To improve the performance, durability and efficiency of composites structures, an exact knowledge of elastic and damping parameters is required. Such parameters exhibit significant uncertainties which arise from the variable characteristics of the individual layers, their interconnection and the manufacturing process. These parameters are normally represented as random variables/fields to present these uncertainties in numerical models, e.g. in a FEM model. As a tradition, the sampling-based, e.g. Monte Carlo, methods are applied for uncertainty quantification where obtaining trustable results requires large number realizations of uncertain parameters. Non-Sampling methods, in contrast, requires few realizations of parameters and accordingly very efficient in term of computational time. This work employs the non-sampling methods, e.g. generalized polynomial chaos and the Karhunen-Loève expansions to represent and identify damping and elastic material parameters of composite structures. To this end, the parameters are represented by truncated expansions with deterministic coefficients and random orthogonal basis. The unknown coefficients are estimated from experimental modal data for samples of composite plates. As well as the expansions are known, they can be combined with FE model to realize the random structural responses. The method is proven to yield closer predictions to the experimental data.     «

To improve the performance, durability and efficiency of composites structures, an exact knowledge of elastic and damping parameters is required. Such parameters exhibit significant uncertainties which arise from the variable characteristics of the individual layers, their interconnection and the manufacturing process. These parameters are normally represented as random variables/fields to present these uncertainties in numerical models, e.g. in a FEM model. As a tradition, the sampling-based, e...     »