Data-driven Crack Detection in the Realm of Structural Health Monitoring: An Overview*

The advancements of sensor technologies, improvements in computational power, and massive amounts of data from the increasing number of sensors deployed on structures have augmented the need for data-driven techniques in structural health monitoring (SHM), particularly for fatigue crack detection. Data-driven methods and tools are increasingly used in analyzing structures to ensure the absence of any fatigue crack in various engineering fields including civil, mechanical, aerospace, and maritime engineering. This overview aims to present the most recent research that utilizes data-driven methods for fatigue crack detection in engineering structures that fall within the realms of signal processing (SP) and machine learning (ML). It focuses on studies utilizing Deep Learning (DL) for crack detection, highlighting the importance of Transfer Learning (TL).