The use of fiber reinforced plastics (FRPs) is on the rise in aerospace and other high-tech fields

owing to their high specific strength/stiffness

design flexibility

and corrosion resistance. Structural health monitoring (SHM) is therefore crucial to ensure structural safety. Recently

machine learning has emerged as a promising approach for SHM of FRPs. Data-driven methods that employ machine learning algorithms have shown higher accuracy

robustness

and efficiency in SHM. After the brief introduction of the machine learning algorithms commonly used in SHM of FRPs

the applications of machine learning in SHM are reviewed mainly from the following aspects: Damage pattern recognition

damage localization

and damage degree recognition of FRPs. Finally

the future development trend of SHM based on machine learning in FRPs are discussed.