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.

中文翻译：

---

使用多模态变分自动编码器更新贝叶斯结构模型


本研究提出了贝叶斯结构模型更新的新颖框架。所提出的方法利用多模态变分自动编码器（VAE）的代理单模态编码器。当处理少量观测值时，该方法有助于近似似然性。它特别适合适用于各种动态分析模型的高维相关同步观测。所提出的方法使用单层框架建筑的数值模型进行了基准测试，并进行了加速度和动态应变测量。此外，涉及三自由度集总质量模型的非线性模型参数的贝叶斯更新的示例证明了与使用原始 VAE 相比的计算效率，同时保持了实际应用的足够精度。