Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization
Cracks in concrete structures are an integral part of this heterogeneous material. Characteristics of cracks induced by standardized tests yield valuable information about the tested concrete formulation and its mechanical properties. Observing cracks on the surface of the concrete structure leaves...
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| Format: | Article |
| Language: | English |
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Pouyan Press
2025-04-01
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| Series: | Journal of Soft Computing in Civil Engineering |
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| Online Access: | https://www.jsoftcivil.com/article_199726_dae9514bcc90cf041060314fbff65fb4.pdf |
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| author | Anna Nowacka Katja Schladitz Szymon Grzesiak Matthias Pahn |
| author_facet | Anna Nowacka Katja Schladitz Szymon Grzesiak Matthias Pahn |
| author_sort | Anna Nowacka |
| collection | DOAJ |
| description | Cracks in concrete structures are an integral part of this heterogeneous material. Characteristics of cracks induced by standardized tests yield valuable information about the tested concrete formulation and its mechanical properties. Observing cracks on the surface of the concrete structure leaves a wealth of structural information unused, since a crack in concrete is rarely a planar structure but rather spatial. Computed tomography enables looking into the sample without interfering with or destroying the microstructure. The reconstructed tomographic images are 3d images, consisting of voxels whose gray values represent local X-ray absorption. In order to identify voxels belonging to the crack, so to segment the crack structure in the images, appropriate algorithms need to be developed. Convolutional neural networks are known to solve this type of task very well given enough and consistent training data. However, fiber reinforced concrete has not been widely analyzed in the literature, since it is hard to obtain segmented cracks excluding the fibers. We overcome this problem by adapting a 3d version of the well-known U-Net and training it on semi-synthetic 3d images of real concrete samples equipped with simulated cracks. We thus lay the foundations for large experimental quantitative studies of 3d crack initiation and development in various types of concrete. |
| format | Article |
| id | doaj-art-dc7dc2bd492a43ee8b1d3a4077dde477 |
| institution | Kabale University |
| issn | 2588-2872 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-dc7dc2bd492a43ee8b1d3a4077dde4772025-08-20T03:34:42ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722025-04-0192568310.22115/scce.2024.414873.1712199726Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based CharacterizationAnna Nowacka0Katja Schladitz1Szymon Grzesiak2Matthias Pahn3M.Sc., Department of Image Processing, Fraunhofer ITWM, Kaiserslautern, GermanyDr., Department of Image Processing, Fraunhofer ITWM, Kaiserslautern, GermanyM.Sc., Department of Civil Engineering, RPTU, Kaiserslautern, GermanyProf. Dr.-Ing., Department of Civil Engineering, RPTU, Kaiserslautern, Germany, MatthiasCracks in concrete structures are an integral part of this heterogeneous material. Characteristics of cracks induced by standardized tests yield valuable information about the tested concrete formulation and its mechanical properties. Observing cracks on the surface of the concrete structure leaves a wealth of structural information unused, since a crack in concrete is rarely a planar structure but rather spatial. Computed tomography enables looking into the sample without interfering with or destroying the microstructure. The reconstructed tomographic images are 3d images, consisting of voxels whose gray values represent local X-ray absorption. In order to identify voxels belonging to the crack, so to segment the crack structure in the images, appropriate algorithms need to be developed. Convolutional neural networks are known to solve this type of task very well given enough and consistent training data. However, fiber reinforced concrete has not been widely analyzed in the literature, since it is hard to obtain segmented cracks excluding the fibers. We overcome this problem by adapting a 3d version of the well-known U-Net and training it on semi-synthetic 3d images of real concrete samples equipped with simulated cracks. We thus lay the foundations for large experimental quantitative studies of 3d crack initiation and development in various types of concrete.https://www.jsoftcivil.com/article_199726_dae9514bcc90cf041060314fbff65fb4.pdf3d computed tomography3d semantic segmentationdeep learningsupervised learningmulti-scale methodsstructural concrete |
| spellingShingle | Anna Nowacka Katja Schladitz Szymon Grzesiak Matthias Pahn Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization Journal of Soft Computing in Civil Engineering 3d computed tomography 3d semantic segmentation deep learning supervised learning multi-scale methods structural concrete |
| title | Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization |
| title_full | Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization |
| title_fullStr | Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization |
| title_full_unstemmed | Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization |
| title_short | Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization |
| title_sort | segmentation of spatial crack structures in concrete by deep learning enabling image based characterization |
| topic | 3d computed tomography 3d semantic segmentation deep learning supervised learning multi-scale methods structural concrete |
| url | https://www.jsoftcivil.com/article_199726_dae9514bcc90cf041060314fbff65fb4.pdf |
| work_keys_str_mv | AT annanowacka segmentationofspatialcrackstructuresinconcretebydeeplearningenablingimagebasedcharacterization AT katjaschladitz segmentationofspatialcrackstructuresinconcretebydeeplearningenablingimagebasedcharacterization AT szymongrzesiak segmentationofspatialcrackstructuresinconcretebydeeplearningenablingimagebasedcharacterization AT matthiaspahn segmentationofspatialcrackstructuresinconcretebydeeplearningenablingimagebasedcharacterization |