Computational aspects of updating contact interface models in assembled structures

Stochastic model updating of joints is a common solution for realistic modelling of built-up structures. However, detailed large-scale models render the simulations to be computationally expensive. This issue is addressed in the following two ways. The first approach involves stochastic decomposition of the functional space by machine learning techniques such as Gaussian processes, artificial neural networks, generalized radial basis neural networks and support vector machines. The second approach comprises a more physics-based strategy which involves reducing the internal degrees of freedom of the sub-components using the fixed-interface component mode synthesis method. The assembly problem is performed on two beam structures connected by a bolted lap joint. Modal testing is performed on nominally identical beam structures to account for manufacturing variability. The results obtained have been validated and the good performance achieved by the proposed reduced models demonstrates their robustness.