Inferring structural variability using modal analysis in a Bayesian framework

Abstract

Dynamic systems with different geometric configurations may present remarkable distinct dynamic behaviour. However, variability is identifiable in the measured modal shapes and modal frequencies. This paper explores a Bayesian framework in order to infer structural variability based on modal parameters. This is relevant in cases of difficult access for inspection in finished products/structures. An approach using a radial basis neural network benchmarked by a Gaussian process meta model is developed and then followed by a test case with experimental data. It is concluded that the proposed methodology shows promise in solving this kind of problems.