Universitätsbibliothek
Weimar Open Access

This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.

In recent decades, a multitude of concepts and models were developed to understand, assess and predict muscular mechanics in the context of physiological and pathological events. Most of these models are highly specialized and designed to selectively address fields in, e.g., medicine, sports science, forensics, product design or CGI; their data are often not transferable to other ranges of application. A single universal model, which covers the details of biochemical and neural processes, as well as the development of internal and external force and motion patterns and appearance could not be practical with regard to the diversity of the questions to be investigated and the task to find answers efficiently. With reasonable limitations though, a generalized approach is feasible. The objective of the work at hand was to develop a model for muscle simulation which covers the phenomenological aspects, and thus is universally applicable in domains where up until now specialized models were utilized. This includes investigations on active and passive motion, structural interaction of muscles within the body and with external elements, for example in crash scenarios, but also research topics like the verification of in vivo experiments and parameter identification. For this purpose, elements for the simulation of incompressible deformations were studied, adapted and implemented into the finite element code SLang. Various anisotropic, visco-elastic muscle models were developed or enhanced. The applicability was demonstrated on the base of several examples, and a general base for the implementation of further material models was developed and elaborated.

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

Die Gase Sauerstoff und Stickstoff werden für eine Vielzahl an technischen, industriellen, biologischen und medizinischen Einsatzzwecken benötigt. So liegen Anwendungsgebiete dieser Gase neben der klassischen metallverarbeitenden und der chemischen Industrie bei Sauerstoff vor allem in der Medizin, Verbrennungs- und Kläranlagenoptimierung sowie der Fischzucht und bei Stickstoff als Schutz- beziehungsweise Inertgas in der Kunststoffindustrie, der Luft- und Raumfahrt sowie dem Brandschutz. Die Bereitstellung der Gase Sauerstoff und Stickstoff wird nahezu ausschließlich durch die Abtrennung aus der Umgebungsluft realisiert, welche aus ca. 78 Vol.-% Stickstoff, 21 Vol.-% Sauerstoff und 1 Vol.-% Spurengasen (Ar, CO2, Ne, He, ...) besteht. Am Markt etablierte Verfahren der Luftzerlegung sind das Linde-, das PSA- (pressure swing adsorption/Druckwechseladsorption) oder verschiedene Membran-Verfahren. Hierdurch werden die benötigten Gase entweder direkt vor Ort beim Verbraucher erzeugt (PSA- und Polymer-Membranverfahren: geringe Reinheiten) oder zentral in großen Anlagen hergestellt (Linde-Verfahren: hohe Reinheiten) und anschließend zum Verbraucher in Form von Flaschen- oder Tankgasen geliefert (Tansportkosten). Für kleinere Verbraucher mit hohen Ansprüchen an die Reinheit des benötigten Sauerstoffs beziehungsweise Stickstoffs ergibt sich nur die Möglichkeit, die Gase als kostenintensive Transportgase zentraler Gaseversorger zu beziehen und sich somit in eine Abhängigkeit (Lieferverträge, Flaschen-/Tankmieten, ...) zu diesen zu begeben sowie eine eigene Lagerhaltung für die benötigten Gase (Mehraufwand, Lagerkosten, Platzbedarf) zu betreiben. Ziel dieser Arbeit ist es, keramische Material-Systeme auf Basis chemischer Hochtemperatur-Reaktionen als Reaktive Oxidkeramiken zu entwickeln und diese hinsichtlich eines möglichen Einsatzes für die Sauerstoffseparation in neuartigen Luftzerlegungsanlagen zu untersuchen. Derartige Anlagen sollen in ihrem Prinzip an die regenerative Sauerstoffseparation angelehnt sein und in ihren Reaktoren die Reaktiven Oxidkeramiken als Festbett-Material abwechselnd mit Luft be- und Vakuum oder O2-armen Atmosphären entladen. Die Verwendung Reaktiver Oxidkeramiken, welche im Vergleich zu den bisherigen Materialien höhere Sauerstoffaustauschmengen und -raten bei gleichzeitig hoher Lebensdauer und Korrosionsbeständigkeit sowie relativ einfacher Handhabe aufweisen würden, soll ein Schritt in Richtung einer effizienten alternativen Luftzerlegungstechnologie sein. Mit den Reaktiven Oxidkeramiken in einer Luftzerlegungsanlage sollte es im besten Fall möglich sein, in kleinen Anlagen sehr reinen Sauerstoff und zugleich sauerstofffreies Inertgas zu erzeugen sowie eine Sauerstoffan- oder -abreicherung von Luft, Prozess- oder Abgasen zu generieren. Somit besäße eine solche, auf Reaktiven Oxidkeramiken basierende Technologie sehr weit gefächerte Einsatzgebiete und demzufolge ein enormes wirtschaftliches Potential.

The design of engineering structures takes place today and in the past on the basis of static calculations. The consideration of uncertainties in the model quality becomes more and more important with the development of new construction methods and design requirements. In addition to the traditional forced-based approaches, experiences and observations about the deformation behavior of components and the overall structure under different exposure conditions allow the introduction of novel detection and evaluation criteria. The proceedings at hand are the result from the Bauhaus Summer School Course: Forecast Engineering held at the Bauhaus-Universität Weimar, 2017. It summarizes the results of the conducted project work, provides the abstracts of the contributions by the participants, as well as impressions from the accompanying programme and organized cultural activities. The special character of this course is in the combination of basic disciplines of structural engineering with applied research projects in the areas of steel and reinforced concrete structures, earthquake and wind engineering as well as informatics and linking them to mathematical methods and modern tools of visualization. Its innovative character results from the ambitious engineering tasks and advanced modeling demands.