This article provides a comprehensive review of deep learning-based structural health
monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and …

Structural damage identification has received considerable attention during the past
decades. Although several reviews have been presented, some new developments have …

Structural health monitoring (SHM) techniques have been widely used in long-span bridges.
However, due to limitations of computational ability and data analysis methods, the …

This study presents a comprehensive review of the history of research and development of
different damage-detection methods in the realm of composite structures. Different fields of …

This article proposes an unsupervised deep learning–based approach to detect structural
damage. Supervised deep learning methods have been proposed in recent years, but they …

Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …

In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …

Structural damages occur in modern structures during operations due to environmental and
human factors. The damages accumulating with time may lead to a significant decrease in …

Track geometry is one of the critical indicators of railway tracks' condition which requires
continuous monitoring and maintenance over time. In this paper, a novel artificial …

This paper proposed a modified faster region‐based convolutional neural network (faster R‐
CNN) for the multitype seismic damage identification and localization (ie, concrete cracking …