Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 10; p. 2778
• Main Authors: Azimi, Mohsen, Eslamlou, Armin, Pekcan, Gokhan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.05.2020; MDPI
• Subjects: Algorithms; Artificial intelligence; Bridges; Cameras; Computer peripherals; crack detection; damage detection; Data science; Datasets; Deep learning; Infrastructure; Machine learning; Medical research; Neural networks; Physics; Science; Sensors; Software; structural health monitoring; Unmanned aerial vehicles; United States > US; deep learning; data science; computer vision; damage detection; structural health monitoring; machine learning; crack detection

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 p...