Michael Anton Kraus

Sudden infrastructure failures can disrupt evacuation networks and force rapid route reconfiguration under time pressure. While evacuation research often focuses on travel times or path efficiency, less attention has been given to discrete route choice behavior following abrupt topological disruption. This study investigates how individuals redistribute across alternative paths after the removal of a critical network edge in an immersive virtual reality (VR) flood scenario based on an urban district in Frankfurt, Germany. Participants (N = 128) initially followed a designated evacuation route. Midway, a bridge collapse removed a key network edge, forcing rerouting at a downstream decision node with three available outgoing branches. We model the street system as an undirected graph with locally directional choice sets and analyze resulting route choices. We quantify redistribution using empirical branch selection probabilities, a chi-square test against uniform allocation, and Shannon entropy as a concentration measure. Results reveal stable and non-uniform redistribution patterns across branches, with branch preferences aligning with simple geometric properties of the available alternatives, including directional continuity, path length, and detour. Redistribution was largely invariant to demographic and experience-related attributes, suggesting a dominant role of geometry in this setting. Even in the absence of crowd interaction, consistent branch distributions emerge. These findings highlight the importance of topological structure in shaping evacuation behavior under disruption and demonstrate how immersive experiments can inform network-level resilience analysis.

As part of urban mining initiatives aimed at reducing waste, CO ₂ emissions and the extraction of raw materials, the demand for sustainable resource recovery and repurposing of construction materials is growing. With many European buildings due to be renovated to meet current energy standards, recovering insulating glass units (IGUs) presents a valuable opportunity to reclaim significant amounts of glass panes while reducing environmental impact of glass facades. However, the outer surfaces of IGUs are especially exposed to environmental influences, such as weathering and mechanical impacts, which may introduce surface defects or changes to the glass surface composition, such as corrosion. These phenomena may affect the processability and remanufacturing processes. This study evaluates the feasibility of producing laminated safety glass (LSG) from reclaimed insulating glass units (IGUs). First the surface roughness is analyzed to evaluate environmental exposure of the glass surfaces. Next, laminated glass, with a new PVB interlayer, is manufactured using two configurations of the glass surfaces, primarily with the reclaimed glass, to determine the effect of natural aging, positioning, and combinations on its adhesion performance tested under temperature and humidity conditions in accordance with EN ISO 12543-4, as well as under mechanical loads in the pummel tests. Comparative tests are also conducted with LSG made entirely from new glass. The results demonstrate the possibility of producing standard-compliant LSG from reclaimed float glass.

Abstrakt: Der Glas‐ und Fassadenbau befindet sich derzeit in einer Phase beschleunigten Wandels: Die europäische Energiekrise, verschärfte Anforderungen an die energetische Performance der Gebäudehülle sowie die Megatrends Nachhaltigkeit und künstliche Intelligenz wirken gleichzeitig auf Produkte, Fertigungsverfahren und Bemessungsmethoden. Der vorliegende Beitrag ordnet vier Entwicklungslinien ein, die den Glas‐ und Fassadenbau in den kommenden Jahren prägen werden. Erstens werden hochleistungsfähige Isolierverglasungen betrachtet – Vakuumisolierglas, Dreifach‐Isolierglas mit Dünnglas als Mittelscheibe sowie photovoltaikintegrierte Aufbauten – einschließlich ihrer konstruktiven Besonderheiten und des Stands der bauaufsichtlichen Regelung. Zweitens lassen sich durch neue Fertigungsverfahren – wie laserinduziertes Glasbiegen – strukturoptimierte Verglasungen effizienter herstellen. Drittens wird die zunehmende Verzahnung von Verarbeitungstechnik und Eigenschaftsgestaltung dargestellt. Viertens wird der Einsatz künstlicher Intelligenz entlang der Wirkungskette „Daten – Modell – Bemessung – Betrieb“ systematisiert. Abschließend werden die Technologien vergleichend bewertet und Perspektiven für zukünftige Anwendungen im Fassadenbau aufgezeigt.

A novel method for the geometrically nonlinear plate analysis of cold bent monolithic glass using Physics-Informed Neural Networks (PINNs) is presented. The approach leverages the power of PINNs as solvers for the associated nonlinear partial differential equations of von Karman theory. Traditional methods for this type of analysis are computationally expensive and often require complex finite element modelling, while closed-form analytical solutions are seldomly found. We bypass these limitations by formulating the governing PDEs as loss functions for a neural network. We detail the complete modelling setup, including the incorporation of boundary conditions directly into the network's loss functions. The paper then outlines a systematic training methodology, exploring various neural network architectures and hyperparameter settings to optimize performance and convergence. We demonstrate the PINN's capability to accurately predict the deformed shape and stress distribution in cold bent glass plates under various loading conditions through comparison with FE solutions. The core contribution of this work is a practical and efficient framework for applying PINNs to a real-world structural engineering problem. Ultimately, the paper provides a clear outline on how the trained PINN can be used as a powerful and rapid design tool for cold bent glass, enabling architects and engineers to explore complex geometries and loading scenarios with unprecedented speed and accuracy.