Structural Health Monitoring

Structural Health Monitoring (SHM) aims to assess the condition of structures, such as bridges and wind turbines, by analyzing sensor data to detect damage early and prevent catastrophic failures. Current research emphasizes the use of machine learning, particularly deep learning architectures like convolutional neural networks, autoencoders, generative adversarial networks, and transformers, often integrated with physics-based models (e.g., using physics-informed neural networks or Gaussian processes) to improve accuracy and robustness. These advancements are significant because they enable more efficient, cost-effective, and reliable monitoring, leading to improved safety, reduced maintenance costs, and extended lifespan of critical infrastructure.