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Bayesian estimation of tension in bridge hangers using modal frequency measurements

  • Papadimitriou, Costas (University of Thessaly, Department of Mechanical Engineering) ;
  • Giakoumi, Konstantina (University of Thessaly, Department of Mechanical Engineering) ;
  • Argyris, Costas (University of Thessaly, Department of Mechanical Engineering) ;
  • Spyrou, Leonidas A. (Centre for Research and Technology Hellas (CERTH), Institute for Research and Technology) ;
  • Panetsos, Panagiotis (Egnatia Odos S.A., Capital Maintenance Department)
  • Received : 2016.07.22
  • Accepted : 2016.10.10
  • Published : 2016.12.25
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Abstract

The tension of an arch bridge hanger is estimated using a number of experimentally identified modal frequencies. The hanger is connected through metallic plates to the bridge deck and arch. Two different categories of model classes are considered to simulate the vibrations of the hanger: an analytical model based on the Euler-Bernoulli beam theory, and a high-fidelity finite element (FE) model. A Bayesian parameter estimation and model selection method is used to discriminate between models, select the best model, and estimate the hanger tension and its uncertainty. It is demonstrated that the end plate connections and boundary conditions of the hanger due to the flexibility of the deck/arch significantly affect the estimate of the axial load and its uncertainty. A fixed-end high fidelity FE model of the hanger underestimates the hanger tension by more than 20 compared to a baseline FE model with flexible supports. Simplified beam models can give fairly accurate results, close to the ones obtained from the high fidelity FE model with flexible support conditions, provided that the concept of equivalent length is introduced and/or end rotational springs are included to simulate the flexibility of the hanger ends. The effect of the number of experimentally identified modal frequencies on the estimates of the hanger tension and its uncertainty is investigated.

Keywords

Acknowledgement

Supported by : European Social Fund (ESF), Greek National Resources

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