| Methods for flutter stability analysis of long-span bridges: a review T Abbas, I Kavrakov, G Morgenthal Proceedings of the Institution of Civil Engineers-Bridge Engineering 170 (4 …, 2017 | 69 | 2017 |
| A comparative assessment of aerodynamic models for buffeting and flutter of long-span bridges I Kavrakov, G Morgenthal Engineering 3 (6), 823-838, 2017 | 49 | 2017 |
| Prediction of aeroelastic response of bridge decks using artificial neural networks T Abbas, I Kavrakov, G Morgenthal, T Lahmer Computers & Structures 231, 106198, 2020 | 47 | 2020 |
| Complete framework of wind-vehicle-bridge interaction with random road surfaces A Camara, I Kavrakov, K Nguyen, G Morgenthal Journal of Sound and Vibration 458, 197-217, 2019 | 41 | 2019 |
| A synergistic study of a CFD and semi-analytical models for aeroelastic analysis of bridges in turbulent wind conditions I Kavrakov, G Morgenthal Journal of Fluids and Structures 82, 59-85, 2018 | 32 | 2018 |
| IABSE Task Group 3.1 benchmark results. Part 1: Numerical analysis of a two-degree-of-freedom bridge deck section based on analytical aerodynamics G Diana, S Stoyanoff, K Aas-Jakobsen, A Allsop, M Andersen, T Argentini, ... Structural Engineering International 30 (3), 401-410, 2020 | 26 | 2020 |
| IABSE Task Group 3.1 Benchmark Results. Part 2: Numerical Analysis of a Three-Degree-of-Freedom Bridge Deck Section Based on Experimental Aerodynamics G Diana, S Stoyanoff, K Aas-Jakobsen, A Allsop, M Andersen, T Argentini, ... Structural Engineering International 30 (3), 411-420, 2020 | 22 | 2020 |
| A categorical perspective towards aerodynamic models for aeroelastic analyses of bridge decks I Kavrakov, D Legatiuk, K Gürlebeck, G Morgenthal Royal Society open science 6 (3), 181848, 2019 | 21 | 2019 |
| Comparison metrics for time-histories: Application to bridge aerodynamics I Kavrakov, A Kareem, G Morgenthal Journal of Engineering Mechanics 146 (9), 04020093, 2020 | 20* | 2020 |
| Determination of complex aerodynamic admittance of bridge decks under deterministic gusts using the Vortex Particle Method I Kavrakov, T Argentini, S Omarini, D Rocchi, G Morgenthal Journal of Wind Engineering and Industrial Aerodynamics 193, 103971, 2019 | 13 | 2019 |
| Aeroelastic analyses of bridges using a Pseudo-3D vortex method and velocity-based synthetic turbulence generation I Kavrakov, G Morgenthal Engineering Structures 176, 825-839, 2018 | 12 | 2018 |
| Data-driven aerodynamic analysis of structures using Gaussian Processes I Kavrakov, A McRobie, G Morgenthal Journal of Wind Engineering and Industrial Aerodynamics 222, 104911, 2022 | 9 | 2022 |
| Synergistic framework for analysis and model assessment in bridge aerodynamics and aeroelasticity I Kavrakov Bauhaus-Universität Weimar, 2020 | 6 | 2020 |
| Monitoring the structural response of reinforced concrete poles along high-speed railway tracks L Göbel, F Mucha, Z Jaouadi, I Kavrakov, D Legatiuk, L Abrahamczyk, ... Proceedings of the International RILEM Conference on Materials, Systems and …, 2016 | 6 | 2016 |
| IABSE Task Group 3.1 benchmark results. Numerical full bridge stability and buffeting simulations G Diana, S Stoyanoff, A Allsop, L Amerio, MS Andersen, T Argentini, ... Structural Engineering International 33 (4), 623-634, 2023 | 5 | 2023 |
| Physics-informed Gaussian process model for Euler-Bernoulli beam elements GR Tondo, S Rau, I Kavrakov, G Morgenthal arXiv preprint arXiv:2308.02894, 2023 | 3 | 2023 |
| Super-long span bridge aerodynamics: on-going results of the TG3. 1 benchmark test–Step 1.2 G Diana, S Stoyanoff, A Allsop, L Amerio, T Argentini, MC Montoya, ... 20th Congress of IABSE, New York City 2019: The Evolving Metropolis-Report …, 2019 | 3 | 2019 |
| Einfluss realer Materialeigenschaften auf numerische Modellvorhersagen: fallstudie Betonmast L Göbel, F Mucha, I Kavrakov, L Abrahamczyk, M Kraus Bautechnik 95 (2), 111-122, 2018 | 3 | 2018 |
| Influence of Aerodynamic Model Assumptions on the Wind-Vehicle-Bridge Interaction I Kavrakov, A Camara, G Morgenthal IABSE Congress Report Stockholm, 1152-1159, 2016 | 3 | 2016 |
| A physics-informed machine learning model for reconstruction of dynamic loads GR Tondo, I Kavrakov, G Morgenthal arXiv preprint arXiv:2308.08571, 2023 | 2 | 2023 |
