Digital twin-based identification of aerodynamic admittance functions of a long-span bridge

In consideration of uncertainties involved in the aerodynamic admittance function (AAF) identification conducted in wind tunnels and structural health monitoring (SHM) systems installed in long-span bridges, a digital twin-based AAF identification method using field measurement data collected by a SHM system is proposed in this study. Firstly, a theoretical model for buffeting analysis of a long-span bridge under low wind speed conditions is introduced. Based on the measured wind speed, displacement, and acceleration data, the design document-based finite element model of the bridge is updated and the coherence functions, aerodynamic force coefficients, and damping ratios of the bridge are identified. Subsequently, based on the digital twin concept, the parameters in the AAFs of the bridge are identified using a genetic algorithm and the digital twin is established. The effects of wind turbulence on AAFs as well as the statistics of AAFs parameters are further investigated. The feasibility and accuracy of the digital twin are validated through a case study of a real long-span suspension bridge. The comparisons between the simulating results of the digital twin and the field measured data verify the efficacy of the proposed method in identifying AAFs and predicting the buffeting responses of the bridge.