Bayesian Finite Element Model Updating Using Static and Dynamic Data
Finite element models of current structures often behave differently than the structure itself. Model updating techniques are used to enhance the capabilities of the numerical model such that it behaves like the real structure. Experimental data is used in model updating techniques to identify the parameters of the numerical model. In civil infrastructure these model updating techniques use either static or dynamic measurements, separately. This paper studies how a Bayesian updating framework behaves when both static and dynamic data are used to updated the model. Displacements at specific structure locations are obtained for static tests using a computer vision method. High density mode shapes and natural frequencies are obtained using a moving accelerometer structure. The static data and the modal characteristics are combined in a Bayesian modal updating technique that accounts for the incompleteness and uncertainty of the data as well as the possible nonuniqueness of the solution. Results show how the posterior probability density function changes when different type of information is included for updating.
KeywordsBoris Cali Colombia
Unable to display preview. Download preview PDF.
- 1.. Beck, Jim L., and Lambros S. Katafygiotis. 1998. Updating Models and Their Uncertainties. I: BayesianStatistical Framework. Journal of Engineering Mechanics 124 (4):455–461.Google Scholar
- 3.Caicedo, J. M. 2005. Displacement measurements in civil structures using digital cameras and lasers. In XXIIIInternational Modal Analysis Conference. Orlando, Florida: SEM.Google Scholar
- 4.Wieger, G. R., and J. M. Caicedo. 2008. Displacement Records of Civil Structures. In ASCE InauguralInternational Conference of the Engineering Mechanics Institute. Minneapolis, Minnesota.Google Scholar
- 5.Wieger, G. R. 2009. Development and Verification of a Computer Vision Technique to Measure the Responseof civil Structures, Department of Civil and Environmental Engineering, University of South Carolina,Columbia.Google Scholar
- 7.Balmes, E. 2000. Review and evaluation of shape expansion methods. In IMAC XVIII : a conference onstructural dynamics. San Antonio, Texas.Google Scholar
- 9.Marulanda A., Johannio, and J. M. Caicedo. 2008. Modal Identification Using a Smart Mobile Sensor. In ASCEInaugural International Conference of the Engineering Mechanics Institute. Minneapolis, Minnesota.Google Scholar
- 10.James, George H., Thomas G. Carne, James P. Lauffer, and Arlo R. Nord. 1992. Modal testing using naturalexcitation. In 10th International Modal Analysis Conference. San Diego, CA.Google Scholar