A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications.

翻译：本研究提出了一种新颖的贝叶斯结构模型更新框架。该方法利用多模态变分自编码器（VAE）的代理单模态编码器，在处理少量观测数据时能够有效近似似然函数。该方法特别适用于适用于各类动态分析模型的高维相关同步观测。通过采用带有加速度与动态应变测量的单层框架建筑数值模型对所提方法进行了基准测试。此外，以一个三自由度集中质量模型的非线性参数贝叶斯更新为例，证明了该方法在保持实际应用所需精度的同时，相较于使用原始VAE具有更高的计算效率。