Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)

This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.

The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.

The deposited data includes:

• krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.
• krkCMd_images.zip: archive containing source image files in folders by test series: 
  -   stacked images of cracks in subsequent stages of self-healing (.tif files),
  -   zip archives assigned to image stacks and containing sets of ImageJ data files .roi,
  -   ImageJ .roi files specifying the locations of grid lines in the images.
• krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.

For details please see the data descriptor [1]. When referring to the data in publications please cite [1].

[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. Sci Data 12, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z

[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. Constr. Build. Mater. 422, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768