Parametric_Rockmass_Studies

Repository for the code to do parametric studies on the structure of rockmass.

Methods

Samples of discrete discontinuity networks are created with the visual programming language "Grasshopper" in the computer aided design software Rhino 3D (https://www.rhino3d.com/). "Virtual measurements" are then taken on these samples and used for further processing and investigation of rock mass parameters.

Publications

A first paper about this study was published in the journal Rock Mechanics and Rock Engineering:

• Erharter, G.H. Rock Mass Structure Characterization Considering Finite and Folded Discontinuities: A Parametric Study. Rock Mech Rock Eng (2024). https://doi.org/10.1007/s00603-024-03787-9

Repository structure

Parametric_Rockmass_Studies
├── dataset
│   ├── boxcounts.zip                     - zipped folder containing boxcounts as individual txt files to compute the fractal dimension of every sample
│   ├── PDD1.xlsx                         - excel file that contains sample identifiers, input parameters for sample creation, virtually measured parameters and computed parameters for every sample of the dataset
├── src
│   ├── 00_Cluster.ghcluster              - grasshopper (Rhino) cluster that is used in main grasshopper script
│   ├── 00_main.gh                        - main grasshopper script that genarates parametric rock mass samples
│   ├── A_compiler.py                     - Script that compiles the recorded data from samples of the discrete discontinuity networks and creates one excel file for further processing.
│   ├── B_analyzer.py                     - Script that processes the compiled records of the discrete discontinuity dataset, computes new parameters and creates figures to visualize the dataset.
│   ├── C_boxcounting.py                  - Script that performs voxel based boxcounting that is used to estimate each samples' fractal dimension.
│   ├── D_function_vis.py                 - Script that generates specific plots for publications.
│   ├── X_library.py                      - Script that contains a custom library with different classes of functions for math, plotting or general use (utilities).
├── .gitignore
├── LICENSE                               - file with specifications of the applied MIT License
├── environment.yaml                      - dependency file to use with conda
├── README.md

Requirements

The environment is set up using conda.

To do this create an environment called Jv using environment.yaml with the help of conda. If you get pip errors, install pip libraries manually, e.g. pip install pandas

conda env create --file environment.yaml

Activate the new environment with:

conda activate Jv

Contact

georg.erharter@ngi.no