Employing vision-based sensing for long-term monitoring

Abstract

Despite the crucial role of structural health monitoring (SHM), traditional contact-based sensors are costly and lacking automation. Vision-based sensors have recently emerged as a potential substitute, due to their non-contact nature and low cost. However, investigations lack validating their long-term performance. This study assesses vision-based sensing for robust and efficient long-term displacement monitoring. Additionally, the suitability of various machine learning (ML) algorithms in automatically extracting information from the generated data are examined. A first experiment examines the capability to monitor ambiently excited structures over long periods. The parameters assessed are: a) noise and drift (e.g., related to environmental changes such as temperature, humidity, and lighting); and b) obtainable displacement resolution related to ambient excitation. Then, a second experiment examines the capability to monitor dynamically excited structures over long periods (via the employment of an artificial excitation, e.g., an eccentrically loaded mass motor). Here, the following parameters assessed are the: a) frequency bandwidth range; b) frequency resolution; c) displacement resolution related to dynamic excitation; and d) maximum number monitored points for given computational resources. Finally, for processing the generated data, the following ML algorithms are examined: a) Artificial Neural Networks (ANNs); b) support vector machines (SVMs); c) decision trees; and d) Convolutional Neural Networks (CNNs). Concerning long-term monitoring, the paper found that non-contact sensing had comparable accuracy with traditional contact-based sensors. About the ML models, the CNNs were found to be advantageous. Whilst this study was carried out on a small-scale structure, the potential of vision-based sensing for long-term monitoring of full-scale structures was highlighted.