Christian Jung, Claudia Redenbach, Katja Schladitz

• Sustainability is an important topic in the field of materials science and civil engineering. In particular, concrete, as a building material, needs to be of high quality to ensure its durability. Damage and failure processes such as cracks in concrete can be evaluated non-destructively by micro-computed tomography. Cracks can be detected in the images, for example via edge-detection filters or machine learning models. To study the goodness, robustness, and generalizability of these methods, annotated 3d image data are of fundamental importance. However, data acquisition and, in particular, its annotation is often tedious and error-prone. To overcome data shortage, realistic data can be synthesized. The data set described in this article addresses the lack of freely available annotated 3d images of cracked concrete. To this end, seven concrete samples without cracks were scanned via micro-computed tomography. Realizations of a dedicated stochastic geometry model are discretized to binary images and morphologically transformed to mimic real crack structures. These are superimposed on the concrete images and simultaneously yield the label images that distinguish crack from non-crack regions. The data set contains 1 344 of such image pairs and includes a large variety of crack structures. The data set may be used for training machine learning models and for objectively testing crack segmentation methods.