Active transfer learning for structural health monitoring

A new approach to structural health monitoring (SHM) is gaining attention as it tackles the challenges of obtaining labeled data, which can be costly and impractical. This method, known as population-based SHM, utilizes data from various structures to improve model accuracy. By addressing the issue of distinct data distributions across different structures, this innovative technique aims to reduce generalization errors that often plague traditional machine learning methods. This advancement is significant as it could lead to more reliable monitoring systems, ultimately enhancing safety and maintenance in engineering.