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Calibration of the Finite Element Model of a Twelve-Span Prestressed Concrete Bridge Using Ambient Vibration Data

Abstract : The recently constructed Newmarket Viaduct in Auckland is a critical link in the New Zealand state highway network. Newmarket Viaduct is a 12-span, pre-cast, post-tensioned structure built using the balanced cantilever method. A continuous health monitoring system was designed and installed in the bridge. As a critical part in the SHM process, a baseline finite element (FE) model of Newmarket Viaduct was established. This paper describes the implementation of the FE model calibration using ambient vibration data. The initial model of the bridge was developed from the information provided in the design documentation, material testing data and site inspections. Two ambient vibration testing campaigns used some 60 wireless sensors in multiple setups to collect data to map with high density 3D mode shapes of the bridge. The output-only modal identification results obtained from the ambient vibration measurements of the bridge were used to update the FE bridge model. Different parameters of the model were calibrated using an automated procedure to improve the correlation between measured and calculated modal parameters. Careful attention was paid to the selection of the parameters to be modified during updating in order to ensure that the changes to the model were realistic and physically meaningful. The calibrated FE model reflecting the as-built structural condition and dynamic response mechanisms of Newmarket Viaduct will serve as a baseline model for assessment of structural health using continuous monitoring data.
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Submitted on : Wednesday, July 9, 2014 - 10:16:58 AM
Last modification on : Wednesday, July 9, 2014 - 3:28:17 PM
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  • HAL Id : hal-01021186, version 1



Xinghua Chen, Piotr Omenzetter, Sherif Beskhyroun. Calibration of the Finite Element Model of a Twelve-Span Prestressed Concrete Bridge Using Ambient Vibration Data. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021186⟩



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