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  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
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  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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Model calibration of metsovo bridge using ambient vibration measurements from various construction phases

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Auteur
Argyris C., Papadimitriou C., Panetsos P., Tsopelas P.
Date
2015
Language
en
DOI
10.7712/120215.4261.702
Sujet
Bayesian networks
Electric measuring bridges
Inference engines
Modal analysis
Structural dynamics
Uncertainty analysis
Vibration analysis
Vibration measurement
Bayesian inference
High fidelity models
Modal characteristics
Model updating
Operational modal analysis
Predictive capabilities
Sensor configurations
Structural model updating
Bridges
National Technical University of Athens
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Résumé
Available methods for structural model updating are employed to develop high fidelity models of the soil-foundation-structure of 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. Ambient vibration measurements are available during different 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 a detailed numerical model of the bridge based on solid finite elements. A Bayesian method for parameter estimation is used for estimating the parameters of the soil-foundation-structure models under the different construction phases. The inference is based on modal properties identified using the experimental measurements from a number of sensor configuration setups. HPC based on the Transitional MCMC technique, integrated with model reduction tools and surrogate XTMCMC techniques, are used to carry out the Bayesian parameter inference. The discrepancies in the identified parameters using ambient vibrations under the different construction phases are discussed. The effectiveness of the updated models and their predictive capabilities are assessed.
URI
http://hdl.handle.net/11615/70782
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  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]
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