semantic_scholare-Journal of Nondestructive Testing2026-08-01
Unsupervised Deep Learning for Enhanced Damage Detectability with Small Vibration Data
Wenmiao Gao, Zheng-Han Chen, Alireza Entezami, Hassan Sarmadi
TL;DR: An unsupervised deep learning methodology that integrates generative and discriminative models for enhanced damage detectability under small vibration data conditions is proposed and demonstrates the ability to enhance data diversity, improve class separability, and increase the sensitivity of damage indicators to structural damage.
Bridges, as critical components of transportation networks, demand reliable structural health monitoring (SHM) programs that enable quantitative assessment of their structural states and long-term performance under varying environmental and loading conditions. However, in many pr…