  Monitoring structural damage is extremely important for sustaining and
preserving the service life of civil structures. While successful monitoring
provides resolute and staunch information on the health, serviceability,
integrity and safety of structures; maintaining continuous performance of a
structure depends highly on monitoring the occurrence, formation and
propagation of damage. Damage may accumulate on structures due to different
environmental and human-induced factors. Numerous monitoring and detection
approaches have been developed to provide practical means for early warning
against structural damage or any type of anomaly. Considerable effort has been
put into vibration-based methods, which utilize the vibration response of the
monitored structure to assess its condition and identify structural damage.
Meanwhile, with emerging computing power and sensing technology in the last
decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have
become more feasible and extensively used in vibration-based structural damage
detection with elegant performance and often with rigorous accuracy. While
there have been multiple review studies published on vibration-based structural
damage detection, there has not been a study where the transition from
traditional methods to ML and DL methods are described and discussed. This
paper aims to fulfill this gap by presenting the highlights of the traditional
methods and provide a comprehensive review of the most recent applications of
ML and DL algorithms utilized for vibration-based structural damage detection
in civil structures.
