Covariance-driven stochastic subspace identification of an end-supported pontoon bridge under varying environmental conditions

Abstract

The Bergsøysund Bridge is currently being extensively monitored with accelerometers, anemometers, wave radars and GNSS sensors. By applying Covariance-driven Stochastic Subspace Identification (Cov-SSI), the modal parameters of the bridge are estimated. The results are interpreted in the context of the environment, represented by significant wave heights. The problem is characterized by the fact that modes are closely spaced in frequency and have high damping. Two weighting algorithms for the Cov-SSI are applied, to assess their performance for application on structures with these characteristics.