Lab-to-field integration in bridge monitoring: a hybrid structural health monitoring framework employing deep learning and unmanned aerial vehicle imagery

Recent advances in unmanned aerial vehicles (UAVs) and computer vision have opened new frontiers for automated structural health monitoring (SHM) of bridges. Nonetheless, most existing vision-based approaches rely solely on static field images or synthetic datasets, limiting their ability to capture the progression of damage and generalize to real-world conditions. To overcome this drawback, this study presents a hybrid SHM framework that integrates UAV-based visual inspection with a computer vision method trained on both laboratory and field imagery data. Sequential crack images are collected from laboratory tests on concrete beams subjected to progressive shear loading, while UAV-captured images from in-service bridges are incorporated to capture real-world visual variability present in operational environments. The computer vision-based method integrates a convolutional neural network (CNN) architecture with a long short-term memory (LSTM) network. The novelty of this study lies in bridging laboratory sequential data with UAV-captured field imagery through a CNN-LSTM model, enabling lab-to-field integration for SHM. To quantify the impact of temporal learning, the CNN-LSTM model is benchmarked against purely spatial baselines, such as CNN-only model and a CNN with a MobileNet, demonstrating the benefits achieved when temporal learning is incorporated. The proposed CNN–LSTM framework consistently outperformed both baseline configurations, achieving superior accuracy and robustness using laboratory and field datasets. This lab-to-field integration improves the SHM framework’s robustness to environmental noise and provides a scalable path for real-world SHM deployments using UAVs, even in scenarios lacking high-frequency temporal data.