Active learning aided Bayesian nonparametric general regression for model updating using modal data

Abstract

In this paper, we propose an active learning aided Bayesian nonparametric general regression (ALBNGR) network for structural model updating using modal data. The proposed network provides an approximate, nonlinear and nonparametric mapping from the modal data to the structural parameters. This serves as a surrogate model for the natural frequencies and mode shapes of a finite element model. To further reduce the number of finite element model evaluations, the proposed method adopts an active learning aided sequential modeling strategy to improve the local accuracy of the surrogate model. Active learning assists in enriching the dataset using gradient descent, with the gradient vector calculated using analytical expressions. The training dataset is updated iteratively and then sequential surrogate models are constructed but only the model in the initial round is trained to obtain the optimal scaling parameter. It is reused in the subsequent rounds to refine the surrogate model along with the updated dataset. Therefore, the efficiency of the active learning aided sequential modeling process can be enhanced. The effectiveness and advantages of the proposed method are demonstrated through the application of two simulated examples and an experimental case of the Canton Tower.