Bayesian Structural Model Updating with Multimodal Variational Autoencoder

• URL: http://arxiv.org/abs/2406.09051v2
• Date: Thu, 20 Jun 2024 07:47:28 GMT
• Title: Bayesian Structural Model Updating with Multimodal Variational Autoencoder
• Authors: Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee, Taro Yaoyama,
• Abstract summary: The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE) It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models.
• Score: 2.4297252937957436
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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