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 on current literature on the topic, also providing a synergistic analysis leading to the understanding of the evolution of DL as a basis for presenting the future research and development needs. Our overall finding is that despite the rapid progression of digital technology along with the progression of DL, the DL-based SHM appears to be in its infant stages with enormous potential for future developments to bring the SHM technology to a common practical use with wide scope applications, performance reliability, cost, and degree of automation. It is anticipated that this review paper will serve as a basic resource for readers seeking comprehensive and holistic understanding of the subject matter.