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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.
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.
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
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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.
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.
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.
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.
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.
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.