Efficient Bayesian inference for finite element model updating with surrogate modeling techniques

Bayesian finite element model updating has become an important tool for structural health monitoring. However, it takes a large amount of computational cost to update the finite element model using the Bayesian inference methods. The surrogate modeling techniques have received much attention in recent years due to their ability to speed up the computation of Bayesian inference. This study introduces two new surrogate models for Bayesian inference. Specifically, the radial basis function neural networks and fully-connected neural networks are used to construct surrogate models for the intractable likelihood function, avoiding the enormous computational cost of repeatedly calling the finite element model in the Monte Carlo sampling process. A full-scale numerical simulation of a concrete frame and a six-story steel frame experiment were selected as case studies. The trained surrogate models were used for Bayesian model updating, and the updated results were compared with the results obtained directly using the finite element model evaluation. The posterior distributions of the finite element model parameters obtained using the trained surrogate models are sufficiently accurate compared to those obtained using direct finite element evaluation. In addition, using surrogate models for finite element model updating greatly reduces computational costs.