# 2 - Probabilistic Analysis

In Tutorial 01 - Quick Start, you learned about the three main analysis types in **SWedge**: Deterministic, Probabilistic, and Combinations, and how to run a Deterministic analysis (the default). In this tutorial, you will learn how to run a Probabilistic analysis.

In a Probabilistic analysis, statistical input data can be entered to account for uncertainty in joint orientation and strength values. The result is a distribution of Factors of Safety, from which a Probability of Failure is calculated. To learn more about Probabilistic Analysis, see Overview of Probabilistic Analysis in SWedge.

Topics Covered in This Tutorial:

- Project Settings
- Random Variables
- Fisher Distribution
- Tension Crack
- Mean Wedge
- Picked Wedges
- Histograms
- Scatter Plots
- Stereonet View
- Show Failed Wedges
- Failure Mode Filter
- Design Factor of Safety

Finished Product:

The finished product of this tutorial can be found in the *Tutorial 02 Probabilistic.swd7* file, located in the **Examples > Tutorials** folder in your **SWedge** installation folder.

## 1.0 Creating a New File

If you have not already done so, run the **SWedge** program by double-clicking the **SWedge** icon in your installation folder or by selecting **Programs > Rocscience > SWedge > SWedge** in the Windows **Start** menu.

When the program starts, a default model is automatically created. If you do NOT see a model on your screen:

**Select:** File > New

Whenever a new file is created, the default input data forms valid slope geometry, as shown in the image below.

If the **SWedge** application window is not already maximized, maximize it now so that the full screen is available for viewing the model.

Notice the four-pane, split screen format of the display, which shows **Top**, **Front**, **Side** and **Perspective** views of the model. This view is referred to as the **Wedge View**. The **Top**, **Front** and **Side** views are orthogonal with respect to each other (i.e., viewing angles differ by 90 degrees.)

## 2.0 Project Settings

The **Project Settings** dialog allows you to configure the main analysis parameters for your model (i.e., Analysis Type, Units, Sampling Method, etc.). Select **Project Settings** on the toolbar or on the **Analysis** menu.

**Select:** Analysis > Project Settings

### 2.1 ANALYSIS TYPE AND BLOCK SHAPE

By default a Deterministic analysis is selected for a new file.

- Select the
**General**tab. - Change the
**Analysis Type**to**Probabilistic**. - Keep
**Block Shape**as**Wedge**.

### 2.2 UNITS

For this tutorial we will be using Metric units, so make sure the **Metric, stress as MPa** option is selected for **Units**.

### 2.3 SAMPLING AND RANDOM NUMBERS

- Select the
**Sampling**tab.

The**Sampling Method**determines how the statistical distribution for the random input variables will be sampled. The default settings are**Sampling Method**=**Latin Hypercube**and**Number of Samples**=**10,000**. For more help, see Sampling in SWedge Project Settings.

Note the**Design Factor of Safety**option. This option is used in Probabilistic and Combinations analyses to determine both the Probability of Failure and the number of failed wedges in graphs. The Probability of Failure is now P(FS < Design FS). For this tutorial, keep**Design Factor of Safety =****1**. - Select the
**Random Numbers**tab.

Note that**Random Number Generation**is set to**Pseudo-random**by default. This allows you to obtain reproducible results for a Probabilistic analysis by using the same seed value to generate random numbers. We will discuss Pseudo-random versus Random sampling later in this tutorial.

### 2.4 PROJECT SUMMARY

- Select the
**Project Summary**tab and enter "**SWedge Probabilistic Analysis Tutorial**" as the**Project Title**. - Click
**OK**to close the**Project Settings**dialog.

**NOTE:** You can have **Project Summary** information appear on analysis results printouts by setting up a header or footer through **Page Setup** on the **File** menu.

## 3.0 Probabilistic Input Data

To enter input data for the Probabilistic analysis, select **Input Data** on the tool bar or on the **Analysis** menu.

**Select:** Analysis > Input Data

For a Probabilistic analysis, the **Input Data** dialog is organized under several tabs as shown below.

To carry out a Probabilistic analysis with **SWedge**, at least one input parameter must be defined as a random variable. To define a random variable, you select a statistical distribution (e.g., Normal, Lognormal, Fisher, etc.) for the variable and enter appropriate parameters (e.g., Standard Deviation, Relative Minimum, Relative Maximum, etc.).

**NOTE:** For more information about statistical input, see Overview of Statistical Distributions in SWedge.

For the example in this tutorial, we will define the following input parameters as random variables:

- Joint 1 orientation
- Joint 1 shear strength
- Joint 2 orientation
- Joint 2 shear strength
- Tension Crack orientation

All other input parameters are assumed to be exactly known (i.e., **Statistical Distribution = None**) and will not be involved in the statistical sampling.

### 3.1 SLOPE

Select the **Slope** tab. For this tutorial, we assume the orientation of the slope plane is constant so we won't set any statistical distributions in this tab.

- Keep all
**Statistical Distribution**parameters set to**None**. - Keep the default orientation
**Mean Values**as**Dip**=**65**,**Dip Direction**=**185.** - Set
**Slope Properties****Unit Weight MN/m**=^{3}**0.026**. - Set
**Slope Properties Height (m)**= 20. - Select the
**Slope Properties Length (m)**check box and enter**60**.

**NOTE:** For a Probabilistic analysis, it is usually a good idea to define **Slope Properties Length** to limit the size of wedges according to this dimension. If you leave the parameter undefined, depending on your joint orientation distributions, very large wedges can be generated parallel to the slope, which may give unrealistic or misleading analysis results.

### 3.2 UPPER FACE

Select the **Upper Face** tab. For this tutorial, we assume the orientation of the upper face is constant so we won't set any statistical distributions in this tab.

- Keep all
**Statistical Distribution**parameters set to**None**. - Keep the default orientation
**Mean Values**as**Dip**=**12**,**Dip Direction**=**185**). - Select the
**Bench Width**check box and set**Width (m)**=**15**.

**NOTE:** For a Probabilistic analysis, it is usually a good idea to define a **Bench Width** to limit the size of wedges according to this dimension. If you leave this parameter undefined, depending on your joint orientation distributions, very large wedges can be generated perpendicular to the slope, which may give unrealistic or misleading analysis results.

### 3.3 JOINT 1 ORIENTATION

Select the **Joint 1** tab.

Note that there are two methods of defining the variability of joint orientation (**Orientation Definition Method**):

**Dip/Dip Direction:** The Dip and Dip Direction are treated as independent random variables (i.e., you can define different statistical distributions for each one).

**Fisher Distribution:** Generates a symmetric, three-dimensional distribution of orientations around the mean plane orientation, so only a single standard deviation is required. In general, a Fisher Distribution is recommended for generating random joint plane orientations because it provides more predictable orientation distributions and lessens the chance of input data errors.

**NOTE:** For more help on the Orientation Definition Method, see Joint Orientation in an SWedge Probabilistic Analysis.

For this tutorial, we are using the **Fisher Distribution** option.

- Set
**Orientation Definition Method**=**Fisher Distribution**. - Set
**Mean Dip Value**=**45**,**Mean Dip Direction Value = 105**, and**Standard Deviation = 7**.

### 3.4 JOINT 2 ORIENTATION

- Select the
**Joint 2**tab. - Set
**Orientation Definition Method**=**Fisher Distribution**. - Set
**Mean Dip Value**=**70**,**Mean Dip Direction Value = 235**, and**Standard Deviation = 7**.

### 3.5 JOINT 1 STRENGTH

Select the **Strength 1** tab.

Note that there are two methods of defining the statistical variability of joint shear strength (**Random Variables**)::

**Parameters:** The individual strength criterion parameters (e.g., cohesion and friction angle) can each be assigned a statistical distribution.

**Strength:** The shear strength variability is defined with respect to the mean strength envelope. This method has the advantage of requiring only a single parameter (coefficient of variation) to define the shear strength variability.

**NOTE:** For more help on probabilistic joint shear strength options, see Joint Strength in an SWedge Probabilistic Analysis in SWedge.

- Set
**Random Variables**=**Strength**. - Set
**Statistical Distribution**=**Lognormal**. - Set
**Coefficient of Variation**=**0.25**, Cohesion (**c [MPa]**) =**0.02**, and**Phi [deg]**=**20**.

NOTES:

- The
**Coefficient of Variation**is defined as the Standard Deviation (of the shear strength) divided by the Mean (shear strength). - Only
**Lognormal**and**Gamma**distributions are allowed when defining shear strength as a random variable because they are defined only for positive values. This ensures that the randomly generated values of shear strength are always positive (negative shear strength has no physical meaning in**SWedge**).

### 3.6 JOINT 2 STRENGTH

- Select the
**Strength 2**tab. - Set
**Random Variables**=**Strength**. - Set
**Statistical Distribution**=**Lognormal**. - Set
**Coefficient of Variation**=**0.25**, Cohesion (**c [MPa]**) =**0**,**Phi [deg]**=**30**.

### 3.7 TENSION CRACK

- Select the
**Tension Crack**tab.

For this tutorial, we'll include a tension crack in the model and define the orientation as a random variable. - Select the
**Tension Crack Exists**check box. - Set
**Orientation Definition Method**=**Fisher Distribution**. - Set
**Mean Dip Value**=**70**,**Mean Dip Direction Value**=**165**, and**Standard Deviation**=**7**. - For
**Define****Tension Crack Location**, select**Use Bench Width to Maximize**.

**NOTE:** The **Use Bench Width to Maximize** option automatically locates the tension crack to create the maximum possible wedge size for the specified bench width. A tension crack is NOT included if it decreases the wedge size.

## 4.0 Compute

Click **OK** in the **Input Data** dialog to compute the **SWedge** Probabilistic analysis.

**SWedge** uses the Latin Hypercube sampling method to generate 10,000 random input data samples for each random variable, using the specified Statistical Distributions, and to compute the Factor Safety for 10,000 possible wedges. The calculation takes only a few seconds, and the progress of the calculation is indicated in the status bar.

**TIP:** You can also click the **Apply** button in the **Input Data** dialog to compute the analysis without closing the dialog. This allows you to easily test different input parameters and re-compute the results.

## 5.0 Probabilistic Analysis Results

This is the first result reported in the **Probabilistic Analysis** section of the **Wedge Information Pane**l in the application **Sidebar**.

For this example, if you entered the Input Data correctly, you should obtain a Probability of Failure (PF) of about 9% (PF = 0.0911).

### 5.1 WEDGE INFORMATION PANEL

A summary of analysis results is displayed in the **Wedge Information Panel** in the application **Sidebar**.

The primary result of interest from a Probabilistic analysis is the **Probability of Failure**, which is the first result shown in the **Probabilistic Analysis** section of the panel. If you entered the input data correctly, you should obtain a **Probability of Failure** of about 9% (0.0911).

Note that the **Probability of Failure** is equal to the **Number of Failed Wedges** (i.e., those with a Factor of Safety < 1) divided by the **Number of Samples** (as entered in the **Project Settings** dialog), i.e., 911 / 10000.

**NOTE:** For a discussion of the Probability of Failure, see Probability of Failure in SWedge Analysis Results.

### 5.2 WEDGE VIEW

The wedge displayed by default in the **Wedge View** after a Probabilistic analysis is based on the **Mean Values** entered in the **Probabilistic Input Data** dialog and is referred to as the Mean Wedge. The **Factor of Safety** of the Mean Wedge should be 1.3660, as shown in the **Wedge Information Panel**.

Note that the Tension Crack for the Mean Wedge is positioned to create the maximum wedge size for the given bench width (remember that the **Use Bench Width to Maximize** option is in effect).

To view the wedge with the minimum Factor of Safety generated by the Probabilistic analysis, right-click in the **Wedge View** and select **Show Min FS Wedge** from the popup menu. The analysis information now shown in the **Wedge Information Pane** is now for the wedge with the minimum Factor of Safety (should be 0.534).

To restore the Mean Wedge display and information, right-click in the **Wedge View** and select **Show Mean FS Wedge**.

### 5.3 HISTOGRAM VIEW

To plot histograms of results after a Probabilistic analysis, select **Plot Histogram** on the toolbar or on the **Statistics** menu to open the **Histogram Plot Parameters** dialog.

**Select:** Statistics > Plot Histogram

Click **OK** to plot a **Safety Factor** histogram.

The histogram displayed in the **Histogram View** represents the distribution of the Factor of Safety for all valid wedges generated by the random sampling based on data entered in the **Input Data** dialog. The red bars on the left of the distribution represent wedges with a Factor of Safety of less than 1.0.

To display the histogram bars in 3D, right-click in the histogram and select **3D Histogram** from the popup menu.

#### 5.3.1 Mean Factor of Safety

Notice the values displayed at the bottom of the histogram for the mean Factor of Safety, the standard deviation, and the minimum and maximum values.

The mean Factor of Safety from a Probabilistic analysis (i.e., the average of all the Factors of Safety generated by the analysis) is generally slightly different from the Factor of Safety of the Mean Wedge (i.e., the Factor of Safety of the wedge corresponding to the mean values entered in the **Input Data** dialog). For this tutorial, these values are:

- The mean Factor of Safety (from the histogram) = 1.424
- The Factor of Safety of the Mean Wedge (from the
**Wedge Information Panel**) = 1.366

Theoretically, for an infinite number of samples, these two values should be equal. However, due to the random nature of the statistical sampling, the two values are usually slightly different for a typical probabilistic analysis with a finite number of samples.

#### 5.3.2 Viewing Other Wedges in the Distribution

A useful feature of the **Histogram View** is the ability to view any wedge generated by a Probabilistic analysis corresponding to any point along the histogram.

Tile the **Wedge View** and the **Histogram View** so that both are visible.

**Select:** Window > Tile Vertically

Double-click at any point along the histogram to display the nearest corresponding wedge in the **Wedge View** along with corresponding results in the **Wedge Information Panel**. The selected wedge is referred to as the Picked Wedge.

When using this feature, all applicable views in addition to the **Wedge View** (e.g., the **Stereonet View**) are updated to reflect the data for the Picked Wedge. In addition, the feature can be used on any statistical data histogram generated by **SWedge** and also works with scatter plots.

Before continuing, right-click in the **Wedge View** and select **Show Mean FS Wedge** from the popup menu to re-set the display to the Mean Wedge.

#### 5.3.3 Histograms of Other Data

You can plot histograms of other statistical data, such as:

- Random output variables (e.g., wedge eight, normal stress on joint planes, driving force, etc.).
- Random input variables (i.e., any variable entered in
**Input Data**that is assigned a statistical distribution).

Re-open the **Histogram Plot Parameters **dialog to plot wedge weight.

**Select:** Statistics > Plot Histogram

- Set
**Data Type = Wedge Weight**. - Select the
**Best Fit Distribution**check box. - Click
**OK**.

In this case, the **Best Fit Distribution** is a Normal distribution and the corresponding parameters are displayed at the bottom. All features of the Safety Factor histogram described above also work for this and other data types.

Before continuing, close the **Wedge Weight** and **Safety Factor Histogram Views** by clicking the X in the upper right-hand corner of each view. Also right-click in the **Wedge View** and select **Show Mean FS Wedge** from the popup menu to re-set the display to the Mean Wedge

Now letâ€™s generate a histogram of an input random variable. Re-open the **Histogram Plot Parameters **dialog.

**Select:** Statistics > Plot Histogram

- Set
**Data Type**=**Dip of Joint 1**. - Click
**OK**.

**NOTE:** Input Distribution for input variables can be displayed on histograms. However, because the orientation of **Joint 1** was generated using a Fisher Distribution, which is three-dimensional, the Input Distribution cannot be displayed on the histogram because it is a two-dimensional plot of only one component (**Dip**) of the **Joint 1** orientation.

#### 5.3.4 Show Failed Wedges

Another feature of **SWedge** histogram plots is the **Show Failed Wedges** option. This option allows you to see the relationship between wedge failure and the distribution of any input or output variable. The option is selected by default and the distribution of failed wedges (i.e., wedges with a Factor of Safety < 1) is highlighted on the histogram.

In this case, there is not a strong correlation between wedge failure and the **Dip** angle of **Joint 1**. However, there appears to be some bias towards failure at higher dip angles, as might be expected.

### 5.4 SCATTER PLOTS

Scatter plots allow you to examine the relationship between any two analysis variables. To generate a scatter plot, open the **Scatter Plot Parameters** dialog.

** Select:** Statistics > Plot Scatter

- Select the variables you would like to plot on the X and Y axes (
**X Axis Dataset**and**Y Axis Dataset**). For example, let's plot**Effective Normal Stress**and**Shear Strength**. - Select the
**Show Regression Line**check box to display the best fit straight line through the data. - Click
**OK**to generate the plot.

From the failed wedge data, it can be readily seen that wedge failure corresponds to low values of normal stress and shear strength, as we would expect.

Since we used the Mohr-Coulomb strength criterion, the best fit linear regression line for the scatter plot corresponds (approximately) to the mean strength envelope. We can verify this from the parameters listed at the bottom of the plot:

- The
**Alpha**value (0.198003) represents the y-intercept of the linear regression line on the scatter plot. For**Joint 1**, recall that we defined the**Cohesion = 0.02 MPa**. For the Mohr-Coulomb criterion, cohesion is the y-intercept of the strength envelope. - The
**Beta**value (0.364425) represents the slope of the linear regression line. For the Mohr-Coulomb criterion, the slope of the strength envelope is equal to tan(phi). For**Joint 1**we defined**Phi = 20 degrees**and**Arctan(0.362) = 19.9**.

Also note the **Correlation Coefficient** listed at the bottom of the plot, which indicates the degree of correlation between the two variables plotted. The **Correlation Coefficient** can vary between -1 and 1, where numbers close to zero indicate a poor correlation, and numbers close to 1 or â€“1 indicate a good correlation. Note that a negative **Correlation Coefficient** simply means that the slope of the best fit linear regression line is negative.

The **Correlation Coefficient** is related to the **Coefficient of Variation**, which is defined for the shear strength of **Joint 1**. To demonstrate this:

- Re-open the
**Input Data**dialog and select the**Strength 1**tab. - Set
**Coefficient of Variation**= 0.1 and click**Apply**to re-compute the analysis.

Notice that the scatter of data around the mean strength envelope is much narrower and that the**Correlation Coefficient**has increased to 0.899. - Set
**Coefficient of Variation**=**0.01**and click**Apply**again.

Notice that the scatter of data is very narrow and that the**Correlation Coefficient**is 0.999. - To restore the original strength data, re-set
**Coefficient of Variation**=**0.25**and click**OK**.

### 5.5 STEREONET VIEW

The **Stereonet View** in **SWedge** displays a stereographic projection of the wedge planes (great circles) and corresponding poles. For a Probabilistic analysis, the Stereonet View can display the poles of all randomly generated plane orientations as well as the joint intersections. In addition, orientations corresponding to failed wedges can be highlighted. To display the **Stereonet View**:

** Select:** Analysis > Stereonet

- Right-click in the
**Stereonet View**and make sure that the**Show Planes**,**Show All Poles**,**Show Intersections**, and**Show Failed**options are all selected. Your screen should look like the following figure:

Notice the three sets of data (poles) corresponding to **Joint 1**, **Joint 2**, and the **Tension Crack** orientations. The set of data in the lower half of the plot represents the joint intersections, and the poles and intersections corresponding to failed wedges are highlighted in red.

### 5.6 COMPUTE WITH RANDOM SAMPLING

So far in this tutorial we have used the default **Pseudo-Random** sampling option, set as **Random Number Generation** in the **Random Number**s tab of the **Project Setting**s dialog (see Section 2.3 of the tutorial). Pseudo-Random sampling allows you to obtain reproducible results for a Probabilistic analysis by using the same seed value to generate random numbers. This is why you can obtain the exact values shown in this tutorial.

We will now demonstrate how different outcomes can result from a Probabilistic analysis by allowing a variable seed value to generate the random input data samples. Before continuing, tile all the open views by selecting **Tile** on the toolbar or on the **Windows** menu.

If you have followed the instructions in this tutorial, you should see four open views as shown in the following figure (**Stereonet View**, **Histogram View**, **Scatter Plot View** and **Wedge View**). You may need to decrease the font size of the plot titles and/or footers in order for them to fit in the reduced windows. Right-click in the plot window, select **Chart Properties** , then change font settings as needed.

**NOTE:** If your screen does not look similar to the above figure, close all other views and re-tile..

Now go back to the **Project Settings** dialog.

**Select:** Analysis > Project Settings

- Select the
**Random Numbers**tab. - Change the
**Random Number Generation**method from**Pseudo-Random**to**Random**.

The**Random**option will use a different seed value to generate random numbers each time you re-run the Probabilistic analysis. This will result in different samplings of your input random variables and different analysis results (e.g., Probability of Failure) each time you re-compute. - Select the
**Sampling**tab and decrease**Number of Samples**from**10000**to**1000**to make the change easier to see on the plots. - Click
**OK**. - Select
**Compute**on the toolbar or on the**Analysis**menu.

Notice that the plots and Probability of Failure are updated with the new results. - Select
**Compute**repeatedly and observe how the plots and the Probability of Failure are updated each time the analysis is re-run.

As you re-run the analysis, you should notice that the Probability of Failure varies between about 7% and 11%. Note also that**Wedge View**does not change when you re-compute since the default Mean Wedge is displayed and it is not affected by re-running the analysis.

### 5.7 SELECTING RANDOM WEDGES

We will again look at the ability to pick random wedges by double-clicking in either the **Histogram View** or the **Scatter Plot View** and noting the effect in the **Stereonet View**.

Double-click in either view repeatedly to observe the following:

- The
**Wedge Information Panel**displays results for the Picked Wedge (the wedge that corresponds to the data location at which you clicked the plot). - The
**Wedge View**is updated to display the Picked Wedge. - The great circles in the
**Stereonet View**are updated to display the planes representing the Picked Wedge.

### 5.8 FILTERING BY SLIDING MODE

We will now look at the ability in **SWedge** to filter wedges by sliding mode. This feature is available in both Probabilistic and Combinations analyses. To start, open the **Failure Mode Filter** dialog.

**Select:** Analysis > Failure Mode Filter

- Select the
**Apply failure mode filter**check box to activate the filter options. By default, all filters are selected. - De-select
**Allow sliding on both joint #1 and joint # 2**. - Click
**OK**to apply the filters.

In this example, applying the filter produces a **No Wedges are Formed** message.

This means that all of the wedges generated in this Probabilistic analysis fail by sliding along both Joint 1 and Joint 2. We can confirm this by exporting the results to Excel and looking at the failure modes for each wedge.

To export analysis results to Excel:

- Select
**Statistics > Export Dataset**. - In the
**Export Statistical Datasets**dialog, click the**Excel**button to open results in Excel. - Go to the
**Failure Mode**column (the last column) and note that, for each wedge, the Failure Mode is**Sliding on Joints 1 & 2**.

This concludes the tutorial.