As climate change is exacerbated and existing resources are depleted, the need for
sustainable industries becomes ever so important. Aviation is not an exception. Despite the …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge
from a labeled source domain to a new unlabeled target domain. Most existing methods …

Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …

The practical application of structural health monitoring is often limited by the availability of
labelled data. Transfer learning–specifically in the form of domain adaptation (DA)–gives …

Bridge damage detection methods based on moving load induced response and deep
learning are popular, and they are effective when the training data generated by a finite …

Neural network-based strategies require balanced training datasets to avoid creating
unreliable classification and prediction models. While these strategies are commonly used …

Long multi-span bridges represent a broad section of the civil roadway infrastructure.
Despite being so common, their condition-based maintenance through vibration-based …

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced
efficiency, automation, accuracy, cost savings, and independence in maintenance …

To achieve accurate damage localization for CFRP structures in various scenarios with
limited sample sizes, this paper proposes a novel transfer learning strategy called the …