Probabilistic Analysis

In Slide, you can perform a Probabilistic slope stability analysis to determine the effect of uncertainty or variability of input parameters, on the results of the slope stability analysis.

For an overview of the Probabilistic Analysis procedure in Slide, see the Probabilistic Analysis Overview topic.

The Probabilistic Analysis options in the Project Settings dialog, are described below.

Probabilistic Analysis

To enable a Probabilistic Analysis with Slide, the first thing you must do is select the Probabilistic Analysis checkbox on the Statistics page of the Project Settings dialog. When you select the Probabilistic Analysis checkbox:

Sampling Method

The Sampling Method determines how the statistical input distributions for the random variables you have defined for a Probabilistic Analysis, will be sampled. Two Sampling Methods are available in Slide – Monte Carlo or Latin Hypercube sampling.

Monte Carlo Method

The Monte Carlo sampling technique uses random numbers to sample from the input data probability distributions. Monte Carlo techniques are commonly applied to a wide variety of problems involving random behavior, in geotechnical engineering.

Monte Carlo sampling of Normal distribution (1000 samples)

image\sampling_monte1.gif

Latin Hypercube Method

The Latin Hypercube sampling technique gives comparable results to the Monte Carlo technique, but with fewer samples. The method is based upon "stratified" sampling with random selection within each stratum. This results in a smoother sampling of the probability distributions. Typically, an analysis using 1000 samples obtained by the Latin Hypercube technique will produce comparable results to an analysis of 5000 samples using the Monte Carlo method.

Latin Hypercube sampling of Normal distribution (1000 samples)

image\sampling_latin1.gif

Number of Samples

The Number of Samples which will be generated for each random variable, for the Probabilistic Analysis. For example, if Number of Samples = 1000, then 1000 values of each random variable (for example, Cohesion of Material 1) will be generated, according to the Sampling Method and statistical distribution for each random variable. The analysis will then be run 1000 times, and a safety factor calculated for each sample. This results in a distribution of safety factors, from which the Probability of Failure is calculated.

TIP: how many samples is "enough" ?? You can get a good idea of how many samples is appropriate for your analysis, by viewing a Samples Convergence Plot in the Slide Interpret program (select Statistics > Convergence Plot in the Slide Interpret program). This plot will indicate the minimum number of samples which is necessary to converge to a final answer (i.e. mean safety factor, probability of failure).

Spatial Variability Analysis

This option allows you to define spatial variability of material properties. See the Spatial Variability topic.

Probabilistic Analysis Type

There are two different methods of performing a Probabilistic Analysis with Slide:

These are described below.

Global Minimum Method

The Global Minimum Probabilistic Analysis Type in Slide, is commonly used in slope stability analysis. With this method:

The Global Minimum Probabilistic Analysis Type, assumes that the Probability of Failure calculated for the (Deterministic) Global Minimum slip surface, is representative of the Probability of Failure for the entire slope. In many cases, this may be a valid or reasonable assumption. An alternative method which does not rely on this assumption (the Overall Slope Method) is described below.

NOTE: if you are using the Spatial Variability Analysis option, then the Global Minimum analysis type is NOT available; you MUST use the Overall Slope analysis type.

Overall Slope Method

The Overall Slope Probabilistic Analysis Type in Slide represents a different approach to the probabilistic analysis of slope stability.

The advantage of the Overall Slope method, compared to the Global Minimum method, is that the Overall Slope method does NOT assume that the Probability of Failure for the slope, is equal to the Probability of Failure of the Deterministic Global Minimum slip surface.

Instead, the ENTIRE SEARCH is repeated, using different values (samples) of the input data random variables, for each search iteration. This is perhaps a more rational approach to probabilistic slope stability analysis, since it does not assume a fixed location of the Global Minimum slip surface. This approach would not have been feasible only a few years ago, due to the extensive computation time which would have been involved. With the current speed of the latest personal computers, such an analysis is now practical.

However, it must be pointed out, that the Overall Slope method will involve a substantially greater computation time, than the Global Minimum method. Depending on the Number of Samples, and the complexity of your model, the Overall Slope Probabilistic Analysis in Slide, may take SEVERAL HOURS to complete. In general, you may wish to run an Overall Slope probabilistic analysis, at the end of a day, as an overnight run. Remember that the Slide Compute Engine can run multiple files in succession, so you can set up several files for a Overall Slope Probabilistic Analysis, and run the analyses overnight.

If you have selected multiple analysis methods (e.g. Bishop, Janbu etc) the Overall Slope Probabilistic Analysis is carried out independently for each Analysis Method.

NOTE: if you are using the Spatial Variability Analysis option, then you MUST use the Overall Slope analysis type.

Critical Probabilistic Slip Surface

In addition to the Overall Slope reliability, the Critical Probabilistic Slip Surface is also calculated, when the Probabilistic Analysis Type = Overall Slope. The Critical Probabilistic Surface is the individual slip surface which has the maximum Probability of Failure (and also the Minimum Reliability Index). This slip surface WILL NOT NECESSARILY BE THE SAME AS THE CRITICAL DETERMINISTIC SLIP SURFACE. See the Critical Probabilistic Surface topic for more information.