Development of Advanced Tomography Data Analysis Techniques

In project D03, the focus is on the development of extended methods for tomography data analysis. The approach is to investigate the components of the specimens by segmenting them subvoxel-precisely and predict the distribution of the contained carbon fibers.

After the development of appropriate algorithms, the samples will be deformed by the application of force and then scanned again in the tomograph. The goal then is to determine the relationship between structure and stability. The multitemporal data sets produced in this process are matched using 3D-LSM (least squares matching) in order to investigate not only the displacement vectors (caused by the temporal difference) but also the deformations and cracks with subvoxel accuracy. With the aim to further optimize the data quality and thus the data analysis, a sensor modeling and corresponding calibration strategies are developed, which largely eliminate the systematic errors of tomographs.

the picture shows an example of a 2D section through a component containing carbon fibres reconstructed from tomography data — Example of a 2D section through a component containing carbon fibres reconstructed from tomography data

Entwicklung von erweiterten Methoden für die Tomographiedatenanalyse

Scientists

Project Manager

Hans-Gerd Maas

Prof. Dr. sc. techn. habil.

Technische Universität Dresden

Institute of Photogrammetry and Remote Sensing
D-01062 Dresden (Germany)

Phone: +49 351 463 32859

hans-gerd.maas@tu-dresden.de

Institute website

Research Associate

Franz Wagner

M.Sc.

Technische Universität Dresden

Institute of Photogrammetry and Remote Sensing
D-01062 Dresden (Germany)

Phone: +49 351 463 39041

franz.wagner@tu-dresden.de

Institute website

Cooperations

A03

Fiber Reinforced Plant Organs

C01

Internal Shells

C03

Multi-Scale Shell Models

D01

3D Printing of CRC

Tomography

Publikationen | Publications

Giese, J.; Herbers, M.; Liebold, F.; Wagner, F.; Grzesiak, S.; de Sousa, C.; Pahn, M.; Maas, H.-G.; Marx, S.; Curbach, M.; Beckmann, B. (2023) Investigation of the Crack Behavior of CRC Using 4D Computed Tomography, Photogrammetry, and Fiber Optic Sensing in Buildings 13, issue 10, 2595 – DOI: https://doi.org/10.3390/buildings13102595

Liebold, F.; Lorenzoni, R.; Curosu, I.; Léonard, F.; Mechtcherine, V.; Paciornik, S.; Maas, H.-G. (2021) 3D Least Squares Matching Applied to Micro-Tomography Data in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 43, issue B2, p. 533–539 – DOI: 10.5194/isprs-archives-XLIII-B2-2021-533-2021

Liebold, F.; Maas, H.-G. (2022) 3D-Deformationsanalyse und Rissdetektion in multitemporalen Voxeldaten von Röntgentomographen in: Kersten, T.; Tilly, N. [eds.] Proc. der 42. Wissenschaftlich-Technischen Jahrestagung der DGPF, Band 30, 05./06.10.2022 in Dresden, p. 105–116 – DOI: 10.24407/KXP:1796026123

Liebold, F.; Wagner, F.; Giese, J.; Grzesiak, S.; de Sousa, C.; Beckmann, B.; Pahn, M.; Marx, S.; Curbach, M.; Maas, H.-G. (2023) Damage Analysis and Quality Control of Carbon-Reinforced Concrete Beams Based on In Situ Computed Tomography Tests in Buildings 13, issue 10, 2669 – DOI: https://doi.org/10.3390/buildings13102669

Mester, L.; Klempt, V.; Wagner, F.; Scheerer, S.; Klarmann, S.; Vakaliuk, I.; Curbach, M.; Maas, H.-G.; Löhnert, S.; Klinkel, S. (2023) A Comparison of Multiscale Methods for the Modelling of Carbon-Reinforced Concrete Structures in: Ilki, A.; Çavunt, D.; Çavunt, Y. S. [eds.] Building for the Future: Durable, Sustainable, Resilient – Proc. of fib Symposium 2023, 05.–07.06.2023 in Istanbul (Turkey), publ. in: Lecture Notes in Civil Engineering 350, Cham: Springer, p. 1418–1427 – DOI: 10.1007/978-3-031-32511-3_145

Mester, L.; Wagner, F.; Liebold, F.; Klarmann, S.; Maas, H.-G.; Klinkel, S. (2022) Image-based modelling of carbon-fibre reinforced concrete shell structures in: Stokkeland, S.; Braarud, H. C. [eds.] Concrete Innovation for Sustainability – Proc. for the 6th fib International Congress 2022, 12.–16.06.2022 in Oslo (Norway), Oslo: Novus Press, p. 1631–1640.

Wagner, F.; Eltner, A.; Maas, H.-G. (2023) River water segmentation in surveillance camera images: A comparative study of offline and online augmentation using 32 CNNs in: International Journal of Applied Earth Observation and Geoinformation 119, 103305 – DOI: 10.1016/j.jag.2023.103305

Wagner, F.; Maas, H.-G. (2023) A Comparative Study of Deep Architectures for Voxel Segmentation in Volume Images in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023, p. 1667–1676 – https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1667-2023

Wagner, F.; Mester, L.; Klinkel, S.; Maas, H.-G. (2023) Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning in: Buildings 13, issue 9, 2399 – DOI: 10.3390/buildings13092399

Datensätze | Data sets

Blanch, X.; Wagner, F.; Eltner, A. (2023) River Water Segmentation Dataset (RIWA) at: Kaggle – DOI: 10.34740/KAGGLE/DSV/4901781

Wagner, F. (2023) Carbon Rovings Segmentation Dataset (RIWA) at: Kaggle – DOI: 10.34740/KAGGLE/DS/2920892

Wagner, F. (2023) Concrete Pores Segmentation Dataset (RIWA) at: Kaggle – DOI: 10.34740/KAGGLE/DS/2921245

Wagner, F. (2023) Fiber Segmentation Dataset (RIWA) at: Kaggle – DOI: 10.34740/KAGGLE/DS/2894881