Physics-guided deep learning framework for predictive modeling of bridge vortex-induced vibrations from field monitoring

Vortex-induced vibrations (VIVs) with large amplitudes have been observed on long-span bridges worldwide. Classic semi-empirical VIV models that depend on wind tunnel tests are challenged when required to predict the VIV response of real bridges due to the complexity of real winds, high Reynolds number effects, and uncertainty of bridge structures. The prediction accuracy by these laboratory-based models may, thus, be reduced for real large-scale bridges. Emerging field monitoring systems on prototype bridges allow one to reconsider modeling of bridge VIVs with considerations of real natural winds and full-scale structures by massive monitoring data. In this research, first, we derive a general form of time-dependent ordinary differential equation based on Scanlan's semi-empirical model and field observed bridge VIVs to describe VIV dynamics. Second, guided by the formulation and field observation, we propose a deep learning framework to identify the VIV dynamics, leading to a data-driven model. We demonstrate the proposed framework on a real long-span bridge by performing long-time prediction of the VIV response under real natural winds.