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…
Ensuring the long-term integrity and health of bridge structures under diverse structural, environmental, and operational conditions remains a persistent challenge within the structural health monitoring (SHM) community. Although machine learning–aided unsupervised anomaly detect…
Climate change has become a critical challenge for maintenance and functionality of civil structures. Apart from global warming, climate change-induced frost periods can seriously affect dynamic behaviour of bridges. From a meteorological perspective, instability in the polar vor…
Dam displacement monitoring is imperative to assess the operational status and structural safety of dams under various environmental conditions and operational loads. Although most of the dam structures are instrumented with robust in-situ sensing systems, long-term field monitor…