A Data Fusion Method for Small Sample Model Testing and Finite Element Simulation: Taking π-Shaped Beam Column Nodes as an Example

Abstract

The analysis and research of composite structure specimens depend on test methods. However, due to the high cost, complex test conditions, time-consuming, and other problems, it is difficult to carry out a large number of tests. A large amount of data is often required for parametric analysis and structural optimization of composite structure specimens. Therefore, to solve the problem of insufficient data samples in the analysis and research of specimens. In this paper, a finite element model updating method based on Bayesian theory and a Gaussian process data fusion method is proposed, that is, the amount of data is expanded by the proposed model updating method, and then the experimental and numerical simulation data are fused based on the Gaussian process data fusion method. Finally, the effectiveness of the proposed model updating method and data fusion method is verified by a numerical example of π type beam-column joints. The results show that the method has high generalization ability and prediction accuracy in the case of small samples through the fusion of numerical simulation and experimental data.