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Realistic uncertainty description incorporating aleatoric and epistemic uncertainties can be described within the framework of polymorphic uncertainty, which is computationally demanding. Utilizing a domain decomposition approach for random field based uncertainty models the proposed level-based sampling method can reduce these computational costs significantly and shows good agreement with a standard sampling technique. While 2-level configurations tend to get unstable with decreasing sampling density 3-level setups show encouraging results for the investigated reliability analysis of a structural unit square.
Bolted connections are commonly used in steel construction. The load-bearing behavior of bolt fittings has extensively been studied in various research activities and the bearing capacity of bolted connections can be assessed well by standard regulations for practical applications. With regard to tensile loading, the nut does not have strong influence on resistances, since the failure occurs in the bolts due to higher material strengths of the nuts. In some applications, so-called “blind holes” are used to connect plated components. In a manner of speaking, the nut is replaced by the “outer” plate with a prefabricated hole and thread, in which the bolt can be screwed and tightened. In such connections, the limit load capacity cannot solely be assessed by the bolt resistance, since the threaded hole in the base material has strong influence on the structural behavior. In this context, the available screw-in depth of the blind hole is of fundamental importance. The German National Annex of EN 1993-1-8 provides information on a necessary depth in order to transfer the full tensile capacity of the bolt. However, some connections do not allow to fabricate such depths. In these cases, the capacity of the connection is unclear and not specified. In this paper, first experiments on corresponding connections with different screw-in depths are presented and compared to limit load capacities according to the standard.
Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
This dataset presents the numerical analysis of the heat and moisture transport through a facade equipped with a living wall system designated for greywater treatment. While such greening systems provide many environmental benefits, they involve pumping large quantities of water onto the wall assembly, which can increase the risk of moisture in the wall as well as impaired energetic performance due to increased thermal conductivity with increased moisture content in the building materials. This dataset was acquired through numerical simulation using the coupling of two simulation tools, namely Envi-Met and Delphin. This coupling was used to include the complex role the plants play in shaping the near-wall environmental parameters in the hygrothermal simulations. Four different wall assemblies were investigated, each assembly was assessed twice: with and without the living wall. The presented data include the input and output parameters of the simulations, which were presented in the co-submitted article [1].
This study demonstrates the application and combination of multiple imaging techniques [light microscopy, micro-X-ray computer tomography (μ-CT), scanning electron microscopy (SEM) and focussed ion beam – nano-tomography (FIB-nT)] to the analysis of the microstructure of hydrated alite across multiple scales. However, by comparing findings with mercury intrusion porosimetry (MIP), it becomes obvious that the imaged 3D volumes and 2D images do not sufficiently overlap at certain scales to allow a continuous quantification of the pore size distribution (PSD). This can be overcome by improving the resolution and increasing the measured volume. Furthermore, results show that the fibrous morphology of calcium-silicate-hydrates (C-S-H) phases is preserved during FIB-nT. This is a requirement for characterisation of nano-scale porosity. Finally, it was proven that the combination of FIB-nT with energy-dispersive X-ray spectroscopy (EDX) data facilitates the phase segmentation of a 11 × 11 × 7.7 μm3 volume of hydrated alite.
Burning of clinker is the most influencing step of cement quality during the production process. Appropriate characterisation for quality control and decision-making is therefore the critical point to maintain a stable production but also for the development of alternative cements. Scanning electron microscopy (SEM) in combination with energy dispersive X-ray spectroscopy (EDX) delivers spatially resolved phase and chemical information for cement clinker. This data can be used to quantify phase fractions and chemical composition of identified phases.
The contribution aims to provide an overview of phase fraction quantification by semi-automatic phase segmentation using high-resolution backscattered electron (BSE) images and lower-resolved EDX element maps. Therefore, a tool for image analysis was developed that uses state-of-the-art algorithms for pixel-wise image segmentation and labelling in combination with a decision tree that allows searching for specific clinker phases. Results show that this tool can be applied to segment sub-micron scale clinker phases and to get a quantification of all phase fractions. In addition, statistical evaluation of the data is implemented within the tool to reveal whether the imaged area is representative for all clinker phases.
Bauhaus-Gastprofessorin Mirjam Wenzel referierte am 30. Juni 2021 im Audimax der Bauhaus-Universität Weimar zur Entstehungsgeschichte und Konzeption Jüdischer Museen. Dabei ging sie darauf ein, inwiefern diese Museen besonders relevant für aktuelle gesellschaftliche und politische Fragestellungen sind. Prof. Wenzels zweiter öffentlicher Vortrag an der Bauhaus-Universität Weimar skizzierte die Potentiale von Kultureinrichtungen in Zeiten gesellschaftspolitischer Veränderungen im Allgemeinen und die Bedeutung Jüdischer Museen angesichts verbaler und tätlicher Gewalt gegen Jüdinnen und Juden im Besonderen.
In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk.
Marine macroalgae such as Ulva intestinalis have promising properties as feedstock for cosmetics and pharmaceuticals. However, since the quantity and quality of naturally grown algae vary widely, their exploitability is reduced – especially for producers in high-priced markets. Moreover, the expansion of marine or shore-based cultivation systems is unlikely in Europe, since promising sites either lie in fishing zones, recreational areas, or natural reserves. The aim was therefore to develop a closed photobioreactor system enabling full control of abiotic environmental parameters and an effective reconditioning of the cultivation medium in order to produce marine macroalgae at sites distant from the shore. To assess the feasibility and functionality of the chosen technological concept, a prototypal plant has been implemented in central Germany – a site distant from the sea. Using a newly developed, submersible LED light source, cultivation experiments with Ulva intestinalis led to growth rates of 7.72 ± 0.04 % day−1 in a cultivation cycle of 28 days. Based on the space demand of the production system, this results in fresh mass productivity of 3.0 kg m−2, respectively, of 1.1 kg m−2 per year. Also considering the ratio of biomass to energy input amounting to 2.76 g kWh−1, significant future improvements of the developed photobioreactor system should include the optimization of growth parameters, and the reduction of the system’s overall energy demand.
Scaling of concrete due to salt frost attack is an important durability issue in moderate and cold climates. The actual damage mechanism is still not completely understood. Two recent damage theories—the glue spall theory and the cryogenic suction theory—offer plausible, but conflicting explanations for the salt frost scaling mechanism. The present study deals with the cryogenic suction theory, which assumes that freezing concrete can take up unfrozen brine from a partly frozen deicing solution during salt frost attack. According to the model hypothesis, the resulting saturation of the concrete surface layer intensifies the ice formation in this layer and causes salt frost scaling. In this study an experimental technique was developed that makes it possible to quantify to which extent brine uptake can increase ice formation in hardened cement paste (used as a model material for concrete). The experiments were carried out with low temperature differential scanning calorimetry, where specimens were subjected to freeze–thaw cycles while being in contact with NaCl brine. Results showed that the ice content in the specimens increased with subsequent freeze–thaw cycles due to the brine uptake at temperatures below 0 °C. The ability of the hardened cement paste to bind chlorides from the absorbed brine at the same time affected the freezing/melting behavior of the pore solution and the magnitude of the ice content.