Abstract
A comprehensive monitoring system is installed and currently in operation on the Bergsøysund Bridge, an end-supported floating pontoon bridge, collecting data on accelerations, displacements, waves, and wind. Using covariance-driven stochastic subspace identification (Cov-SSI), data-driven stochastic subspace identification, and frequency domain decomposition, the modal parameters of the structure are estimated to investigate its dynamic behaviour. Aspects regarding the selection of good parameters for the Cov-SSI analyses are highlighted, and the clarifying effect of applying stabilization criteria on multiple orders of output is discussed. The effects of the significant wave height on the modal parameters are investigated based on an automatic selection of stable poles from stabilization plots produced by the Cov-SSI method.
Highlights
OMA is continuously increasing, with the acronyms frequency domain decomposition (FDD), SSI, ARMA, SOBI and representing some of the most well-Floating bridges of various designs have existed for approx- known methods
The senthe crude, yet effective, peak-picking study performed on the sitivity of the input parameters to the results, the Golden Gate Bridge by McLamore et al [2], the research field number of time lags, was very large, and considerable tweakconcerning the OMA of civil structures has experienced sig- ing of the selected values was required
A manual identification with the covariance-driven stochastic subspace identification (Cov-SSI) was performed for the selected low-level and traffic-driven recordings, yielding the results shown in Tables 5 and 6, respectively
Summary
OMA is continuously increasing, with the acronyms FDD, SSI, ARMA, SOBI and representing some of the most well-. The senthe crude, yet effective, peak-picking study performed on the sitivity of the input parameters to the results, the Golden Gate Bridge by McLamore et al [2], the research field number of time lags, was very large, and considerable tweakconcerning the OMA of civil structures has experienced sig- ing of the selected values was required. By automating the selection of modal parameters, the effects of environmental parameters on the modal quantities are studied based on a large pool of recordings
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