18 citations · 47 across the 10 of their papers we have counts for
6 papers · 1 filter
Latent Space-based Stochastic Model Updating
Sangwon Lee, Taro Yaoyama, Masaru Kitahara +1
Model updating of engineering systems inevitably involves handling both aleatory or inherent randomness and epistemic uncertainties or uncertainities arising from a lack of knowled…
Bayesian Structural Model Updating with Multimodal Variational Autoencoder
Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee +1
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 autoen…
Site-Specific Ground Motion Generative Model for Crustal Earthquakes in Japan Based on Generative Adversarial Networks
Yuma Matsumoto, Taro Yaoyama, Sangwon Lee +2
We develop a site-specific ground-motion model (GMM) for crustal earthquakes in Japan that can directly model the probability distribution of ground motion acceleration time histor…
Latent Space-Based Likelihood Estimation Using a Single Observation for Bayesian Updating of a Nonlinear Hysteretic Model
Sangwon Lee, Taro Yaoyama, Yuma Matsumoto +2
This study presents a novel approach to quantifying uncertainties in Bayesian model updating, which is effective in sparse or single observations. Conventional uncertainty quantifi…
Probabilistic Model Updating of Steel Frame Structures Using Strain and Acceleration Measurements: A Multitask Learning Framework
Taro Yaoyama, Tatsuya Itoi, Jun Iyama
This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural…
Stress Resultant-Based Approach to Mass Assumption-Free Bayesian Model Updating of Frame Structures
Taro Yaoyama, Tatsuya Itoi, Jun Iyama
Bayesian model updating facilitates the calibration of analytical models based on observations and the quantification of uncertainties in model parameters such as stiffness and mas…