Sequential stochastic identification of elastic constants using Lamb waves and particle filters
Sequential stochastic identification of elastic parameters of thin aluminum plates using Lamb waves is proposed. The identification process is formulated as a Bayesian state estimation problem in which the elastic constants are the unknown state variables. The comparison of a sequence of numerical and pseudo-experimental fundamental dispersion curves is used for an inverse analysis based on particle filter to obtain sequentially the elastic constants. The proposed identification procedure is illustrated by numerical experiments in which the elastic parameters of an aluminum thin plate are estimated. The results show that the proposed approach is able to identify the unknown elastic constants sequentially and that this approach can be also useful for the quantification of uncertainty with respect to the identified parameters.