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Statistical Identification Of A Prestressed Concrete Beam With Unbonded Tendons Using Modal Data

Submitted2012-01-18
Last Update2012-01-18
TitleStatistical Identification Of A Prestressed Concrete Beam With Unbonded Tendons Using Modal Data
Author(s)Author #1
Author title:
Name: Hamid Al-Ani
Org: Associate Professor, Department of Civil Engineering, Al-Zaytoona Private University of Jordan, Amman, Jordan,
Country:
Email: hamid_abdulsalam@hotmail.com

Author #2
Author title:
Name: Walid M. Hasan
Org: Assistant Professor, Department of Civil Engineering, Al-Isra University, Amman, Jordan,
Country:
Email: walid.hasan@ iu.edu.jo

Author #3
Author title:
Name: Moh'd El-Khatieb
Org: Assistant Professor, Department of Civil Engineering, Al-Isra University, Amman, Jordan
Country:
Email:

Other Author(s)
Contact AuthorAuthor #1
Alt Email: hamid_abdulsalam@hotmail.com
Telephone:
KeywordsPrestressed beam, Statistical identification, Model updating, Estimation methods, Finite element model.
AbstractAn iterative statistical identification method, based on Bayesian approach, was used to identify the actual stiffness and prestressing force of a prestressed simply supported beam with unbonded curved tendons. A finite element model, with consistent mass matrix, was used as analytical model and the first three natural frequencies of the beam were used as experimental modal parameters. Because the procedure involves inversion of matrices, the ill-conditioning of the problem was also investigated. The aim of this paper is to identify a reliable model of a prestressed beam which represents very well the real structure by identifying the stiffness parameters and the prestressing force. This model can be used, then, as a reference model to detect damage or loss of prestressing force. It was seen that the accuracy of the identified parameters and the rate of convergence are highly influenced by the coefficients of variation assigned to the various parameters. The effect of the uncertainties associated with the physical and experimental parameters on the accuracy of the identification results was illustrated by some graphics and tables. Other graphics and tables show the utility of the improved statistical identification method to accelerate the convergence of the identified parameters.
Paperview paper 2308.pdf (301KB)

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