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Description: This report presents a fast data assimilation method to produce an interpolating time and space temperature distribution for steel members subject to fire testing. The method assimilates collected temperature data into the numerical integration of the heat equation. This physically based method also allows the computation of lateral and axial heat flux into and inside the member.

Description: In civil engineering, the corrosion of steel reinforcements in structural elements of concrete bares a risk of stability-reduction, mainly caused by the exposure to chlorides. 3D computed tomography (CT) reveals the inner structure of concrete and allows one to investigate the corrosion with non-destructive testing methods. To carry out such investigations, specimens with a large artificial crack and an embedded steel rebar have been manufactured. 3D CT images of those specimens were acquired in the original state. Subsequently three cycles of electrochemical pre-damaging together with CT imaging were applied. These time series have been evaluated by means of image processing algorithms to segment and quantify the corrosion products. Visualization of the results supports the understanding of how corrosion propagates into cracks and pores. Furthermore, pitting of structural elements can be seen without dismantling. In this work, several image processing and visualization techniques are presented that have turned out to be particularly effective for the visualization and segmentation of corrosion products. Their combination to a workflow for corrosion analysis is the main contribution of this work.

Description: Monocrystaline Ni-base superalloys are the material of choice for first row blades in jet engine gas turbines. Using a novel visualization tool for 3D reconstruction and visualization of dislocation line segments from stereo-pairs of scanning transmission electron microscopies, the superdislocation substructures in Ni-base superalloy LEK 94 (crept to ε = 26%) are characterized. Probable scenarios are discussed, how these dislocation substructures form.

Description: A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo-pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo-pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (〉 300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo-pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3 % and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed.

Description: A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (〉300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3% and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed.

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.