Quantification of cracks in concrete thin sections considering current methods of image analysis

• Published in: Journal of microscopy (Oxford) Vol. 286; no. 2; pp. 154 - 159
• Main Authors: Patzelt, Max, Erfurt, Doreen, Ludwig, Horst‐Michael
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.05.2022
• Subjects: Area; concrete; Cracks; degradation; Epoxy resins; Freeze-thawing; Image analysis; Image processing; Machine learning; Programming languages; transmitted light microscopy; Workflow; concrete; cracks; degradation; machine learning; image analysis; transmitted light microscopy
• ISSN: 0022-2720; 1365-2818
• DOI: 10.1111/jmi.13091

Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre‐processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze‐thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.