Identification of the form of self-excited aerodynamic force of bridge deck based on machine learning

This paper introduces an intelligent identification method for self-excited aerodynamic equations. The method is based on advanced sparse recognition technology and equipped with a new sampling strategy designed for weak nonlinear dynamic systems with limit cycle characteristics. Considering the complexity of the experiment condition and the difficult a priori selection of hyperparameters, a method based on information criteria and ensemble learning is proposed to derive the global optimal aerodynamic self-excited model. The proposed method is first validated by simulated data obtained from some well-known equations and then applied to the identification of flutter aerodynamic equations based on wind tunnel experiments. Finally, reasons for the different sparse recognition results under different sizes of candidate function space are discussed from the perspective of matrix linear correlation and numerical calculation.