Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Kavrakov, Igor"'
Publikováno v:
J. Wind Eng. Ind. Aerodyn. 253 (2024) 105848
Recent advancements in data-driven aeroelasticity have been driven by the wealth of data available in the wind engineering practice, especially in modeling aerodynamic forces. Despite progress, challenges persist in addressing free-stream turbulence
Externí odkaz:
http://arxiv.org/abs/2406.15603
Advancements in machine learning and an abundance of structural monitoring data have inspired the integration of mechanical models with probabilistic models to identify a structure's state and quantify the uncertainty of its physical parameters and r
Externí odkaz:
http://arxiv.org/abs/2405.12802
Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian process (GP) model for Timoshenko beam elements. The model is constructed as
Externí odkaz:
http://arxiv.org/abs/2309.11875
Publikováno v:
IABSE Symposium: Long Span Bridges, Istanbul, Turkey, 26-28 April 2023
Long-span bridges are subjected to a multitude of dynamic excitations during their lifespan. To account for their effects on the structural system, several load models are used during design to simulate the conditions the structure is likely to exper
Externí odkaz:
http://arxiv.org/abs/2308.08571
Publikováno v:
IABSE Symposium: Challenges for Existing and Oncoming Structures, Prague, Czech Republic, 25-27 May 2022
A physics-informed machine learning model, in the form of a multi-output Gaussian process, is formulated using the Euler-Bernoulli beam equation. Given appropriate datasets, the model can be used to regress the analytical value of the structure's ben
Externí odkaz:
http://arxiv.org/abs/2308.02894
Wind-induced response governs the design of the long-span bridges. The shape of the deck is one of the most important factors that not only affects the mechanical properties but greatly influences the aerodynamic performance of the bridge. An efficie
Externí odkaz:
http://arxiv.org/abs/2203.14414
With the increasing spans and complex deck shapes, aerodynamic nonlinearity becomes a crucial concern in the design of long-span bridges. This paper investigates the nonlinear interaction between the gust-induced and motion-induced forces acting on b
Externí odkaz:
http://arxiv.org/abs/2109.00441
Publikováno v:
J. Wind Eng. Ind. Aerodyn. 222 (2022) 104911
An abundant amount of data gathered during wind tunnel testing and health monitoring of structures inspires the use of machine learning methods to replicate the wind forces. This paper presents a data-driven Gaussian Process-Nonlinear Finite Impulse
Externí odkaz:
http://arxiv.org/abs/2103.13877
Publikováno v:
In Journal of Wind Engineering & Industrial Aerodynamics September 2023 240
Publikováno v:
In Journal of Fluids and Structures August 2022 113