Dataset for developing deep learning models to assess crack width and self-healing progress in concrete.

The presented dataset comes from an experimental study on the autogenous self-healing of high-strength concrete and the development of deep learning metasensor for crack width assessment and self-healing evaluation. 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 along grid lines were extracted. Then, reference measurements of the crack widths were obtained by an operator. The dataset comprises 19,098 records of brightness profiles, reference measurements, and benchmark measurements by deep learning and analytic models. The source images, 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 CNN models or analytic algorithms for assessing crack widths in concrete. The technical validation study explored three factors that affect crack measurement: the specimen position in relation to the scanner, the surface moisture level, and the operator performing manual measurements.