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Author(s):	Kraus, Michael A.; Taras, Andreas
Title:
Abstract:	Herrn Prof. Dr.-Ing. Ingbert Mangerig zur Vollendung seines 70. Lebensjahres gewidmet Die Technologie der Künstlichen Intelligenz (KI) hält derzeit flächendeckend Einzug in Forschung und Praxis aller Branchen. Vorliegender Beitrag greift dies auf, um Hintergründe der KI allgemein sowie speziell für Physik-informierte KI (PIKI) einzuführen. Ausgewählte Beispiele der Berechnungs- und Bemessungspraxis des Stahlbaus veranschaulichen die Anwendung von Physik-informierten Neuronalen Netzen (PINN), wobei spezifische Anforderungen an die Formulierungen des Lernproblems herausgearbeitet werden. PINNs stellen somit eine Alternative zu etablierten numerischen Verfahren unter besonderer Berücksichtigung vorhandener Simulations- und Versuchsdaten dar. Dies erlaubt die Interpretation und Nutzung von PINNs als digitalen Zwilling eines Tragwerks über dessen Lebenszyklus. Die dargestellte PIKI bedingt nicht per se eine Big-Data-Situation und ist damit für die Ingenieurforschung und -praxis interessant. Ein Ausblick auf künftige Anwendungen der KI im Stahlbau rundet diesen Beitrag ab. Computation and verification of steel constructions using physics-informed artificial intelligence Currently the technology of artificial intelligence (AI) spreads into research and industry practice of all branches in diverse forms. Given that situation, this article serves to introduce the reader to theoretical background on AI in general as well as to the specific case of physics-informed AI (PIKI). Selected examples from design and verification practice of steel construction then illustrate the application of physics-informed neural nets (PINN) methods, where the specific requirements for the formulation of the learning problem are highlighted. PINN serves as an alternative to established computational methods for design of steel structures and its components using available experimental and simulation data. This enables the interpretation and use of PINNs being the digital twin of a steel structure over its lifecycle. PIKI as presented here does not per se cause a “big data” situation and is therefore interesting for engineering research and practice. This paper eventually gives perspectives on future applications of AI for steel construction.
Source:	Stahlbau 89 (2020), No. 10
Page/s:	824-832
Language of Publication:	German

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