Publication Date:
2023-02-21
Description:
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 Auditorium2019-03
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: Bauwesen2018-09
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 Algorithmen2016-04
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 processing2015-08
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: Bauwesen2015-03
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: Computertomographie2014-03
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: Computertomographie2013-05
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 Algorithms2012-12
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: Bauwesen2012-05
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: Poster2011-11
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