Nondestructive evaluation (NDE) and structural health monitoring (SHM) of civil infrastructures usually deal with an extensive amount of data obtained from the sensors employed/deployed. For example, ground-penetrating radar (GPR) technology utilizes antennas to collect a large number of A-scans for concrete bridge deck or high-definition cameras may be used to measure the physical parameters of structures such as the displacement, strain/stress, rotation, vibration, crack, and spalling. While most of such data have conventionally been analyzed by experts in each technology, many studies are being conducted to automate the data analysis using machine learning/artificial intelligence algorithms. In an effort to assemble those studies, MDPI’s Infrastructures journal has proposed and organized this Special Issue. To be specific, this Special Issue will publish study results and research papers that present innovative uses of machine learning/artificial intelligence for processing NDE/SHM data. Additionally, it also encourages papers that provide comprehensive reviews of the literature on this topic.

Keywords: machine learning; artificial intelligence; non-destructive testing; non-destructive evaluation; structural health monitoring.