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

  • 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, theImage 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.show moreshow less

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Metadaten
Document Type:Article
Author: Max PatzeltORCiD, Doreen Erfurt, Prof. Dr.-Ing. Horst-Michael LudwigORCiDGND
DOI (Cite-Link):https://doi.org/10.1111/jmi.13091Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220811-46754Cite-Link
URL:https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13091
Parent Title (English):Journal of Microscopy
Language:English
Date of Publication (online):2022/07/22
Date of first Publication:2022/02/12
Release Date:2022/08/11
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Professur Werkstoffe des Bauens
Volume:2022
Issue:Volume 286, Issue 2
Pagenumber:6
First Page:154
Last Page:159
Tag:concrete; crack; degradation; transmitted light microscopy
GND Keyword:Beton; Rissbildung; Bildanalyse; Maschinelles Lernen; Mikroskopie
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:56 Bauwesen / 56.45 Baustoffkunde
Licence (German):License Logo Creative Commons 4.0 - Namensnennung-Nicht kommerziell-Keine Bearbeitung (CC BY-NC-ND 4.0)