144 citations · 427 across the 10 of their papers we have counts for
10 papers
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…
Recovering compressed images for automatic crack segmentation using generative models
Yong Huang, Haoyu Zhang, Hui Li +1
In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essent…
Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction
Fangqiao Hu, Jin Zhao, Yong Huang +1
A learning-based 3D reconstruction method for long-span bridges is proposed in this paper. 3D reconstruction generates a 3D computer model of a real object or scene from images, it…
Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment
Yong Huang, James L. Beck, Hui Li
The focus in this paper is Bayesian system identification based on noisy incomplete modal data where we can impose spatially-sparse stiffness changes when updating a structural mod…
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals
Yong Huang, James L. Beck, Stephen Wu +1
The application of compressive sensing (CS) to structural health monitoring is an emerging research topic. The basic idea in CS is to use a specially-designed wireless sensor to sa…
Hierarchical sparse Bayesian learning: theory and application for inferring structural damage from incomplete modal data
Yong Huang, James L. Beck
Structural damage due to excessive loading or environmental degradation typically occurs in localized areas in the absence of collapse. This prior information about the spatial spa…
Robust Bayesian compressive sensing for signals in structural health monitoring
Yong Huang, James L. Beck, Stephen Wu +1
In structural health monitoring (SHM) systems, massive amounts of data are often generated that need data compression techniques to reduce the cost of signal transfer and storage.…
Hierarchical sparse Bayesian learning for structural health monitoring with incomplete modal data
Yong Huang, James L. Beck
For civil structures, structural damage due to severe loading events such as earthquakes, or due to long-term environmental degradation, usually occurs in localized areas of a stru…