Intelligent Identification and Verification of Flutter Derivatives and Critical Velocity of Closed-Box Girders Using Gradient Boosting Decision Tree

Abstract

Flutter derivatives (FDs) of the bridge deck are basic aerodynamic parameters by which flutter analysis determines critical flutter velocity (CFV), and they are traditionally identified by sectional model wind tunnel tests or computational fluid dynamics (CFD) numerical simulation. Based on some wind tunnel testing results and numerical simulation data, the machine learning models for identifying FDs of closed-box girders are trained and developed via a gradient boosting decision tree in this study. The models can explore the underlying input–output transfer relationship of datasets and realize rapid intelligent identification of FDs without wind tunnel tests or numerical simulation. This method also provides a convenient and feasible option for expanding datasets of FDs, and the distribution of FDs can be analyzed through the post-interpretation of trained models. Combined with FD sensitivity analysis, the models can be verified by the calculation error of CFV. In addition, the proposed method can help determine the appropriate shape of the box girder cross-section in the preliminary design stage of long-span bridges and provide the necessary reference for aerodynamic shape optimization by modifying the local geometric features of the cross-section.