17 citations · 43 across the 10 of their papers we have counts for
10 papers
Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines
Gledson Rodrigo Tondo, Samir Chawdhury, Sergio Andres Castro Giraldo +1
Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is introduced for modelling aerodynami…
Aerodynamic force reconstruction using physics-informed Gaussian processes
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
Accurate modeling of aerodynamic loads is essential for understanding and predicting the responses of complex structural systems. However, these models often rely on simplification…
Data Driven Calibration of Analytical Concrete Creep Models Considering Preloading Effects Using Gaussian Processes
Leonie Heller, Christopher Taube, Gledson Rodrigo Tondo +1
The time-dependent deformation of concrete, particularly creep, remains a key challenge for reliable and material-efficient design. Experimental results show that tailored preloadi…
The role of physical models in the validation and calibration of numerical models -- The example of the Lillebælt Bridge
Paula Apollonia Wunderlich, Gledson Rodrigo Tondo, Guido Morgenthal
With the rapid advancement of computer technologies enabling fast calculations of complex structures, numerical methods have become a central tool in engineering sciences, while ph…
Assessing the dynamic response of long-span bridges under simultaneous wind and traffic loads
Gledson Rodrigo Tondo, Guido Morgenthal
Wind-traffic interactions strongly influence the dynamic response of long-span bridges, yet loads are often analysed independently. This work models concurrent wind and traffic and…
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
Knowledge of the force time history of a structure is essential to assess its behaviour, ensure safety and maintain reliability. However, direct measurement of external forces is o…
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
Igor Kavrakov, Gledson Rodrigo Tondo, Guido Morgenthal
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…
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes
Gledson Rodrigo Tondo, Sebastian Rau, Igor Kavrakov +1
Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian proces…
A physics-informed machine learning model for reconstruction of dynamic loads
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
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 dur…
Physics-informed Gaussian process model for Euler-Bernoulli beam elements
Gledson Rodrigo Tondo, Sebastian Rau, Igor Kavrakov +1
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 mo…