{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:04:25Z","timestamp":1770984265773,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["432767097"],"award-info":[{"award-number":["432767097"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open-Access Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>Cyclic freezing and thawing (FT) are a primary cause of cracking in concrete, yet current assessment procedures in Germany rely heavily on qualitative estimation using the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM) capillary suction, internal damage and freeze-thaw (CIF) and Capillary de-icing freeze-thaw (CDF) tests. Although these standard tests provide a general overview of the condition of concrete damage in specimens through the estimation of water saturation through capillary suction, mass of surface delamination, qualitative open surface damage, and relative dynamic modulus of elasticity, they do not take quantitative analysis of voids, including cracks and air pores, directly into account. To address this, we propose a novel workflow utilizing deep learning-based semantic segmentation with Fourier-based slice-to-volume registration by combining 2D light microscopy (LM) of petrographic sections and 3D micro-computed tomography (\u03bcCT). We segment cracks, air pores, and aggregates in both modalities and employ feature matching alongside spatial harmonics analysis for 3D shape description. The best proposed 3D registration framework through feature matching demonstrated a success rate of 89.75%, achieving a dissimilarity of 5.21% in relative root mean square error (RRMSE) terms and thereby significantly surpassing the performance of compared 2D-only methods adapted from the body of research. Our approach enables precise, automated, and verifiable quantification of voids across CT and LM modalities and paves the way for advanced computational modeling-based methods to investigate moisture transfer mechanisms for more accurate assessments of frost damage in concrete, service life prediction models, deep learning applications for multimodal data fusion, and more comprehensive FT damage simulations.<\/jats:p>","DOI":"10.3390\/ma19040663","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T16:13:32Z","timestamp":1770653612000},"page":"663","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fourier-Based Non-Rigid Slice-to-Volume Registration of Segmented Petrographic LM and CT Scans of Concrete Specimens"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8098-6022","authenticated-orcid":false,"given":"Mohamed Said Helmy","family":"Alabassy","sequence":"first","affiliation":[{"name":"F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin Christian","family":"Hampe","sequence":"additional","affiliation":[{"name":"F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doreen","family":"Erfurt","sequence":"additional","affiliation":[{"name":"F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8998-4936","authenticated-orcid":false,"given":"Horst-Michael","family":"Ludwig","sequence":"additional","affiliation":[{"name":"F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-0671","authenticated-orcid":false,"given":"Andrea","family":"Osburg","sequence":"additional","affiliation":[{"name":"F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108095","DOI":"10.1016\/j.istruc.2024.108095","article-title":"Lifetime prediction of damaged or cracked concrete structures: A review","volume":"71","author":"Li","year":"2025","journal-title":"Structures"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22278","DOI":"10.1038\/s41598-023-48211-4","article-title":"On effects of freezing and thawing cycles of concrete containing nano-Formula: See text: Experimental study of material properties and crack simulation","volume":"13","author":"Seyedpour","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_3","unstructured":"Zhang, H. (2011). 5-Concrete. Building Materials in Civil Engineering, Woodhead Publishing. Woodhead Publishing Series in Civil and Structural Engineering."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1006\/jcis.1998.5986","article-title":"Mercury Intrusion Porosimetry and Image Analysis of Cement-Based Materials","volume":"211","author":"Abell","year":"1999","journal-title":"J. Colloid Interface Sci."},{"key":"ref_5","unstructured":"ACI Committee 224 (2001). 224R-01: Control of Cracking in Concrete Structures (Reapproved 2008). Technical Documents, American Concrete Institute."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/S0008-8846(01)00671-8","article-title":"How to study drying shrinkage microcracking in cement-based materials using optical and scanning electron microscopy?","volume":"32","author":"Bisschop","year":"2002","journal-title":"Cem. Concr. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/jmi.13091","article-title":"Quantification of cracks in concrete thin sections considering current methods of image analysis","volume":"286","author":"Patzelt","year":"2022","journal-title":"J. Microsc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1002\/cepa.2913","article-title":"Verkn\u00fcpfung & Automatisierung computergest\u00fctzter Methoden zur quantitativen Rissanalyse von Beton","volume":"6","author":"Patzelt","year":"2023","journal-title":"ce\/papers"},{"key":"ref_9","unstructured":"Grattan-Bellew, P.E., and Danay, A. (1992, January 28). Comparison of laboratory and field evaluation of alkali- silica reaction in large dams. Proceedings of the International Conference on Concrete Alkali-Aggregate Reactions in Hydroelectric Plants and Dams, Frederiction, NB, Canada."},{"key":"ref_10","unstructured":"Dunbar, P.A., and Grattan-Bellew, P.E. (1995). Results of damage rating evaluation of condition of concrete from a number of structures affected by AAR. Proceedings of the CANMET\/ACI International Workshop on Alkali-Aggregate Reactions in Concrete, Dartmouth, NS, Canada, 1\u20134 October 1995, Natural Resources Canada."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106456","DOI":"10.1016\/j.cemconres.2021.106456","article-title":"Microscopic assessment of ASR-affected concrete under confinement conditions","volume":"145","author":"Zahedi","year":"2021","journal-title":"Cem. Concr. Res."},{"key":"ref_12","unstructured":"Villeneuve, V., Fournier, B., and Duchesne, J. (2012, January 20\u201325). Determination of the damage in concrete affected by ASR\u2013the damage rating index (DRI). Proceedings of the 14th International Conference on Alkali-Aggregate Reaction (ICAAR), Austin, TX, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cemconres.2016.12.001","article-title":"Overall assessment of Alkali-Aggregate Reaction (AAR) in concretes presenting different strengths and incorporating a wide range of reactive aggregate types and natures","volume":"93","author":"Sanchez","year":"2017","journal-title":"Cem. Concr. Res."},{"key":"ref_14","unstructured":"Martin, R.P., Sanchez, L., Fournier, B., and Toutlemonde, F.c. (2016, January 3\u20137). Diagnosis of AAR and DEF: Comparison of residual expansion, stiffness damage test and damage rating index. Proceedings of the ICAAR 2016\u201415th International Conference on Alkali Aggregate Reaction, Sao Paulo, Brazil."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sanchez, L., Drimalas, T., and Fournier, B. (2020). Assessing condition of concrete affected by internal swelling reactions (ISR) through the Damage Rating Index (DRI). Cement, 1\u20132, 100001.","DOI":"10.1016\/j.cement.2020.100001"},{"key":"ref_16","unstructured":"(2006). Pr\u00fcfung des Frost-Tauwiderstandes von Beton\u2014Innere Gef\u00fcgest\u00f6rung; Deutsche Fassung CEN\/TR_15177:2006 (Standard No. DIN-Fachbericht CEN\/TR 15177:2006-06)."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/BF02485951","article-title":"CDF test\u2014Test method for the freeze-thaw resistance of concrete-tests with sodium chloride solution (CDF)","volume":"29","author":"Setzer","year":"1996","journal-title":"Mater. Struct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/BF02480521","article-title":"Test methods of frost resistance of concrete: CIF-Test: Capillary suction, internal damage and freeze thaw test\u2014Reference method and alternative methods A and B","volume":"37","author":"Setzer","year":"2004","journal-title":"Mater. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gong, F., Zhi, D., Jia, J., Wang, Z., Ning, Y., Zhang, B., and Ueda, T. (2022). Data-Based Statistical Analysis of Laboratory Experiments on Concrete Frost Damage and Its Implications on Service Life Prediction. Materials, 15.","DOI":"10.3390\/ma15186282"},{"key":"ref_20","first-page":"253","article-title":"Evaluating concrete quality under freeze\u2013thaw damage using the drilling powder method","volume":"14","author":"Rath","year":"2025","journal-title":"J. Sustain. Cem.-Based Mater."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.conbuildmat.2015.10.194","article-title":"Freeze\u2013thaw durability of high strength concrete under deicer salt exposure","volume":"102","author":"Liu","year":"2016","journal-title":"Constr. Build. Mater."},{"key":"ref_22","unstructured":"Auberg, R. (2002). Application of CIF-Test in practise for reliable evaluation of frost resistance of concrete. Proceedings of the International RILEM Workshop on Frost Resistance of Concrete, Essen, Germany, 18\u201319 April 2002, RILEM Publications SARL."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.cemconres.2010.11.007","article-title":"Experimental study of the material and bond properties of frost-damaged concrete","volume":"41","author":"Hanjari","year":"2011","journal-title":"Cem. Concr. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1016\/j.conbuildmat.2018.08.029","article-title":"A review of the impact of micro- and nanoparticles on freeze-thaw durability of hardened concrete: Mechanism perspective","volume":"186","author":"Ebrahimi","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_25","unstructured":"Tange Hasholt, M., Fridh, K., and Hooton, R.D. (2016). Freeze-Thaw-Attack on concrete structures -laboratory tests, monitoring, practical experience. Proceedings of the International RILEM Conference on Materials, Systems and Structures in Civil Engineering Conference segment on Frost Action in Concrete, Lyngby, Denmark, 22\u201323 August 2016, Technical University of Denmark."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Franke, J., and Sch\u00f6bel, A. (2024). Cracks in Concrete. Statistical Machine Learning for Engineering with Applications, Springer Nature.","DOI":"10.1007\/978-3-031-66253-9"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s11831-016-9194-z","article-title":"Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review","volume":"24","author":"Zakeri","year":"2017","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.aej.2017.01.020","article-title":"Crack detection using image processing: A critical review and analysis","volume":"57","author":"Mohan","year":"2018","journal-title":"Alex. Eng. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2286","DOI":"10.1002\/stc.2286","article-title":"Pixel-level crack delineation in images with convolutional feature fusion","volume":"26","author":"Ni","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sohn, K., Villegas, R., Pan, G., and Lee, H. (2015). Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction. Proceedings to the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7\u201312 June 2015, IEEE.","DOI":"10.1109\/CVPR.2015.7298621"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2075","DOI":"10.1002\/stc.2075","article-title":"Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images","volume":"25","author":"Xu","year":"2018","journal-title":"Struct. Control Health Monit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fan, R., Bocus, M.J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., and Liu, M. (2019). Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding. Proceedings to the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9\u201312 June 2019, IEEE.","DOI":"10.1109\/IVS.2019.8814000"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Benz, C., Debus, P., Ha, H.K., and Rodehorst, V. (2019). Crack Segmentation on UAS-based Imagery using Transfer Learning. Proceedings to the 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), Dunedin, New Zealand, 2\u20134 December 2019, IEEE.","DOI":"10.1109\/IVCNZ48456.2019.8960998"},{"key":"ref_36","unstructured":"Iglovikov, V., and Shvets, A. (2018). TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1016\/j.autcon.2020.103291","article-title":"Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning","volume":"118","author":"Kang","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. Proceedings to the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7\u201313 December 2015, IEEE.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TIP.2014.2378052","article-title":"Tubularity flow field\u2013a technique for automatic neuron segmentation","volume":"24","author":"Mukherjee","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108747","DOI":"10.1016\/j.patcog.2022.108747","article-title":"Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images","volume":"129","author":"Barisin","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic Road Crack Detection Using Random Structured Forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/mice.12564","article-title":"Automatic detection method of cracks from concrete surface imagery using two\u2013step light gradient boosting machine","volume":"36","author":"Chun","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","article-title":"Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification","volume":"33","author":"Kaynig","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107","DOI":"10.5566\/ias.1964","article-title":"Structure Detection with Second Order Riesz Transforms","volume":"38","author":"Dobrovolskij","year":"2019","journal-title":"Image Anal. Stereol."},{"key":"ref_47","unstructured":"Willmann, A., and Kobbelt, L. (2025). Unsupervised Semantic Segmentation of Cracks in Concrete Specimens. 36th Forum Bauinformatik 2025, RWTH Aachen University."},{"key":"ref_48","unstructured":"Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., and Freeman, W.T. (2022). Unsupervised Semantic Segmentation by Distilling Feature Correspondences. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kim, C., Han, W., Ju, D., and Hwang, S.J. (2024). EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR52733.2024.00338"},{"key":"ref_50","unstructured":"Patzelt, M., and Hampe, M. (2024). \u00b5CT-Scans of a Selection of Concrete Samples Made of Cement and Coarse Aggregates Without Sand Fraction; Dataset Version 1.0, 2024, F. A. Finger Institut f\u00fcr Baustoffkunde, Bauhaus-Universit\u00e4t Weimar."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2018.2878966","article-title":"DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection","volume":"28","author":"Zou","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/978-3-031-73480-9_22","article-title":"Rigid Single-Slice-in-Volume Registration via Rotation-Equivariant 2D\/3D Feature Matching","volume":"Volume 15249","author":"Modat","year":"2024","journal-title":"Biomedical Image Registration"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cag.2021.04.037","article-title":"InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification","volume":"98","author":"Agus","year":"2021","journal-title":"Comput. Graph."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Onajite, E. (2014). Understanding Sample Data. Seismic Data Analysis Techniques in Hydrocarbon Exploration, Elsevier.","DOI":"10.1016\/B978-0-12-420023-4.00008-3"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108257","DOI":"10.1016\/j.aim.2022.108257","article-title":"The parametric approach to the Willmore flow","volume":"400","author":"Palmurella","year":"2022","journal-title":"Adv. Math."},{"key":"ref_56","first-page":"232","article-title":"Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks","volume":"Volume 10433","author":"Descoteaux","year":"2017","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1109\/TMI.2024.3385024","article-title":"Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging","volume":"43","author":"Ghoul","year":"2024","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_58","unstructured":"Song, J., Su, F., Chen, J., Tai, C., and Cai, S. (2000). Line net global vectorization: An algorithm and its performance evaluation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR 2000, Hilton Head, SC, USA, 13\u201315 June 2000, IEEE."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, Z., Yang, Q., Barratt, D.C., Clarkson, M.J., Vercauteren, T., and Hu, Y. (2024). Nonrigid Reconstruction of Freehand Ultrasound without a Tracker. arXiv.","DOI":"10.1007\/978-3-031-72083-3_64"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6520620","DOI":"10.1155\/2019\/6520620","article-title":"Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique","volume":"2019","author":"Li","year":"2019","journal-title":"Adv. Civ. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sason, I., and Verdu, S. (2017). Arimoto-R\u00e9nyi conditional entropy and Bayesian hypothesis testing. 2017 IEEE International Symposium on Information Theory, IEEE.","DOI":"10.1109\/ISIT.2017.8007073"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Laga, H. (2018). A survey on nonrigid 3D shape analysis. Academic Press Library in Signal Processing, Academic Press.","DOI":"10.1016\/B978-0-12-811889-4.00007-5"},{"key":"ref_63","unstructured":"(2019). Pr\u00fcfung von Festbeton\u2014Teil 2: Herstellung und Lagerung von Probek\u00f6rpern f\u00fcr Festigkeitspr\u00fcfungen; Deutsche Fassung EN 12390-2:2019 (Standard No. DIN EN 12390-2:2019-10)."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. Graphics Gems IV, ACM Digital Library.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1007\/s11633-023-1466-0","article-title":"Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis","volume":"20","author":"Zhang","year":"2023","journal-title":"Mach. Intell. Res."},{"key":"ref_66","unstructured":"CVAT.ai Corporation (2024). Computer Vision Annotation Tool (CVAT); Software Release Version 2.54.0, CVAT.ai Corporation."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment Anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"LabelMe: A Database and Web-Based Tool for Image Annotation","volume":"77","author":"Russell","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ni, Z.L., Bian, G.B., Zhou, X.H., Hou, Z.G., Xie, X.L., Wang, C., Zhou, Y.J., Li, R.Q., and Li, Z. (2019). RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments. arXiv.","DOI":"10.1007\/978-3-030-36711-4_13"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"100624","DOI":"10.1016\/j.array.2025.100624","article-title":"Under- and over-segmentation: New metrics for image segmentation accuracy measurement","volume":"28","author":"Mohammadi","year":"2025","journal-title":"Array"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/s41592-025-02595-5","article-title":"Cellpose3: One-click image restoration for improved cellular segmentation","volume":"22","author":"Stringer","year":"2025","journal-title":"Nat. Methods"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Attias, H. (1999). A variational Bayesian framework for graphical models. NIPS\u201999, Proceedings of the 13th International Conference on Neural Information Processing Systems, Denver, CO, USA, 29 November\u20134 December 1999, MIT Press.","DOI":"10.1049\/cp:19991096"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1214\/06-BA104","article-title":"Variational inference for Dirichlet process mixtures","volume":"1","author":"Blei","year":"2006","journal-title":"Bayesian Anal."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"109919","DOI":"10.1016\/j.matdes.2021.109919","article-title":"Meso-structure segmentation of concrete CT image based on mask and regional convolution neural network","volume":"208","author":"Tian","year":"2021","journal-title":"Mater. Des."},{"key":"ref_76","unstructured":"Mujawar, T. (2023). Optimizing CT Scans\u2019 Instance Segmentation of Air Pockets in Asphalt Mix Samples via Deep Neural Network, Ruhr-Universit\u00e4t Bochum."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"119185","DOI":"10.1016\/j.conbuildmat.2020.119185","article-title":"Microstructural crack segmentation of three-dimensional concrete images based on deep convolutional neural networks","volume":"253","author":"Dong","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"126717","DOI":"10.1016\/j.conbuildmat.2022.126717","article-title":"Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images","volume":"324","author":"Tao","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"111730","DOI":"10.1016\/j.compositesb.2024.111730","article-title":"3D crack recognition in Engineered Cementitious Composites (ECC) based on super-resolution reconstruction and semantic segmentation of X-ray Computed Microtomography","volume":"285","author":"Hao","year":"2024","journal-title":"Compos. Part B Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"104602","DOI":"10.1016\/j.autcon.2022.104602","article-title":"Scanning electron microscopy (SEM) image segmentation for microstructure analysis of concrete using U-net convolutional neural network","volume":"144","author":"Bangaru","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1145\/62065.62074","article-title":"Parallel thinning with two-subiteration algorithms","volume":"32","author":"Guo","year":"1989","journal-title":"Commun. ACM"},{"key":"ref_82","unstructured":"Silversmith, W., Bae, J.A., Li, P.H., and Wilson, A.M. (2026, January 28). Kimimaro: Skeletonize Densely Labeled 3D Image Segmentations. Available online: https:\/\/zenodo.org\/records\/5539913."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/2945.856997","article-title":"Tissue classification based on 3D local intensity structures for volume rendering","volume":"6","author":"Sato","year":"2000","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e4312","DOI":"10.7717\/peerj.4312","article-title":"A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton","volume":"6","author":"Blanch","year":"2018","journal-title":"PeerJ"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Driscoll, M.K., McCann, C., Kopace, R., Homan, T., Fourkas, J.T., Parent, C., and Losert, W. (2012). Cell shape dynamics: From waves to migration. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002392"},{"key":"ref_86","unstructured":"Vranic, D., and Saupe, D. (2001). 3D Shape Descriptor Based on 3D Fourier Transform. Proceedings of ECMCS\u20142001, the 3rd EURASIP Conference on Digital Signal Processing for Multimedia Communications and Services, Palermo, Italy, 11\u201313 September 2001, Scientific Assoc. of Infocommunications."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Heczko, M., Keim, D., Saupe, D., and Vrani\u0107, D.V. (2001). Verfahren zur \u00c4hnlichkeitssuche auf 3D-Objekten, Springer.","DOI":"10.1007\/978-3-642-56687-5_28"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Graichen, U., Eichardt, R., Fiedler, P., Strohmeier, D., Zanow, F., and Haueisen, J. (2015). SPHARA\u2014A generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: Application to EEG. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121741"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Pesce, V., Colagrossi, A., and Silvestrini, S. (2023). Chapter Nine\u2014Navigation. Modern Spacecraft Guidance, Navigation, and Control, Elsevier.","DOI":"10.1016\/B978-0-323-90916-7.00009-3"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.21105\/joss.01296","article-title":"PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images","volume":"4","author":"Gostick","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1617\/14142","article-title":"RILEM TC 176: Internal damage of concrete due to frost action","volume":"37","author":"Setzer","year":"2004","journal-title":"Mater. Struct."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"9935","DOI":"10.1038\/s41467-024-53993-w","article-title":"Automated estimation of cementitious sorptivity via computer vision","volume":"15","author":"Kabir","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e2021RG000744","DOI":"10.1029\/2021RG000744","article-title":"From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers","volume":"60","author":"Viswanathan","year":"2022","journal-title":"Rev. Geophys."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"056701","DOI":"10.1103\/PhysRevE.86.056701","article-title":"Heterogeneities of flow in stochastically generated porous media","volume":"86","author":"Hyman","year":"2012","journal-title":"Phys. Review. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"063014","DOI":"10.1103\/PhysRevE.88.063014","article-title":"Hyperbolic regions in flows through three-dimensional pore structures","volume":"88","author":"Hyman","year":"2013","journal-title":"Phys. Review. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"A1871","DOI":"10.1137\/130942541","article-title":"Conforming Delaunay Triangulation of Stochastically Generated Three Dimensional Discrete Fracture Networks: A Feature Rejection Algorithm for Meshing Strategy","volume":"36","author":"Hyman","year":"2014","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1007\/s10596-015-9493-8","article-title":"Statistical scaling of geometric characteristics in stochastically generated pore microstructures","volume":"19","author":"Hyman","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e2021GL094400","DOI":"10.1029\/2021GL094400","article-title":"Scale\u2013Bridging in Three\u2013Dimensional Fracture Networks: Characterizing the Effects of Variable Fracture Apertures on Network\u2013Scale Flow Channelization","volume":"48","author":"Hyman","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cageo.2015.08.001","article-title":"dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport","volume":"84","author":"Hyman","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"105665","DOI":"10.1016\/j.cageo.2024.105665","article-title":"pySimFrac: A Python library for synthetic fracture generation and analysis","volume":"191","author":"Guiltinan","year":"2024","journal-title":"Comput. Geosci."}],"container-title":["Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1944\/19\/4\/663\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:58:44Z","timestamp":1770980324000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1944\/19\/4\/663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":100,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["ma19040663"],"URL":"https:\/\/doi.org\/10.3390\/ma19040663","relation":{},"ISSN":["1996-1944"],"issn-type":[{"value":"1996-1944","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}