Computational procedure for a fast calculation of eigenvectors and eigenvalues of structures with random properties

The inherent uncertainties in geometry, material properties, etc. of engineering structures can be represented by stochastic models, where the parameters are described by probabilistic laws. Results from any analysis based on stochastic models inherit probabilistic information as well, which can be used, e.g., for reliability analysis. Particularly in linear dynamics of structures the calculation and analysis of random eigenvalues and eigenvectors is crucial. A very flexible, however computationally intensive way to analyze such systems is direct Monte Carlo simulation (MCS). In this paper procedures are shown, which allow a significant reduction of computational efforts of the simulation using a subspace iteration scheme with “optimally” selected start-vectors. The procedures are not restricted to any kind of sampling technique.