Rock Mass AI

Strayos features a powerful Rock Mass AI module, where it will internally calculate and cluster groupings of Geological features within a rock face profile to show Dip and Strike directions and angles. To begin, navigate to the site you are looking to use Rock Mass AI on. Select the Rock Mass AI module, where it will then bring you to a 3D view of your model. 

From here, select "Add Structure". Users will need to select the area which they are looking to run the rock mass. This should take several minutes to process once an area is selected. 

Once, processed,  Stereonets and Sterograms can be view for overall Rock Mass results. The AI also clusters the results, classifying the "facets" and "seams" into grouping based off their Dip and Strike orientation. Within the clustering there are also DXF and CSV that can be downloaded as well. 

Lastly, users can edit the AI results by using the "Edit Rock Mass" button on the upper tool bar. This allows users to merge seams and facets, delete detected features or draw new ones as needed.

To further read upon the algorithms that produce this data, see below: 

1. At the first stage, Strayos Rock Mass AI module will detect the crack polygons on the surface of the bench, it is not able to distinguish between joints and other random cracks.

2. A the second stage, we designed a clusterization algorithm to cluster those cracks in several groups. It basically fits the cracks on planes that have the same orientation. If a majority of the cracks can be fitted to such planes, they will be clustered into the same group. This algorithm currently has two limitations, The first one is that it requires the majority of the detected cracks to be formed by the joints. 2^nd is that all the cracks can be easily fit to the bench surface direction.

For example, in image 1 below, we can clearly see three different joint groups. However since all the cracks are on the bench surface plane, it means that all the cracks can be fitted into this group(image 2). Therefore, we assign less weight to such group, which means if there are other good groups that can be fitted with the majority of the cracks, even if they have fewer cracks, they will have a higher possibility in the joints group in the final results(image 3). But as I mentioned, this still relies on that the majority of the cracks are formed by joints. In the images from the above analysis, you can easily observe such groups on the bench surface.

Our approach is based on predicting crack-polygons on the terrain's surface and fitting planes through that polygons on the terrain.

Our planes describe the inner structures like crack/bedding planes

For example “Cluster 4” (image below) summarizes planes which points to a big sloped crack. It is not a sort of outer surface structure. It is a plane surface that describes the inner structure.

Here are steps of calculations:

•        Predict joints on the 3D textures.

•        Map predicted 2D-polygons to 3D-model and obtain 3D-polygons

•        Fits 3D-polygons by planes

•        Found Strike and Dip angle for each predicted plane.

◦     "Strike" is the azimuth of an imagined line which is obtained as an intersection-fitted plane and a horizontal plane.

◦     "Dip angle" is the angle of inclination measured downward from horizontal.

◦     Sometimes it is useful to operate with "Dip direction", which is a projection of downward to horizontal plane and equal to Strike + 90[deg].

•        These data related to lines and are described on the stereo net by red-points as the plane's poles.

◦     Example of how to put a pole into stereo net: https://www.youtube.com/watch?v=8JOzmIBVjkk

•        Then data is clustered and obtained clusters are approximated. Approximated clusters in stereomap named as planes.

If you point the camera view from the top to the reconstructed model and use the button “Align view with North” it will match the vertical axis with the North Slope of the crack-plane described by dip-angle 57 [deg]