Assessing random field models in finite element analysis: a case study

The buckling load of thin-walled structures is sensitive to the presence of imperfections. Random field models constitute an established approach to accounting for these imperfections in numerical computations. Current evaluations of finite element models including random fields are usually done by comparison with the experimental buckling loads. This paper proposes an alternative approach to account for discrepancies inherent to numerical modelling. We use this to judge the shape of fitted correlation functions and the sensitivity of the computed buckling load with respect to the number of eigenvectors included in the expansion of the random field. Further, the applicability of fitted model covariance functions is contrasted with a principal component analysis approach as well as with artificially created local random imperfections. The results show that random field models have to be used with greatest care in order to avoid erroneous predictions of the buckling load.