A new approach to model aerodynamic nonlinearities in the time domain utilizing an artificial neural network (ANN) framework with embedded cellular automata (CA) scheme has been developed. This nonparametric modeling approach has shown good promise in capturing the hysteretic nonlinear behavior of aerodynamic systems in terms of hidden neurons involving higher-order terms. Concurrent training of a set of higher-order neural networks facilitates a unified approach for modeling the combined analysis of flutter and buffeting of cable-supported bridges. Accordingly, the influence of buffeting response on the self-excited forces can be captured, including the contribution of damping and coupling effects on the buffeting response. White noise is intentionally introduced to the input data to enhance the robustness of the trained neural network embedded with optimal typology of CA. The effectiveness of this approach and its applications are discussed by way of modeling the aerodynamic behavior of a single-box girder cross-section bridge deck (2-D) under turbulent wind conditions. This approach can be extended to a full-bridge (3-D) model that also takes into account the correlation of aerodynamic forces along the bridge axis. This novel application of data-driven modeling has shown a remarkable potential for applications to bridge aerodynamics and other related areas.
|Contributor||Tracy Kijewski-Correa, Committee Member|
|Contributor||Ahsan Kareem, Committee Chair|
|Contributor||Seymour Spence, Committee Member|
|Degree Level||Master's Thesis|
|Degree Discipline||Civil Engineering and Geological Sciences|
|Departments and Units|