9 papers
Finite element model updating of building structures under seismic excitation: A parallelized latent space-based Bayesian framework
Taro Yaoyama, Sangwon Lee, Minoru Matsubara +3
Enhancing seismic fragility and risk assessment of nuclear power plants relies on accurate prediction of reactor building responses to seismic hazards, which can be further improve…
Hierarchical Bayesian model updating using Dirichlet process mixtures for structural damage localization
Taro Yaoyama, Tatsuya Itoi, Jun Iyama
Bayesian model updating provides a rigorous probabilistic framework for calibrating finite element (FE) models with quantified uncertainties, thereby enhancing damage assessment, r…
Transfer Learning-Based Surrogate Modeling for Nonlinear Time-History Response Analysis of High-Fidelity Structural Models
Keiichi Ishikawa, Yuma Matsumoto, Taro Yaoyama +2
In a performance based earthquake engineering (PBEE) framework, nonlinear time-history response analysis (NLTHA) for numerous ground motions are required to assess the seismic risk…
Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models
Yuma Matsumoto, Taro Yaoyama, Sangwon Lee +2
In probabilistic seismic hazard analysis (PSHA), the exceedance probability of a ground-motion intensity measure (IM) is typically evaluated. However, in recent years, dynamic resp…
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…
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…