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 - GEN A1 - Paetsch, Olaf A1 - Stalling, Detlev A1 - Korzen, Manfred T1 - Online-Visualisierung von Brandversuchen an Bauteilen Y1 - 1997 PB - Konrad-Zuse-Zentrum für Informationstechnik, Berlin (ZIB) / Bundesanstalt für Materialfoschung und -prüfung (BAM) Berlin ER - TY - CHAP A1 - Korzen, Manfred A1 - Schriever, Robert A1 - Ziener, Kai-Uwe A1 - Paetsch, Olaf A1 - Zumbusch, Gerd ED - Ziebs, J. al. T1 - Real-Time 3-D Visualization of Surface Temperature Fields Measured by Thermocouples on Steel Strutures in Fire Engineering T2 - Proc. Int. Symposium Local Strain and Temperature Measurements in Non-Uniform Fields at Elevated Temperatures Y1 - 1996 SP - 253 EP - 262 PB - Woodhead CY - England 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 - CHAP A1 - Agudo Jácome, Leonardo A1 - Hege, Hans-Christian A1 - Paetsch, Olaf A1 - Pöthkow, Kai T1 - 3D Reconstruction, Visualization and Quantification of Dislocations from Transmission Electron Microscopy Stereo-Pairs T2 - Microscopy and Microanalysis 2016, July 24-28 Columbus, Ohio Y1 - 2016 UR - https://www.microscopy.org/MandM/2016/program/abstracts/PDP-57.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 - Jácome, Leonardo Agudo A1 - Eggeler, Gunter A1 - Pöthkow, Kai A1 - Paetsch, Olaf A1 - Hege, Hans-Christian T1 - Three-Dimensional Characterization of Superdislocation Interactions in the High Temperature and Low Stress Creep Regime of Ni-Base Superalloy Single Crystals T2 - Proceedings of CREEP 2015 – 13th International Conference on Creep and Fracture of Engineering Materials and Structures, May 31 – June 4, 2015, Toulouse, France N2 - Monocrystaline Ni-base superalloys are the material of choice for first row blades in jet engine gas turbines. Using a novel visualization tool for 3D reconstruction and visualization of dislocation line segments from stereo-pairs of scanning transmission electron microscopies, the superdislocation substructures in Ni-base superalloy LEK 94 (crept to ε = 26%) are characterized. Probable scenarios are discussed, how these dislocation substructures form. Y1 - 2015 SP - 16 EP - 17 ER - TY - CHAP A1 - Streicher, Doreen A1 - Paetsch, Olaf A1 - Seiler, Robert A1 - Prohaska, Steffen A1 - Krause, Martin A1 - Boller, Christian T1 - 3-D-Visualisierung von Radar- und Ultraschallecho-Daten mit ZIBAmira T2 - Proc. DGZfP-Jahrestagung 2011 Y1 - 2011 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 - CHAP A1 - Paetsch, Olaf T1 - Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen T2 - iCT 2019 N2 - To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too. Y1 - 2019 ER - TY - JOUR A1 - Agudo Jácome, Leonardo A1 - Hege, Hans-Christian A1 - Paetsch, Olaf A1 - Pöthkow, Kai T1 - Three-dimensional reconstruction and quantification of dislocation substructures from transmission electron microscopy stereo pairs JF - Ultramicroscopy N2 - A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (>300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3% and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed. Y1 - 2018 U6 - https://doi.org/10.1016/j.ultramic.2018.08.015 VL - 195 SP - 157 EP - 170 ER - TY - CHAP A1 - Paetsch, Olaf T1 - Möglichkeiten und Grenzen automatischer Merkmalserkennung am Beispiel von Risserkennungen in 3D-CT-Aufnahmen von Betonproben N2 - Die Auswertungen der großen Datenmengen moderner bildgebender Verfahren der ZfP können manuell kaum noch bewältigt werden. Hochauflösende 3D-CT-Aufnahmen bestehen oft aus über 1000 Schichtbildern mit einer Datenmenge von mehreren Gigabytes. Aktuelle Computer können diese zwar problemlos visualisieren und erlauben somit eine visuelle Inspektion, aber die möglichst vollständige Erkennung bestimmter Merkmale in den Daten und deren qualitative wie quantitative Auswertung ist durch Experten manuell nicht mehr zu bewältigen. Das gilt insbesondere im Kontext der Schadensaufklärung für die quantitative Analyse verschiedenartig induzierter Risse in Betonen (z.B. durch mechanische Belastungen sowie Frost, Sulfat und Alkali-Kieselsäure-Reaktion). Eine dazu notwendige Segmentierung und Merkmalserkennung kann nur automatisch durchgeführt werden. Dabei ergibt sich (auch fast automatisch) die Frage nach der Verlässlichkeit der verwendeten Algorithmen. Inwieweit kann man davon ausgehen, dass alle gesuchten Merkmale auch tatsächlich gefunden worden sind? Sind die gefundenen Merkmale quantitativ auswertbar und wie wirken sich Parameteränderungen auf die Ergebnisse aus? Sollten immer dieselben, einmal mit gutem Ergebnis angewandten, Parameter auch bei anderen Proben zur besseren Vergleichbarkeit genutzt werden? Anhand eines Risserkennungsalgorithmus basierend auf Formerkennung und Bildverarbeitung wird die Problematik diskutiert. Als Grundlage zur Bewertung des Ansatzes dienen 3D-CT-Aufnahmen von geschädigten Betonprobekörpern und Datensätze, in denen sich aufgrund ihres homogenen Aufbaus mit einfachen Mitteln Risse sicher und eindeutig erkennen lassen. Zur Auswertung der erkannten Risse gehört auch deren Einbettung in das umliegende Material. Das erfordert neben einer automatischen Risserkennung auch eine Segmentierung des gesamten Probekörpers in Zementsteinmatrix, Gesteinskörnung und Porenraum. Da eine solche Segmentierung aufgrund der Datenmenge nur schwer manuell erfolgen kann, werden erste Ergebnisse aus einer Segmentierung mit Hilfe selbstlernender Convolutional Neural Networks gezeigt. Y1 - 2018 PB - iCT 2019 CY - https://www.ndt.net/article/ctc2019/papers/iCT2019_Full_paper_29.pdf ER - TY - GEN A1 - Agudo Jácome, Leonardo A1 - Hege, Hans-Christian A1 - Paetsch, Olaf A1 - Pöthkow, Kai T1 - Three-Dimensional Reconstruction and Quantification of Dislocation Substructures from Transmission Electron Microscopy Stereo-Pairs N2 - A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo-pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo-pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (> 300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo-pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3 % and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed. T3 - ZIB-Report - 18-50 KW - dislocation, diffraction contrast, scanning transmission electron microscopy, stereoscopy, visualization Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-70339 SN - 1438-0064 ER - TY - CHAP A1 - Paetsch, Olaf T1 - Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen T2 - iCT 2019 N2 - To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too. [1.2MB | id=23664 ] iCT 2019 Session: Short talks Thu 13:50 Auditorium 2019-03 Möglichkeiten und Grenzen automatischer Merkmalserkennung am Beispiel von Risserkennungen in 3D-CT-Aufnahmen von Betonproben O. Paetsch11 Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany Abstract [1MB | id=23104 ] DE DGZfP 2018 Session: Bauwesen 2018-09 Quantitative Rissanalyse im Fahrbahndeckenbeton mit der 3D-Computertomographie D. Meinel125, K. Ehrig128, F. Weise16, O. Paetsch211 1Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany concrete, ROI tomography, in-situ-CT, 3D-CT, Beton, AKR, Feuchtetransport, automatic crack detection Abstract [0.7MB | id=18980 ] DE DGZfP 2015 Session: CT Algorithmen 2016-04 3D Corrosion Detection in Time-dependent CT Images of Concrete O. Paetsch111, D. Baum15, S. Prohaska17, K. Ehrig228, D. Meinel225, G. Ebell24 1Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany CT, multi-angle radiography, defect detection, Feature Extraction, image processing, concrete, corrosion Abstract [0.5MB | id=18043 ] DIR 2015 Session: Quantitative imaging and image processing 2015-08 Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben O. Paetsch111, D. Baum15, G. Ebell24, K. Ehrig228, A. Heyn2, D. Meinel225, S. Prohaska17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Computertomographie [0.4MB | id=17375 ] DE DGZfP 2014 Session: Bauwesen 2015-03 Examination of Damage Processes in Concrete with CT D. Meinel125, K. Ehrig128, V. L’Hostis2, B. Muzeau2, O. Paetsch311 1BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Laboratoire d’Etude du Comportement des Bétons et des Argiles; Commissariat Energie Atomique (CEA)287, Gif-Sur-Yvette, France 3Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany X-ray computed tomography, concrete, corrosion, crack detection, 3D visualization Abstract [4.9MB | id=15692 ] iCT 2014 Session: Non-destructive Testing and 3D Materials Characterisation of... 2014-06 3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben O. Paetsch111, D. Baum15, D. Breßler1, K. Ehrig228, D. Meinel225, S. Prohaska1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Radiographic Testing (RT), statistical analysis, 3D Computed Tomography, visualization, concrete structural damage, automated crack detection [1MB | id=15343 ] DE DGZfP 2013 Session: Computertomographie 2014-03 Vergleich automatischer 3D-Risserkennungsmethoden für die quantitative Analyse der Schadensentwicklung in Betonproben mit Computertomographie O. Paetsch111, K. Ehrig228, D. Meinel225, D. Baum15, S. Prohaska1,1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Radiographic Testing (RT), visualization, crack detection, Visualisierung, computer tomography, template matching, Hessian eigenvalues, ZIBAmira, automated crack detection, percolation [0.9MB | id=14269 ] DE DGZfP 2012 Session: Computertomographie 2013-05 Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography O. Paetsch111, D. Baum15, K. Ehrig228, D. Meinel225, S. Prohaska1,1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany computed tomography, template matching, Hessian eigenvalues, crack statistics, visualization, crack surface, ZIBAmira [0.6MB | id=13736 ] iCT 2012 Session: Poster - Analysis and Algorithms 2012-12 3-D-Visualisierung von Radar- und Ultraschallecho-Daten mit ZIBAmira D. Streicher112, O. Paetsch211, R. Seiler2, S. Prohaska27, M. Krause360 [Profile of Krause] , C. Boller178 1Saarland University74, Saarbrücken, Germany 2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 3BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany [0.4MB | id=12284 ] DE DGZfP 2011 Session: Bauwesen 2012-05 Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography K. Ehrig128, J. Goebbels153, D. Meinel125, O. Paetsch211, S. Prohaska27, V. Zobel2 1Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany [0.7MB | id=11150 ] DIR 2011 Session: Poster 2011-11 Actual Cooperations 10th International Workshop NDT in Progress 2019 2019 Oct 7-9 11th International Symposium on NDT in Aerospace 2019 2019 Nov 13-15 3rd Singapore International NDT Conference & Exhibition, SINCE 2019 2019 Dec 4-5 10th Conference on Industrial Computed Tomography (iCT) 2020 2020 Feb 4-7 34th European Conference on Acoustic Emission Testing (EWGAE 2020) 2020 Sep 9-11 Contribute Papers and Proceedings to NDT.net Share... Home Exhibition Archive Forum Jobs Members Events Directory NDT A-Z Advertise Privacy Policy Contact About © NDT.net - Where expertise comes together. The Largest Open Access Portal of Nondestructive Testing (NDT)- since 1996 Y1 - 2019 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 -