Discovering time-varying aeroelastic models of a long-span suspension bridge from field measurements by sparse identification of nonlinear dynamical systems

TL;DR

This paper presents a data-driven approach using sparse identification to develop real-time, time-varying models of aeroelastic effects on long-span suspension bridges, capturing vortex-induced vibrations from sensor data.

Contribution

It introduces a novel application of sparse identification of nonlinear dynamics (SINDy) to model complex, time-dependent aeroelastic interactions in bridges from sparse sensor measurements.

Findings

Successfully identified time-varying models of bridge dynamics

Captured vortex-induced vibration events accurately

Generated new models surpassing existing theoretical descriptions

Abstract

We develop data-driven dynamical models of the nonlinear aeroelastic effects on a long-span suspension bridge from sparse, noisy sensor measurements which monitor the bridge. Using the {\em sparse identification of nonlinear dynamics} (SINDy) algorithm, we are able to identify parsimonious, time-varying dynamical systems that capture vortex-induced vibration (VIV) events in the bridge. Thus we are able to posit new, data-driven models highlighting the aeroelastic interaction of the bridge structure with VIV events. The bridge dynamics are shown to have distinct, time-dependent modes of behavior, thus requiring parametric models to account for the diversity of dynamics. Our method generates hitherto unknown bridge-wind interaction models that go beyond current theoretical and computational descriptions. Our proposed method for real-time monitoring and model discovery allow us to move our model predictions beyond lab theory to practical engineering design, which has the potential to assess bad engineering configurations that are susceptible to deleterious bridge-wind interactions. With the rise of real-time sensor networks on major bridges, our model discovery methods can enhance an engineers ability to assess the nonlinear aeroelastic interactions of the bridge with its wind environment.