Identification of dynamic models of Metsovo (Greece) Bridge using ambient vibration measurements
2012-10-24T14:23:22Z (GMT) by
Available methods for structural model updating are employed to develop high fidelity models of the Metsovo bridge using ambient vibration measurements. The Metsovo bridge, the highest bridge of the Egnatia Odos Motorway, is a two-branch balanced cantilever ravine bridge. It has a total length of 357m, a very long central span of 235m, and a height of 110m for the taller pier. Ambient vibration measurements are available during different construction phases of the bridge for both bridge branches, as well as after the completed construction phases of the bridge. Operational modal analysis software is used to obtain the modal characteristics of the bridge for the various sets of vibration measurements. The modal characteristics are then used to update an increasingly complex set of finite element models of the bridge. These models are based on beam and solid elements. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimising the modal residuals. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multi-objective identification method. The effectiveness of the updated models and their predictive capabilities are assessed. In particular, the variability of the Pareto optimal models and their associated response prediction variability are explored. It is demonstrated that the Pareto optimal structural models may vary, depending on the fidelity of the model class employed and the size of measurement errors. The developed high fidelity finite element models are used for checking design assumptions and for carrying out more accurate predictions of structural response.