Bridge Health Monitoring

Bridge health monitoring aims to assess and predict the structural integrity of bridges using various data sources and advanced analytical techniques, ultimately preventing failures and improving safety. Current research heavily utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and neural operators, to analyze sensor data (e.g., accelerometers, strain gauges) and visual inspections (e.g., from UAVs) for damage detection, load estimation, and anomaly identification. These data-driven approaches offer improved efficiency and accuracy compared to traditional methods, leading to more cost-effective and timely maintenance strategies for critical infrastructure.