On Construction of Uncertain Material Parameter using Generalized Polynomial Chaos Expansion from Experimental Data

The knowledge of uncertain parameter distributions is often required to investigate any typical stochastic problem. It may be possible to directly measure uncertain parameters but this is often quite easier to identifying these parameters from system outputs by solving an inverse problem. In this paper, a robust and efficient inverse method based of the non–sampling technique, i.e. generalized polynomial chaos expansion, is presented to identifying uncertain elastic parameters from experimental modal data. We review the general polynomial chaos theory and relating issues for uncertain parameter identification. An application is presented in which the elastic parameters of orthotropic plates are identified from the modal data. The distribution functions of uncertain parameters are derived from experimental eigen–frequencies via an inverse stochastic problem. The Pearson model is used to identify the type of density functions. This realization then is employed to construct random orthogonal basis for each uncertain parameter.