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Bauliche Anlagen mit Stahlkonstruktionen (bzw. auch Stahlbetonskelett- Konstruktionen) und metallenen Wänden sind bereits in sehr großer Zahl errichtet. Dazu gehören kleinere bis größere Lagerhallen ebenso wie Einkaufszentren. Sie zeichnen sich durch große Flexibilität, einfache Planung, kurze Bauzeit und rel. geringe Kosten aus. Auch in der nahen Zukunft ist deshalb mit Planung und Errichtung weiterer solcher baulicher Anlagen zu rechnen. Abhängig von der Nutzung der Hallen sind auch mehr oder weniger umfangreiche elektrische und elektronische Systeme vorhanden, die wichtige Funktionen sicherstellen müssen. Der Blitzschutz für diese baulichen Anlagen sollte sich also nicht nur im „klassischen“ Gebäude-Blitzschutz nach DIN V 0185-3 VDE V 0185 Teil 3 [1] erschöpfen; ein Ergänzung hin zu einem sinnvollen Grundschutz der elektrischen und elektronischen Systeme nach DIN V 0185-4 VDE V 0185 Teil 4 [2] ist anzuraten. Im folgenden Beitrag wird ein Konzept vorgestellt, mit dem ein hochwertiger Blitzschutz sowohl der baulichen Anlage und der darin befindlichen Personen, als auch der elektrischen und elektronischen Systeme verwirklicht werden kann. Insbesondere bei großflächigen Hallen stellen sich dabei besondere Anforderungen. Das Konzept und die zugehörigen blitzschutz-technischen Maßnahmen können drei Hauptbereichen zugeordnet werden: - Äußerer Blitzschutz; - Innerer Blitzschutz; - weitergehende besondere Maßnahmen. Das Konzept sowie die Maßnahmen werden allgemein beschrieben und teilweise anhand einer ausgeführten Anlage mit Fotos beispielhaft dokumentiert.
In many historical centers in Europe, stone masonry is part of building aggregates, which developed when the layout of the city or village was densified. The analysis of such building aggregates is very challenging and modelling guidelines missing. Advances in the development of analysis methods have been impeded by the lack of experimental data on the seismic response of such aggregates. The SERA project AIMS (Seismic Testing of Adjacent Interacting Masonry Structures) provides such experimental data by testing an aggregate of two buildings under two horizontal components of dynamic excitation. With the aim to advance the modelling of unreinforced masonry aggregates, a blind prediction competition is organized before the experimental campaign. Each group has been provided a complete set of construction drawings, material properties, testing sequence and the list of measurements to be reported. The applied modelling approaches span from equivalent frame models to Finite Element models using shell elements and discrete element models with solid elements. This paper compares the first entries, regarding the modelling approaches, results in terms of base shear, roof displacements, interface openings, and the failure modes.
Recent earthquakes showed that low-rise URM buildings following codecompliant seismic design and details behaved in general very well without substantial damages. Although advances in simulation tools make nonlinear calculation methods more readily accessible to designers, linear analyses will still be the standard design method for years to come. The present paper aims to improve the linear seismic design method by providing a proper definition of the q-factor of URM buildings. Values of q-factors are derived for low-rise URM buildings with rigid diaphragms, with reference to modern structural configurations realized in low to moderate seismic areas of Italy and Germany. The behaviour factor components for deformation and energy dissipation capacity and for overstrength due to the redistribution of forces are derived by means of pushover analyses. As a result of the investigations, rationally based values of the behaviour factor q to be used in linear analyses in the range of 2.0 to 3.0 are proposed.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.