Univ.-Prof. Dr.-Ing. habil. Okyay Altay
Lehrstuhl für Baustatik - Professor
Büroadresse
Prof. Altay ist seit 2024 Inhaber des Lehrstuhls für Baustatik. Zuvor war er an der RWTH Aachen als Akademischer Oberrat am Lehrstuhl für Baustatik und Baudynamik sowie als Geschäftsführer des Centers for Wind and Earthquake Engineering tätig. 2021 habilitierte er sich an der RWTH mit Arbeiten zur adaptiven Schwingungsreduktion, Materialmodellierung und Echtzeitsimulation und promovierte 2013 mit Auszeichnung zum Thema Flüssigkeitsdämpfer.
Vor seiner wissenschaftlichen Laufbahn arbeitete er bei Bernard Ingenieure in Wien als Projektleiter und später als stellvertretender Geschäftsführer der Tochtergesellschaft RED Bernard im Bereich Brückenüberwachung und Schwingungsreduktion. Sein Studium des Bauingenieurwesens mit Vertiefung im Konstruktiven Ingenieurbau absolvierte er an der RWTH Aachen University, nachdem er seine Schulbildung am TED Ankara College in der Türkei abgeschlossen hatte.
Publikationen
Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads
Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Solving large numerical substructures in real‐time hybrid simulations using proper orthogonal decomposition
Energy harvesting in tuned liquid column dampers using Savonius type hydrokinetic turbines
Energy harvesting in tuned liquid column dampers using Savonius type hydrokinetic turbines
Data generation framework for inverse modeling of nonlinear systems in structural dynamics applications
Data generation framework for inverse modeling of nonlinear systems in structural dynamics applications