To incorporate uncertainty in structural analysis knowledge of the uncertainty in the model parameters is required. This paper describes efficient techniques to identify and quantify variability in the parameters from experimental data by maximising the likelihood of the measurements, using the well-established Monte Carlo or perturbation methods for the likelihood computation. These techniques are validated numerically and experimentally on a cantilever beam with a point mass at an uncertain location. Results show that sufficient accuracy is attainable without a prohibitive computational effort. The perturbation approach requires less compuation but is less accurate when the response is a highly nonlinear function of the parameters.
|Translated title of the contribution||Uncertainty identification by the maximum likelihood method|
|Pages (from-to)||587 - 599|
|Number of pages||13|
|Journal||Journal of Sound and Vibration|
|Publication status||Published - Dec 2005|