Damage Detection

Damage detection research focuses on automatically identifying structural flaws using various data sources, primarily aiming to improve safety and reduce maintenance costs across diverse applications. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), autoencoders, generative adversarial networks (GANs), and transformers, often combined with techniques like transfer learning and attention mechanisms to enhance accuracy and efficiency, particularly in handling noisy or incomplete data. These advancements are significant for improving structural health monitoring (SHM) in civil engineering, cultural heritage preservation, and industrial settings, enabling more proactive and data-driven maintenance strategies.