Micro-Computed Tomography (µCT) systems are used for examining the internal structures of various objects, such as material samples, manufactured parts, and natural objects. Resolving fine details or performing accurate geometric measurements in the voxel data critically depends on the precise calibration of the µCT systems geometry. This paper presents a calibration method for µCT systems using projections of a calibration phantom, where the coordinates of the phantom are initially unknown. The approach involves detecting and tracking steel ball bearings and adjusting the unknown system geometry parameters using non-linear least squares optimization. Multiple geometric models are tested to verify their suitability for a self-calibration approach. The implementation is tested using a calibration phantom captured at different magnifications. The results demonstrate the system’s capability to determine the geometry model parameters with a remaining error on the detector between 0.27 px and 0.18 px. Systematic errors that remain after calibration, as well as changing parameters due to system instabilities, are investigated. The source code of this work is published to enable further research.

Carbon-reinforced concrete (CRC) is increasingly utilized in construction, due to its unique properties, such as corrosion resistance, high-tensile strength, and durability. Understanding its behavior under different loads is crucial to ensuring its safe and effective use in various construction applications. In this study, three-point bending tests were performed in combination with large-scale in situ computed tomography (CT). This paper presents the related three- and four-dimensional evaluation methods, with emphasis on crack width and quality control. The focus was on large CRC beams, with cross-sectional sizes of up to 80 mm by 160 mm. Such dimensions require extremely high energy during a CT scan. Therefore, a new experimental setup with energies of up to 8 MeV was used in this study. However, such high energies posed new challenges to the analysis methods. Therefore, two methods (digital volume correlation and grayscale profile analysis) for accurate crack width estimation were adapted and applied to the 3D reconstructions. In addition, a photogrammetric stereo image sequence was acquired and analyzed, using digital image correlation to cross-validate the results derived from the 3D crack width estimates. The 3D CT images also played a key role in the quality control measures, including the localization of the carbon-reinforcement and the assessment of porosity within the concrete structure.

The highly irregular crack pattern of reinforced concrete has been studied primarily at the surface. The ability to extend image correlation into the interior of structures by using X-ray computed tomography (CT) opens up new possibilities for analyzing the internal mechanics of concrete. In order to enable a complete material characterization, it is necessary to study the crack geometry at the micro level in 3D images over time, i.e., 4D data. This paper presents the results of in situ CT tests that were carried out on carbon-reinforced concrete (CRC) beams subjected to bending load. The main objective of the tests was the experimental analysis of the evolution of individual cracks at different stages of their formation by applying digital volume correlation (DVC) to the 4D image data from the computed tomography. The results obtained from the CT were compared with other measurement techniques, such as distributed fiber optic sensing, clip gauges, and digital image correlation (DIC).

This study investigates the performance of eight different deep learning architectures for voxel segmentation in volume images. The motivation is to segment carbon in carbon reinforced concrete (CRC) in micro-tomography (μ-CT) data. Although there are many 3D convolutional neural networks (CNNs) available, it is not yet clear which one works best for these specific tasks. In this study, the authors compare the following networks: DenseVoxNet, HighResNet, Med3D, Residual 3D U-Net, 3D SkipDenseSeg, 3D U-Net, V-Net, and LV-Net. To provide a more general recommendation for selecting a neural network, three medical datasets were added in addition to the three CRC datasets to facilitate the selection of an appropriate network based on the dataset characteristics. The experiments emphasize the importance of the initial random state, used for example to initialize the network weights. On average, the 3D U-Net is the best generalizing CNN, followed by the Residual 3D U-Net and the 3D SkipDenseSeg. While the 3D U-Net is a good architecture to start with, the experiments show that it does not perform best on all domains. To achieve optimal results, the authors propose recommendations for selecting a 3D neural network based on the dataset attributes.

This study focuses on the development of novel evaluation methods for the analysis of thin carbon reinforced concrete (CRC) structures. CRC allows for the exploration of slender components and innovative construction techniques due to its high tensile strength. In this contribution, the authors have extended the analysis of CRC shells from existing research. The internal structure of CRC specimens was explored using microtomography. The rovings within the samples were segmented from the three-dimensional tomographic reconstructions using a 3D convolutional neural network with enhanced 3D data augmentation strategies and further analyzed using image-based techniques. The main contribution is the evaluation of the manufacturing precision and the simulation of the structural behavior by measuring the carbon grid positions inside the concrete. From the segmentations, surface point clouds were generated and then integrated into a multiscale framework using a parameterized representative volume element that captures the characteristic properties of the textile reinforcement. The procedure is presented using an example covering all necessary design steps from computed tomography to multiscale analysis. The framework is able to effectively evaluate novel construction methods and analyze the linear-elastic behavior of CRC shells.