stability are determined among scenario failure events in a cost-effective manner. Such information is valuable in making slope design decisions and remedial measures.

Efficient slope reliability analysis at low-probability levels in spatially variable soils

Direct Monte Carlo simulation for the reliability analysis of slope stability with spatially variable soil properties suffers from a serious lack of efficiency when the probability of failure, pf, is low (e.g., pf<10-6). Based on the multiple response surfaces and Subset simulation, this paper proposes an efficient approach for the estimating of small probabilities of slope failure in spatially variable soils. An improved Cholesky decomposition technique is presented for the simulation of the globally non-stationary random fields of spatially variable soil properties in the multiple soil layers. Two slope examples are investigated to demonstrate the effectiveness of the proposed method. The efficiency of the proposed approach for parametric sensitivity analysis is also highlighted.

Multi-modal Reliability Analysis of Slope Stability

Probabilistic slope stability analysis typically requires an optimisation technique to locate the most probable slip surface. However, for many slopes particularly those containing many different soil layers or benches several distinct critical slip surfaces may exist. Furthermore, in large slopes these critical slip surfaces may be located at significant distances from each other. In such circumstances, finding and rehabilitating the most probable failure surface is of little merit, as rehabilitating that surface does not improve the safety of the slope as a whole. Unfortunately, existing slip surface search techniques were developed to converge on one global minimum. Therefore, to implement such methods to evaluate the stability of a slope with multiple failure mechanisms requires the user to define probable slip locations prior to calculation. This requires extensive engineering experience and places undue responsibility on the engineer in question. This paper proposes the use of a locally informed particle swarm optimisation method which is able to simultaneously converge to multiple critical slip surfaces. This optimisation model when combined with a reliability analysis is able to define all areas of concern within a slope. A case study of a railway slope is presented which highlights the benefits of the model over single objective optimisation models. The approach is of particular benefit when evaluating the stability of large existing slopes with complicated stratigraphy as these slopes are likely to contain multiple viable slip surfaces.

Efficient System Reliability Analysis of Slope Stability in Spatially Variable Soils Using Monte Carlo Simulation

Monte Carlo simulation (MCS) provides a conceptually simple and robust method to evaluate the system reliability of slope stability, particularly in spatially variable soils. However, it suffers from a lack of efficiency at small probability levels, which are of great interest in geotechnical design practice. To address this problem, this paper develops a MCS-based approach for efficient evaluation of the system failure probability Pf,s of slope stability in spatially variable soils. The proposed approach allows explicit modeling of the inherent spatial variability of soil properties in a system reliability analysis of slope stability. It facilitates the slope system reliability analysis using representative slip surfaces (i.e., dominating slope failure modes) and multiple stochastic response surfaces. Based on the stochastic response surfaces, the values of Pf,s are efficiently calculated using MCS with negligible computational effort. For illustration, the proposed MCS-based system reliability analysis is applied to two slope examples. Results show that the proposed approach estimates Pf,s properly considering the spatial variability of soils and improves the computational efficiency significantly at small probability levels. With the aid of the improved computational efficiency offered by the approach, a series of sensitivity studies are carried out to explore the effects of spatial variability in both the horizontal and vertical directions and the cross-correlation between uncertain soil parameters. It is found that both the spatial variability and cross-correlation affect Pf,s significantly. The proposed approach allows more insights into such effects from a system analysis point of view.

Comparative Analysis of Stability for Slope Engineering with Reliability Analysis and Limit Equilibrium Method

To take the natural differences of geotechnical materials into consideration, reliability analysis of slope stability with Monte-Carlo method was carried out on a slope of deep pit. The results show that Monte-Carlo method is equal to the limit equilibrium method, the most dangerous sliding surfaces matching the practice could be found according to the method introduced in this paper.

The Sensitivity and Reliability Analysis of Slope Stability Based on Strength Reduction FEM

A new method for reliability and sensitivity analysis of slope stability is proposed in this paper, which is based on strength reduction FEM. In this proposed method, the safety factor of slope can be calculated without assuming the shape and position of failure surface beforehand, and the randomness of physical and mechanical parameters of slope are considered in the calculation process. Meanwhile, the slopes failure probability is researched when the coefficients of variability change, and some useful conclusions are achieved.

Risk de-aggregation and system reliability analysis of slope stability using representative slip surfaces

This paper develops a risk de-aggregation and system reliability approach to evaluate the slope failure probability, pf, using representative slip surfaces together with MCS. An efficient procedure is developed to strategically select the candidate representative slip surfaces, and a risk de-aggregation approach is proposed to quantify contribution of each candidate representative slip surface to the pf, identify the representative slip surfaces, and determine how many representative slip surfaces are needed for estimating the pf with reasonable accuracy. Risk de-aggregation is performed by collecting the failure samples generated in MCS and analyzing them statistically. The proposed methodology is illustrated through a cohesive soil slope example and validated against results from previous studies. When compared with the previous studies, the proposed approach substantially improves the computational efficiency in probabilistic slope stability analysis. The proposed approach is used to explore the effect of spatial variability on the pf. It is found that, when spatial variability is ignored or perfect correlation assumed, the pf of the whole slope system can be solely attributed to a single representative slip surface. In this case, it is theoretically appropriate to use only one slip surface in the reliability analysis. As the spatial variability becomes growingly significant, the number of representative slip surfaces increases, and all representative slip surfaces (i.e., failure modes) contribute more equally to the overall system risk. The variation of failure modes has substantial effect on the pf, and all representative surfaces have to be incorporated properly in the reliability analysis. The risk de-aggregation and system reliability approach developed in this paper provides a practical and efficient means to incorporate such a variation of failure modes in probabilistic slope stability analysis.