5 papers
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Qi Li, Yong Huang, Hui Li
Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study proposes a prediction deviation-bas…
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
Teng Li, Stephen Wu, Yong Huang +2
The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from meas…
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
Xianghao Meng, James L. Beck, Yong Huang +1
In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monito…
MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
Xianghao Meng, Yong Huang, James L. Beck +2
Over decades, Markov chain Monte Carlo (MCMC) methods have been widely studied, with a typical application being the quantification of posterior uncertainties in Bayesian system id…
SDIGLM: Leveraging Large Language Models and Multi-Modal Chain of Thought for Structural Damage Identification
Yunkai Zhang, Shiyin Wei, Yong Huang +3
Existing computer vision(CV)-based structural damage identification models demonstrate notable accuracy in categorizing and localizing damage. However, these models present several…