TY - GEN A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Ebell, Gino A1 - Ehrig, Karsten A1 - Heyn, Andreas A1 - Meinel, Dietmar A1 - Prohaska, Steffen T1 - Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben N2 - Kurzfassung. Durch die Alkalität des Betons wird Betonstahl dauerhaft vor Korrosion geschützt. Infolge von Chlorideintrag kann dieser Schutz nicht länger aufrechterhalten werden und führt zu Lochkorrosion. Die zerstörungsfreie Prüfung von Stahlbetonproben mit 3D-CT bietet die Möglichkeit, eine Probe mehrfach gezielt vorzuschädigen und den Korrosionsfortschritt zu untersuchen. Zur Quantifizierung des Schädigungsgrades müssen die bei dieser Untersuchung anfallenden großen Bilddaten mit Bildverarbeitungsmethoden ausgewertet werden. Ein wesentlicher Schritt dabei ist die Segmentierung der Bilddaten, bei der zwischen Korrosionsprodukt (Rost), Betonstahl (BSt), Beton, Rissen, Poren und Umgebung unterschieden werden muss. Diese Segmentierung bildet die Grundlage für statistische Untersuchungen des Schädigungsfortschritts. Hierbei sind die Änderung der BSt-Geometrie, die Zunahme von Korrosionsprodukten und deren Veränderung über die Zeit sowie ihrer räumlichen Verteilung in der Probe von Interesse. Aufgrund der Größe der CT-Bilddaten ist eine manuelle Segmentierung nicht durchführbar, so dass automatische Verfahren unabdingbar sind. Dabei ist insbesondere die Segmentierung der Korrosionsprodukte in den Bilddaten ein schwieriges Problem. Allein aufgrund der Grauwerte ist eine Zuordnung nahezu unmöglich, denn die Grauwerte von Beton und Korrosionsprodukt unterscheiden sich kaum. Eine formbasierte Suche ist nicht offensichtlich, da die Korrosionsprodukte in Beton diffuse Formen haben. Allerdings lässt sich Vorwissen über die Ausbreitung der Korrosionsprodukte nutzen. Sie bilden sich in räumlicher Nähe des BSt (in Bereichen vorheriger Volumenabnahme des BSt), entlang von Rissen sowie in Porenräumen, die direkt am BSt und in dessen Nahbereich liegen. Davon ausgehend wird vor der Korrosionsprodukterkennung zunächst eine BSt-Volumen-, Riss- und Porenerkennung durchgeführt. Dieser in der Arbeit näher beschriebene Schritt erlaubt es, halbautomatisch Startpunkte (Seed Points) für die Korrosionsprodukterkennung zu finden. Weiterhin werden verschiedene in der Bildverarbeitung bekannte Algorithmen auf ihre Eignung untersucht werden. T3 - ZIB-Report - 14-24 KW - Beton KW - Korrosionserkennung KW - Bildverarbeitung KW - Computertomografie KW - concrete KW - corrosiondetection KW - image processing KW - computed tomography Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-50912 SN - 1438-0064 ER - TY - CHAP A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Prohaska, Steffen A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Ebell, Gino T1 - 3D Corrosion Detection in Time-dependent CT Images of Concrete T2 - DIR-2015 Proceedings N2 - In civil engineering, the corrosion of steel reinforcements in structural elements of concrete bares a risk of stability-reduction, mainly caused by the exposure to chlorides. 3D computed tomography (CT) reveals the inner structure of concrete and allows one to investigate the corrosion with non-destructive testing methods. To carry out such investigations, specimens with a large artificial crack and an embedded steel rebar have been manufactured. 3D CT images of those specimens were acquired in the original state. Subsequently three cycles of electrochemical pre-damaging together with CT imaging were applied. These time series have been evaluated by means of image processing algorithms to segment and quantify the corrosion products. Visualization of the results supports the understanding of how corrosion propagates into cracks and pores. Furthermore, pitting of structural elements can be seen without dismantling. In this work, several image processing and visualization techniques are presented that have turned out to be particularly effective for the visualization and segmentation of corrosion products. Their combination to a workflow for corrosion analysis is the main contribution of this work. Y1 - 2015 UR - http://www.ndt.net/events/DIR2015/app/content/Paper/36_Paetsch.pdf ER - TY - JOUR A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Prohaska, Steffen A1 - Ehrig, Karsten A1 - Ebell, Gino A1 - Meinel, Dietmar A1 - Heyn, Andreas T1 - Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben JF - DGZfP-Jahrestagung 2014 Konferenzband Y1 - 2014 ER - TY - CHAP A1 - Ehrig, Karsten A1 - Goebbels, Jürgen A1 - Meinel, Dietmar A1 - Paetsch, Olaf A1 - Prohaska, Steffen A1 - Zobel, Valentin T1 - Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography T2 - International Symposium on Digital Industrial Radiology and Computed Tomography Y1 - 2011 ER - TY - CHAP A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Prohaska, Steffen T1 - Vergleich automatischer 3D-Risserkennungsmethoden für die quantitative Analyse der Schadensentwicklung in Betonproben mit Computer-Tomographie T2 - Tagungsband der DACH Jahrestagung 2012 Y1 - 2012 ER - TY - CHAP A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Breßler, David A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Prohaska, Steffen T1 - 3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben T2 - Tagungsband der DGZfP Jahrestagung 2013 Y1 - 2013 ER - TY - CHAP A1 - Paetsch, Olaf A1 - Baum, Daniel A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Prohaska, Steffen T1 - Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography T2 - Proceedings of Conference on Industrial Computed Tomography Y1 - 2012 SP - 321 EP - 330 ER - TY - CHAP A1 - Meinel, Dietmar A1 - Ehrig, Karsten A1 - Lhostis, Valrie A1 - Muzeau, Benoist A1 - Paetsch, Olaf T1 - Examination of Damage Processes in Concrete with CT T2 - Proceedings of Conference on Industrial Computed Tomography Y1 - 2014 ER - TY - JOUR A1 - Titschack, Jürgen A1 - Baum, Daniel A1 - Matsuyama, Kei A1 - Boos, Karin A1 - Färber, Claudia A1 - Kahl, Wolf-Achim A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Soriano, Carmen A1 - Stock, Stuart R. T1 - Ambient occlusion – a powerful algorithm to segment shell and skeletal intrapores in computed tomography data JF - Computers and Geosciences N2 - During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance- map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources. Y1 - 2018 U6 - https://doi.org/10.1016/j.cageo.2018.03.007 VL - 115 SP - 75 EP - 87 ER - TY - GEN A1 - Titschack, Jürgen A1 - Baum, Daniel A1 - Matsuyama, Kei A1 - Boos, Karin A1 - Färber, Claudia A1 - Kahl, Wolf-Achim A1 - Ehrig, Karsten A1 - Meinel, Dietmar A1 - Soriano, Carmen A1 - Stock, Stuart R. T1 - Ambient occlusion – a powerful algorithm to segment shell and skeletal intrapores in computed tomography data N2 - During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance- map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources. T3 - ZIB-Report - 18-14 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-67982 SN - 1438-0064 ER - TY - JOUR A1 - Jung, Christian A1 - Nowacka, Anna A1 - Barisin, Tin A1 - Meinel, Dietmar A1 - Paetsch, Olaf A1 - Grzesiak, Szymon A1 - Salamon, Michael A1 - Schladitz, Katja A1 - Redenbach, Claudia A1 - Pahn, Matthias T1 - 3d imaging and analysis of cracks in loaded concrete samples. 12th Conference on Industrial Computed Tomography (iCT) 2023, 27 February - 2 March 2023 in Fürth, Germany JF - e-Journal of Nondestructive Testing N2 - Concrete plays a central role as the standard building material in civil engineering. Experimental characterization of the concrete microstructure and a description of failure mechanisms are important to understand the concrete’s mechanical properties. Computed tomography is a powerful source of information as it yields 3d images of concrete specimens. However, complete visual inspection is often infeasible due to very large image sizes. Hence, automatic methods for crack detection and segmentation are needed. A region-growing algorithm and a 3d U-Net showed promising results in a previous study. Cracks in normal concrete and high-performance concrete that were initiated via tensile tests were investigated. Here, the methods are validated on a more diverse set of concrete types and crack characteristics. Adequate adaptions of the methods are necessary to deal with the complex crack structures. The segmentation results are assessed qualitatively and compared to those of a template matching algorithm which is well-established in industry. Y1 - 2023 U6 - https://doi.org/10.58286/27721 VL - 28 IS - 3 ER -