Statistical Identification Of A Prestressed Concrete Beam With Unbonded Tendons Using Modal Data
Submitted | 2012-01-18 |
Last Update | 2012-01-18 |
Title | Statistical 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 Author | Author #1 Alt Email: hamid_abdulsalam@hotmail.com Telephone: |
Keywords | Prestressed beam, Statistical identification, Model updating, Estimation methods, Finite element model. |
Abstract | An 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. |
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