Multiresolution Bayesian nonparametric general regression for structural model updating

A novel Bayesian method, namely, multiresolution Bayesian nonparametric general regression (MR-BNGR), is proposed for structural model updating using modal data, that is, identified natural frequencies and mode shapes. In this method, the model updating problem is posed as a non-linear regression problem from the modal data to the structural parameters. The proposed method is nonparametric, so it does not require an explicit functional form of this mapping. Instead, it utilizes the input-output data to adaptively model its relationship. Its multiresolution nature allows to zoom into the significant region in stages to search the optimal point. Furthermore, the estimation uncertainty can be quantified. Training of the MR-BNGR network is very straightforward and computationally economical. Examples of a 20-storey shear building and a three-dimensional truss are provided to demonstrate the capabilities of the proposed MR-BNGR method, and the results confirm the effectiveness of this novel method.