Back Analysis Of Slope Failure Research Articles

Probabilistic back analysis of rainfall-induced slope failure considering slope survival records from past rainfall events

Many rainfall-induced landslides occur on slopes that have limited, or even no, site investigation data before failure. Probabilistic back analysis of slope failure provides an effective tool to back analyze possible pre-failure soil parameters, thus gaining insights into the mechanism of slope failure. For a slope failure induced by rainfall, the actual factor of safety (FS) is time-variant when time-variant rainfall and infiltration are explicitly modeled. This study proposes a novel probabilistic back analysis method that models explicitly the rainfall triggering mechanism for a rainfall-induced slope failure. Unlike existing methods that are based on a constant FS = 1, the proposed method utilizes FS inequality information for probabilistic back analysis, including slope failure record with FS < 1 and slope survival records from past rainfall events with FS > 1. The proposed method is suitable for high-dimensional problems when soil hydraulic properties are also back analyzed. The proposed method converges to the conventional methods with FS = 1 when the time span of slope failure is narrowed down to a few hours and slope survival records are ignored. Incorporating both slope failure and survival records effectively reduces uncertainties in soil strength and hydraulic parameters.

Assessment of slope instability with effects of critical displacement by using InSAR and FEM

A good knowledge of the mechanism of slope failure in open-cast mines can be understood by evaluating the intrinsic and extrinsic factors and their interactions in causing slope instability, and by gathering displacement information from periodical monitoring. Surface deformations, caused by mining activities at mine sites, are conventionally measured by using survey instruments such as levels, theodolites, total stations, GPS receivers, and photogrammetric cameras. However, conventional long-term monitoring techniques are insufficient due to unfavorable factors such as topographic structure and flora of the observation, urbanization rate, and time delays in obtaining results. Due to this fact that the Interferometric Synthetic Aperture Radar (InSAR) technique has become prominent in recent years as a method that uses satellite data to enable the detection of surface deformations and movements especially in very large areas. In this study, the mechanism of slope failure accurately detected by way of associating mechanical and physical information of the slopes with time-dependent deformation behavior (time series analysis) by periodically monitoring of displacements at the benches of a lignite open pit that experienced interruptions of production due to slope failures. Mining area has been periodically monitored by InSAR and data consisting of the time-dependent behavior of the deformations were correlated with the mechanical and physical property data that were obtained from back analysis of slope failure using finite elements method (FEM) approximation. In this context, a dynamic method was proposed that can predict the failure risk of the slopes at the site before the critical displacement values are not reached.

Slope reliability and back analysis of failure with geotechnical parameters estimated using Bayesian inference

A Bayesian approach is proposed for the inference of the geotechnical parameters used in slope design. The methodology involves the construction of posterior probability distributions that combine prior information on the parameter values with typical data from laboratory tests and site investigations used in design. The posterior distributions are often complex, multidimensional functions whose analysis requires the use of Markov chain Monte Carlo (MCMC) methods. These procedures are used to draw representative samples of the parameters investigated, providing information on their best estimate values, variability and correlations. The paper describes the methodology to define the posterior distributions of the input parameters for slope design and the use of these results for evaluation of the reliability of a slope with the first order reliability method (FORM). The reliability analysis corresponds to a forward stability analysis of the slope where the factor of safety (FS) is calculated with a surrogate model from the more likely values of the input parameters. The Bayesian model is also used to update the estimation of the input parameters based on the back analysis of slope failure. In this case, the condition FS = 1 is treated as a data point that is compared with the model prediction of FS. The analysis requires a sufficient number of observations of failure to outbalance the effect of the initial input parameters. The parameters are updated according to their uncertainty, which is determined by the amount of data supporting them. The methodology is illustrated with an example of a rock slope characterised with a Hoek-Brown rock mass strength. The example is used to highlight the advantages of using Bayesian methods for the slope reliability analysis and to show the effects of data support on the results of the updating process from back analysis of failure.

Probabilistic back analysis of slope failure – A case study in Taiwan

In this paper, the authors present a probabilistic back-analysis of a recent slope failure at a site on Freeway No. 3 in northern Taiwan. Post-event investigations of this failure found uncertain strength parameters and deteriorating anchor systems as the most likely causes for failure. Field measurement after the event indicated an average slip surface of inclination 15°. To account for the uncertainties in input parameters, the probabilistic back analysis approach was adopted. First, the Markov Chain Monte Carlo (MCMC) simulation was used to back-calculate the geotechnical strength parameters and the anchor force. These inverse analysis results, which agreed closely with the findings of the post-event investigations, were then used to validate the maximum likelihood (ML) method, a computationally more efficient back-analysis approach. The improved knowledge of the geotechnical strength parameters and the anchor force gained through the probabilistic inverse analysis better elucidated the slope failure mechanism, which provides a basis for a more rational selection of remedial measures.

Back analysis of the Joss Bay Chalk Cliff Failure using numerical modelling

Use of numerical modelling for back analysis of slope failure can provide a valuable insight into the underlying failure mechanisms and improve our understanding of factors controlling instability and geomorphology of slopes. This paper provides a brief overview of the range of methods that are available for slope instability analyses, and their respective benefits, using the back analysis of the Joss Bay Chalk cliff failure as a case example. A variety of analytical and numerical approaches are used to back analyse the failure to assess the potential impact of both the rock mass strength and discontinuity-controlled behaviour on the potential failure mechanism. Limit equilibrium analysis approaches are used to assess the sensitivity of the simulated model to variation in input parameters. Numerical simulation, constrained by the limit equilibrium models, is then used to evaluate the progressive nature of failure mechanism and directly simulate the trigger mechanism. Importantly, the modelling demonstrates that shear strain initiates at the base of the cliff and subsequently extends upwards resulting in tensile failure in the upper part of the slope. The case example demonstrates the capability of numerical models to simulate the progressive nature of slope failure, and their ability to evaluate the detrimental effects of a wave-cut notch at the base of a cliff.

Back analysis of slope failure with Markov chain Monte Carlo simulation

Field observed performance of slopes can be used to back calculate input parameters of soil properties and evaluate uncertainty of a slope stability analysis model. In this paper, a new probabilistic method is proposed for back analysis of slope failure. The proposed back analysis method is formulated based on Bayes’ theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis–Hasting algorithm. The method is very flexible as any type of prior distribution can be used. The method is also computationally efficient when a response surface method is employed to approximate the slope stability model. An illustrative example of back analysis of a hypothetical slope failure is presented. Effects of jumping distribution functions and number of samples on the efficiency of Markov chains are studied. It is found that the covariance matrix of the jumping function can be set to be one half of the covariance of the prior distribution to achieve a reasonable acceptance rate and that 80,000 samples seem to be sufficient to obtain robust posterior statistics for the example. It is also found that the correlation of cohesion and friction angle of soil does not affect the posterior statistics and the remediation design of the slope significantly, while the type of the prior distribution seems to have much influence on the remediation design.

Undrained Shear Strength of K0 Consolidated Soft Soils

On the basis of critical state soil mechanics, this study derives theoretical formulas for predicting the undrained shear strength of K0 consolidated soft soils in triaxial compression and extension. Although the modified Cam-clay model is often utilized to predict the undrained shear strength of soft clays, it is applicable mainly to isotropically consolidated soils. Because of the anisotropy under K0 consolidation, an inclined elliptical yield surface is chosen, which is different from those methods based on the original Cam-clay model. The inclined elliptical yield surface is testified to be appropriate to the K0 consolidated soft soil and results in a better prediction of undrained strength, especially for the triaxial extension test. It is concluded that the analytical solutions obtained in this paper are in good agreement with the available test results and back-analysis of slope failures. On the basis of the investigation of soil properties, a simple formula is proposed for calculating the mean und...

Reliability in Back Analysis of Slope Failures

The advantages and disadvantages of using a back analysis of slope failures to evaluate soil shear strength are discussed. A methodology is presented herein that allows the implied level of reliability associated with soil shear strength parameters back calculated from slope failures to be estimated. A reliability approach is also used to estimate the probability of failure for a given limit equilibrium slope stability method, design factor of safety, and combination of back calculated Mohr-Coulomb shear strength parameters, c′ and ϕ′. The methodology is illustrated using 39 landslides in the Orinda Formation in the San Francisco Bay area. The impact of additional case histories in the same geologic setting, i.e., a larger data set, on the required design factor of safety for a given probability of failure is also investigated.

A practical procedure for the back analysis of slope failures in closely jointed rock masses

Abstract Where closely jointed rock masses are encountered in slopes, failure can occur both through the rock mass, as a result of combination of macro and micro jointing, and through the rock substance. Determination of the strength of this category of rock mass is extraordinarily difficult since the size of representative specimens is too large for laboratory testing. This difficulty can be overcome by using a non-linear rock mass failure criterion or by back analysis of such slopes to estimate the rock mass strength. In this paper, a practical procedure and a computer program are presented for the back determination of shear strength parameters mobilized in slopes cut in closely jointed rock masses which obey a non-linear failure criterion rather than a linear one. The procedure shows that the constants to derive normal stress dependent shear strength parameters of the failed rock masses can be determined by utilizing a main cross-section and without a pre-determined value of rock mass rating (RMR). Trials are made for different RMR m and RMR s values corresponding to various possible combinations of the constant m and s , which are used in the Hoek–Brown failure criterion, satisfying the limit equilibrium condition. It is also noted that the procedure provides a quick check for the rock mass rating obtained from the site investigations. The method is used in conjunction with the Bishop's method of analysis based on circular slip surfaces. The procedure outlined in this paper has also been satisfactorily applied to documented slope failure case histories in three open pit mines in Turkey.

Technique for the back analysis of slope failures : Nguyen, V U Proc 4th Australia-New Zealand Conference on Geomechanics, Perth, Western Australia, 14–18 May 1984V2, P617–622. Publ Barton: Inst of Engineers, 1984

A technique for the back analysis of slope failures is presented. The technique determines the geomechanical parameters such as cohesion and angle of friction of soil layers in a failed slope at limiting equilibrium by searching for an optimum in an n-dimensional space defined by the n unknown geomechanical parameters. The convergence to the optimum is achieved when the factor of safety determined by the relevant limit equilibrium method is equal to 1.00. Illustrative examples are given and modifications required on existing limit equilibrium computer programs to incorporate the technique are explained. (Author/TRRL)

Interactive graphics in the back-analysis of slope failures : da Gamma, C D Proc 5th Congress of the International Society for Rock Mechanics, Melbourne, 10–15 April 1983V1, PC53–C60. Publ Rotterdam: A. A. Balkema, 1983

Interactive graphics in the back-analysis of slope failures : da Gamma, C D Proc 5th Congress of the International Society for Rock Mechanics, Melbourne, 10–15 April 1983V1, PC53–C60. Publ Rotterdam: A. A. Balkema, 1983