Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing

This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.

Contents

• main.ipynb: Jupyter notebook with the main data analysis workflow.
• energy.py: Methods for the calculation of energy release rates.
• regression.py: Methods for the regression analyses.
• visualization.py: Methods for generating visualizations.
• df_mmft.pkl: Pickled DataFrame with experimental data gathered in the present work.
• df_legacy.pkl: Pickled DataFrame with literature data.

Prerequisites

• To run the scripts and notebooks, you need:
• Python 3.12 or higher
• Jupyter Notebook or JupyterLab
• Libraries: pandas, matplotlib, numpy, scipy, tqdm, uncertainties, weac

Setup

1. Download the zip file or clone this repository to your local machine.
2. Ensure that Python and Jupyter are installed.
3. Install required Python libraries using pip install -r requirements.txt.

Running the Analysis

1. Open the main.ipynb notebook in Jupyter Notebook or JupyterLab.
2. Execute the cells in sequence to reproduce the analysis.

Data Description

df_mmft.pkl

• exp_id: Unique identifier for each experiment.
• datestring: Date of the experiment as a string.
• datetime: Timestamp of the experiment.
• bunker: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.
• slope_incl: Inclination of the slope in degrees.
• h_sledge_top: Distance from sample top surface to the sled in mm.
• h_wl_top: Distance from sample top surface to weak layer in mm.
• h_wl_notch: Distance from the notch root to the weak layer in mm.
• rc_right: Critical cut length in mm, measured on the front side of the sample.
• rc_left: Critical cut length in mm, measured on the back side of the sample.
• rc: Mean of rc_right and rc_left.
• densities: List of density measurements in kg/m^3 for each distinct slab layer of each sample.
• densities_mean: Daily mean of densities.
• layers: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.
• layers_mean: Daily mean of layers.
• surface_lineload: Surface line load of added surface weights in N/mm.
• wl_thickness: Weak-layer thickness in mm.
• notes: Additional notes regarding the experiment or observations.
• L: Length of the slab–weak-layer assembly in mm.

df_legacy.pkl

• #: Record number.
• rc: Critical cut length in mm.
• slope_incl: Inclination of the slope in degrees.
• h: Slab height in mm.
• density: Mean slab density in kg/m^3.
• L: Lenght of the slab–weak-layer assembly in mm.
• collapse_height: Weak-layer height reduction through collapse.
• layers_mean: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.
• wl_thickness: Weak-layer thickness in mm.
• surface_lineload: Surface line load from added weights in N/mm.

For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.

License

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Citation