Modified block shape characterization method for classification of fractured rock: A Python-based GUI tool
The modified block shape classification tool can be use to classify any polyhedral shape formed by the intersection of discontinuities in the rock mass.
The repository was created by Dr. Jaspreet Singh, Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, India.
** MBSCT.exe software can be downloaded from the link https://drive.google.com/drive/folders/1HryaUSyV0tIwsuYAV8D1de8wcZ6OgAwB?usp=sharing Due to size constrains file was not uploaded on GitHub**. MBSCT.exe can directlty be used without by adding the data file to generate the classification plots.
MBSCT.py tool is made available in this repositry and can be used by following the steps below:
Step to Follow:
Step 1. Download the file with the name MBSCT.py and save it in your system drive.
Step 2. Download and install the latest python version.
Step 3. After installing the python, download the essential python libraries NumPy, Pandas, Tkinter, canvas and matplotlib using pip command.
Step 3. Open the command prompt and navigate to the directory where you saved the MBSCT.py file.
Step 4. Type ‘Python MBSCT.py’ and press enter. A Block shape classification tool will open on the window screen.
Step 5. Click on the browse data file button and select the excel file containing the data.
Step 6. Click on the execute button, wait till the code computes. The execution time depends on how bigger the data set is. The code will automatically save the resultant excel file containing block number, alpha, beta and volume information after execution in the current directory.
Step 7. After the code is executed, click on different plot buttons to generate the required plots.
This software is develped as a part of PhD research work of Dr. Jaspreet Singh. For more detail you can follow our publication "Modified block shape characterization method for classification of fractured rock: A python-based GUI tool" publised in Computers and Geosciences Journal. https://doi.org/10.1016/j.cageo.2022.105125.