Deep learning-based structural health monitoring

This article provides a comprehensive review of deep learning-based structural health monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and applications including nondestructive approaches; computer vision-based methods, digital twins, unmanned aerial vehicles (UAVs), and their integration with DL; vibration-based strategies including sensor fault and data recovery methods; and physics-informed DL approaches. Connections between traditional machine learning and DL-based methods as well as relations of local to global approaches including their extensive integrations are established. The state-of-the-art methods, including their advantages and limitations are presented. The review draws…