Structural Damage

Structural damage research focuses on developing methods for detecting, localizing, and predicting damage in various structures, primarily to improve safety and extend lifespan. Current research emphasizes the use of digital twins, sensor data analysis with machine learning algorithms (including convolutional neural networks and autoencoders), and data-driven approaches like semi-supervised learning to achieve automated and proactive damage detection. These advancements are crucial for improving infrastructure maintenance, reducing costs associated with repairs and failures, and enhancing risk assessment strategies across diverse engineering applications.