  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 Response (GP-NFIR) model of the nonlinear
self-excited forces acting on structures. Constructed in a nondimensional form,
the model takes the effective wind angle of attack as lagged exogenous input
and outputs a probability distribution of the forces. The nonlinear
input/output function is modeled by a GP regression. Consequently, the model is
nonparametric, thereby circumventing to set up the function's structure a
priori. The training input is designed as random harmonic motion consisting of
vertical and rotational displacements. Once trained, the model can predict the
aerodynamic forces for both prescribed input motion and aeroelastic analysis.
The concept is first verified for a flat plate's analytical solution by
predicting the self-excited forces and flutter velocity. Finally, the framework
is applied to a streamlined and bluff bridge deck based on Computational Fluid
Dynamics (CFD) data. The model's ability to predict nonlinear aerodynamic
forces, flutter velocity, and post-flutter behavior are highlighted.
Applications of the framework are foreseen in the structural analysis during
the design and monitoring of slender line-like structures.
