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Strategien der Sichtbarkeit und Sichtbarmachung von ‚Wearable Enhancement‘ im Bereich Smart Health
(2022)
Die vorliegende Forschungsarbeit befasst sich mit der Entwicklung und Gestaltung von körpernahen, tragbaren Artefakten für den digitalisierten Gesundheitsbereich. Unter dem entwickelten Begriff des Wearable Enhancements werden die verschiedenen Termini aus smarten Textilien, Fashion Technologies, Wearable Technologies sowie elektronischen Textilien zusammengefasst und zwei zentrale Forschungsfragen untersucht. Wie kann Wearable Enhancement im Bereich Smart Health ethisch, sozial und ökologisch entwickelt und gestaltet werden? Inwiefern können textile Schnittstellen die Wahrnehmung und die Wahrnehmbarkeit des Körpers verändern? Mit der ersten Forschungsfrage sollen vorrangig Ansätze und Strategien der Sichtbarkeit für die Entwicklung und Gestaltung diskutiert werden, welche Aussagen für die Designpraxis, den Gestaltungs- und Designforschungsprozess sowie die Designlösungen selbst generieren sollen. Die zweite Forschungsfrage zielt darauf, Formen der Sichtbarmachung von sowie für Wearable Enhancement zu untersuchen.
Anhand von drei konkreten Fallstudien werden wesentliche Aspekte der Rezeption, Perzeption, Konstruktion, Konfiguration und Konzeption von soziotechnischen Artefakten zur Funktionssteigerung des menschlichen Körpers untersucht und verschiedene Formen der Sichtbarkeit und Sichtbarmachung entwickelt. In der Arbeit wird ein dual-angelegter transdisziplinärer Designforschungsansatz entwickelt und praktiziert, welcher sowohl die menschlichen Bedürfnisse der Nutzer*innen als auch die Weiterentwicklung von Technologien berücksichtigt. Auf dieser Grundlage wird versucht Anregungen für ein zukunftsfähiges und zugleich verantwortungsorientiertes Design zu geben.
In recent years, lightweight materials, such as polymer composite materials (PNCs) have been studied and developed due to their excellent physical and chemical properties. Structures composed of these composite materials are widely used in aerospace engineering structures, automotive components, and electrical devices. The excellent and outstanding mechanical, thermal, and electrical properties of Carbon nanotube (CNT) make it an ideal filler to strengthen polymer materials’ comparable properties. The heat transfer of composite materials has very promising engineering applications in many fields, especially in electronic devices and energy storage equipment. It is essential in high-energy density systems since electronic components need heat dissipation functionality. Or in other words, in electronic devices the generated heat should ideally be dissipated by light and small heat sinks.
Polymeric composites consist of fillers embedded in a polymer matrix, the first ones will significantly affect the overall (macroscopic) performance of the material. There are many common carbon-based fillers such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), carbon nanobuds (CNB), fullerene, and graphene. Additives inside the matrix have become a popular subject for researchers. Some extraordinary characters, such as high-performance load, lightweight design, excellent chemical resistance, easy processing, and heat transfer, make the design of polymeric nanotube composites (PNCs) flexible. Due to the reinforcing effects with different fillers on composite materials, it has a higher degree of freedom and can be designed for the structure according to specific applications’ needs. As already stated, our research focus will be on SWCNT enhanced PNCs. Since experiments are timeconsuming, sometimes expensive and cannot shed light into phenomena taking place for instance at the interfaces/interphases of composites, they are often complemented through theoretical and computational analysis.
While most studies are based on deterministic approaches, there is a comparatively lower number of stochastic methods accounting for uncertainties in the input parameters. In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. However, uncertainties in the input parameters such as aspect ratio, volume fraction, thermal properties of fiber and matrix need to be taken into account for reliable predictions. In this research, a stochastic multiscale method is provided to study the influence of numerous uncertain input parameters on the thermal conductivity of the composite. Therefore, a hierarchical multi-scale method based on computational homogenization is presented in to predict the macroscopic thermal conductivity based on the fine-scale structure. In order to study the inner mechanism, we use the finite element method and employ surrogate models to conduct a Global Sensitivity Analysis (GSA). The SA is performed in order to quantify the influence of the conductivity of the fiber, matrix, Kapitza resistance, volume fraction and aspect ratio on the macroscopic conductivity. Therefore, we compute first-order and total-effect sensitivity indices with different surrogate models.
As stochastic multiscale models are computational expensive, surrogate approaches are commonly exploited. With the emergence of high performance computing and artificial intelligence, machine learning has become a popular modeling tool for numerous applications. Machine learning (ML) is commonly used in regression and maps data through specific rules with algorithms to build input and output models. They are particularly useful for nonlinear input-output relationships when sufficient data is available. ML has also been used in the design of new materials and multiscale analysis. For instance, Artificial neural networks and integrated learning seem to be ideally for such a task. They can theoretically simulate any non-linear relationship through the connection of neurons. Mapping relationships are employed to carry out data-driven simulations of inputs and outputs in stochastic modeling.
This research aims to develop a stochastic multi-scale computational models of PNCs in heat transfer. Multi-scale stochastic modeling with uncertainty analysis and machine learning methods consist of the following components:
-Uncertainty Analysis. A surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. All selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS).
-Hybrid Machine Learning Algorithms. A combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. The thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio are selected as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks.
-Stochastic Integrated Machine Learning. A stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in PNCs is developed. Seven types of machine learning models are exploited in this research, namely Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling to construct the relationship between the variable of the inputs’ uncertainty and the macroscopic thermal conductivity of PNCs. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. The advantages and disadvantages of various methods are also analyzed in terms of computing time and model complexity to finally give a recommendation for the applicability of different models.
We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.
The reduction of the cement clinker content is an important prerequisite for the improvement of the CO2-footprint of concrete. Nevertheless, the durability of such concretes must be sufficient to guarantee a satisfactory service life of structures. Salt frost scaling resistance is a critical factor in this regard, as it is often diminished at increased clinker substitution rates. Furthermore, only insufficient long-term experience for such concretes exists. A high salt frost scaling resistance thus cannot be achieved by applying only descriptive criteria, such as the concrete composition. It is therefore to be expected, that in the long term a performance based service life prediction will replace the descriptive concept.
To achieve the important goal of clinker reduction for concretes also in cold and temperate climates it is important to understand the underlying mechanisms for salt frost scaling. However, conflicting damage theories dominate the current State of the Art. It was consequently derived as the goal of this thesis to evaluate existing damage theories and to examine them experimentally. It was found that only two theories have the potential to describe the salt frost attack satisfactorily – the glue spall theory and the cryogenic suction theory.
The glue spall theory attributes the surface scaling to the interaction of an external ice layer with the concrete surface. Only when moderate amounts of deicing salt are present in the test solution the resulting mechanical properties of the ice can cause scaling. However, the results in this thesis indicate that severe scaling also occurs at deicing salt levels, at which the ice is much too soft to damage concrete. Thus, the inability of the glue spall theory to account for all aspects of salt frost scaling was shown.
The cryogenic suction theory is based on the eutectic behavior of salt solutions, which consist of two phases – water ice and liquid brine – between the freezing point and the eutectic temperature. The liquid brine acts as an additional moisture reservoir, which facilitates the growth of ice lenses in the surface layer of the concrete. The experiments in this thesis confirmed, that the ice formation in hardened cement paste increases due to the suction of brine at sub-zero temperatures. The extent of additional ice formation was influenced mainly by the porosity and by the chloride binding capacity of the hardened cement paste.
Consequently, the cryogenic suction theory plausibly describes the actual generation of scaling, but it has to be expanded by some crucial aspects to represent the salt frost scaling attack completely. The most important aspect is the intensive saturation process, which is ascribed to the so-called micro ice lens pump. Therefore a combined damage theory was proposed, which considers multiple saturation processes. Important aspects of this combined theory were confirmed experimentally.
As a result, the combined damage theory constitutes a good basis to understand the salt frost scaling attack on concrete on a fundamental level. Furthermore, a new approach was identified, to account for the reduced salt frost scaling resistance of concretes with reduced clinker content.
Revisiting vernacular technique: Engineering a low environmental impact earth stabilisation method
(2022)
The major drawbacks of earth as a construction material — such as its low water stability and moderate strength — have led mankind to stabilize earth. Different civilizations developed vernacular techniques mainly focussing on lime, pozzolan or gypsum stabilization. Recently, cement has become the most commonly used additive in earth stabilization as it improves the strength and durability of plain earth. Also, it is a familiar and globally available construction material. However, using cement as an additive reduces the environmental advantages of earth and run counter to global targets regarding the reduction of CO2 emissions. Alternatives to cement stabilization are currently neither efficient enough to reduce its environmental impact nor allow the possibility of obtaining better results than those of cement. As such, this thesis deals with the rediscovery of a reverse engineering approach for a low environmental impact earth stabilization technique, aiming to replace cement in earth stabilization.
The first step in the method consists in a comprehensive review of earth stabilization with regards to earthen building standards and soil classification, which allows us to identify the research gap. The review showed that there is great potential in using other additives which result in similar improvements as those achieved by cement. However, the studies that have been conducted so far either use expansive soils, which are not suitable for earth constructions or artificial pozzolans that indirectly contribute to CO2 emissions. This is the main research gap.
The key concept for the development in the second step of the method is to combine vernacular additives to both improve the strength and durability of plain earth and to reduce the CO2 emissions. Various earth-mixtures were prepared and both development and performance tests were done to investigate the performance of this technique. The laboratory analyses on mix-design have proven a high durability and the results show a remarkable increase in strength performance. Furthermore, a significant reduction in CO2 emissions in comparison to cement stabilization could be shown.
The third step of the method discusses the results drawn from the experimental programme. In addition, the potential of the new earth mixture with regards to its usability in the field of building construction and architectural design is further elaborated on.
The method used in this study is the first of its kind that allows investors to avoid the very time-consuming processes such as finding a suitable source for soil excavation and soil classification. The developed mixture has significant workability and suitability for production of stabilized earthen panels — the very first of its kind. Such a panel is practically feasible, reasonable, and could be integrated into earthen building standards in general and in particular to DIN 18948, which is related to earthen boards and published in 2018.
The modern industries of the 19th and 20th centuries had multiple effects on the spatial transformation of cities and regions. The past decade has witnessed increasing scholarly and governmental attempts toward conserving modern industrial heritage in the so-called Global North, with the goal, among others, of leveraging this heritage as a driver for urban economic development. In Egypt, the process continues to lag behind; on the one hand, this is due to the perplexing official recognition of the (in)tangible witnesses of modern industries. On the other hand, the official recognition and previous publications focus predominantly on weighing the significance of industrial structures based on their monumental architectural aesthetics. Their historical urban role and spatial attributes as part of urban heritage have yet to be seriously acknowledged. Accordingly, this hinders the integration of the extant industrial sites into the broader debate surrounding urban conservation, leaving them vulnerable to decay and destruction.
This dissertation steers away from the singular investigation of selective modern industrial sites to recall their historical spatial development on a city scale. This is effected by investigating a case study - the Egyptian port city of Alexandria. With the limited secondary data available on modern industries in Alexandria, this dissertation relied predominantly on primary sources. The author collected and leveraged both quantitative and qualitative data to recontextualize modern industries in terms of their spatial dynamics, order, and rationale within cities’ transformation.
By recalling historical spatial development in Alexandria, the contribution of this dissertation lies in highlighting what the author refers to as the Omitted Heritage. This is defined by the modern industries in Egypt that are intentionally, unintentionally, and forgetfully excluded in terms of physical documentation, evaluation, appreciation, and integration within urban development plans. The method used excavated the richness of the established modern industries in Alexandria in terms of their quantity and diversity, which would have otherwise remained largely forgotten. The contextualization of modern industries unveiled spatial periodization, spatial dynamics, and conceptual development. The study draws on important analytical aspects that transcend the sites’ boundaries, elevating their significance to the municipal, regional, national, and even global levels. Its recommendations for further research are also divided into those levels.
Quantification of cracks in concrete thin sections considering current methods of image analysis
(2022)
Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.
Städten kam bei demokratischen Innovationsprozessen immer eine zentrale Rolle zu. Die öffentlichen Verwaltungen der großen Städte stellten Regeln für die Einführung und Ausweitung der bürgerschaftlichen Partizipation auf und reagierten damit auf Erfahrungen und Forderungen, die von der schöpferischen politischen Kraft der sozialen und urbanen Bewegungen getragen wurden. Die Geschichte Barcelonas ist dafür ein typisches Beispiel.
Dank dieser sozialen Errungenschaften können wir von einer Reihe von Gütern und Dienstleistungen profitieren, die lokale Wohlfahrtssysteme ausmachen. Die Stadtverwal-tungen übernehmen die Aufgabe, Ressourcen und Dienstleistungen bereitzustellen, die nicht nur mit Wohlfahrt und Gesundheit in Verbindung stehen, sondern auch mit der Sorge um Umfeld und Umwelt, mit der Förderung von Maßnahmen in Bereichen wie Bildung, Kultur, Kunst oder Sport sowie mit der Dynamisierung von Wirtschaft und Gesellschaft. Ob zuständig oder nicht, die Kommunen müssen auf die Forderungen der Bürger*innen reagieren, sind sie doch die Verwaltungen, die den alltäglichen Problemen und Bedürfnissen am nächsten stehen. Daher liegt es weniger im Belieben der Stadtverwaltungen, ob sie notwendige Innova¬tionen anstoßen, sondern diese sind vielmehr Teil ihres Aufgabenbereichs.
Um den Bedürfnissen der Bürger*innen seitens der öffentlichen Verwaltung gerecht zu werden, kam in den meisten Fällen eine von zwei Methoden zur Anwendung: die direkte Verwaltung durch die Behörden oder die indirekte Verwaltung mit dem privaten Sektor. Mit dem Anbruch einer neuen Zeit, in der alternative Methoden an Bedeutung gewonnen haben, wächst das Interesse an Modellen öffentlich-zivilgesellschaftlicher Zusammenarbeit. Hauptziel dieser Modelle ist es, Verwaltungen und Bürgerschaft eine Zusammenarbeit im gemeinsamen und allgemeinen Interesse zu ermöglichen, indem Projekte unterstützt werden, die Zugang, Nähe und Partizipation in sich vereinen. Vor diesem Hintergrund bietet die Verwaltung öffent¬licher Ressourcen Möglichkeiten zur Entwicklung neuer Formen kollektiver Intelligenz, mit ge¬meinsamer Verantwortung und Synergie zwischen Institution und Bürgerschaft, sodass die Städte zu wahrhaft kooperativen Plattformen für öffentliche Innovationen werden.
Ausgehend von der Bemerkung des Philosophen Jacques Derrida, dass Erbe immer auch eine Aufgabe sei, widmet sich der dritte Band der Schriftenreihe des Graduiertenkollegs „Identität und Erbe“ den sozialen und kulturellen Praktiken der Bezugnahme auf Vergangenheit(en) und Identität(en). Mit einem (kulturellen) Erbe soll und muss etwas getan werden, um es überhaupt hervorzubringen. Es konstituiert sich erst im Akt des (Nicht-)Erbens, das heißt im Wechselverhältnis mit den mit und an ihm ausgeführten Praktiken. Gleichwohl ermöglicht erst deren Verbindung mit den materiellen Überresten und Überlieferungen des Erbes eine Aneignung oder Ablehnung der Vergangenheit sowie die Fort- und Umschreibung eines bereits bestehenden Erbes. Diese Vorgänge sind nicht willkürlicher Natur: Die Möglichkeiten zur Interpretation und Deutung werden durch die sozialen, politischen, kulturellen, ökonomischen und technischen Bedingungen der Gegenwart sowie durch die Geschichte und Materialität des Erbes beschränkt, erweitert und gelenkt. Erbe und Erbeprozesse müssen deshalb notwendigerweise miteinander in Beziehung gesetzt werden.
Mit Beiträgen von Simone Bogner und Michael Karpf, Stefan Willer, Giorgia Aquilar, Jörg Springer, Bernd Euler-Rolle, Elizabeth Sikiaridi und Frans Vogelaar, Verena von Beckerath, Alexandra Klei, Oluwafunminiyi Raheem, Ronny Grundig, Özge Sezer, Anna Kutkina, Inge Manka, Karolina Hettchen und Monique Jüttner sowie Julian Blunk.
Operator Calculus Approach to Comparison of Elasticity Models for Modelling of Masonry Structures
(2022)
The solution of any engineering problem starts with a modelling process aimed at formulating a mathematical model, which must describe the problem under consideration with sufficient precision. Because of heterogeneity of modern engineering applications, mathematical modelling scatters nowadays from incredibly precise micro- and even nano-modelling of materials to macro-modelling, which is more appropriate for practical engineering computations. In the field of masonry structures, a macro-model of the material can be constructed based on various elasticity theories, such as classical elasticity, micropolar elasticity and Cosserat elasticity. Evidently, a different macro-behaviour is expected depending on the specific theory used in the background. Although there have been several theoretical studies of different elasticity theories in recent years, there is still a lack of understanding of how modelling assumptions of different elasticity theories influence the modelling results of masonry structures. Therefore, a rigorous approach to comparison of different three-dimensional elasticity models based on quaternionic operator calculus is proposed in this paper. In this way, three elasticity models are described and spatial boundary value problems for these models are discussed. In particular, explicit representation formulae for their solutions are constructed. After that, by using these representation formulae, explicit estimates for the solutions obtained by different elasticity theories are obtained. Finally, several numerical examples are presented, which indicate a practical difference in the solutions.