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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers withdoi:10.3390/s20102778 pmid:32414205 pmcid:PMC7294417 fatcat:sl3t5pxt3rhy3adzjgziumvmia