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.

贝叶斯有限元模型更新已成为结构健康监测的重要工具。然而，使用贝叶斯推理方法更新有限元模型需要大量的计算成本。近年来，代理建模技术由于能够加速贝叶斯推理的计算而受到广泛关注。本研究引入了两种新的贝叶斯推理代理模型。具体来说，利用径向基函数神经网络和全连接神经网络为棘手的似然函数构建代理模型，避免了蒙特卡洛采样过程中重复调用有限元模型的巨大计算成本。选择混凝土框架的全尺寸数值模拟和六层钢框架实验作为案例研究。将训练好的代理模型用于贝叶斯模型更新，并将更新结果与直接使用有限元模型评估获得的结果进行比较。与使用直接有限元评估获得的那些相比，使用经过训练的代理模型获得的有限元模型参数的后验分布足够准确。此外，使用代理模型进行有限元模型更新大大降低了计算成本。