Review highlights deep learning approaches improving structural health monitoring in critical infrastructure, emphasizing challenges and future trends.
Vibration-based damage detection methods, leveraging structural response data to identify the structural damage, plays an important role for structural health monitoring, particularly for critical infrastructure like bridges and high-rise buildings. In recent years, deep learning, which can autonomously learn damage-sensitive features and classify damage from complex datasets, has been widely used in structural damage detection. This paper reviews deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders applied in vibration-based structural damage detection for civil structures over the past ten year, highlighting their advantages and disadvantages. The challenges and future trends for vibration-based damage detection by using deep learning are summarized.
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LI et al. (2025) studied this question.