Author(s): Thomas Munro, Sebastien Fregeau, Ella Hansen, Lucca Coelho, Rodrigo Armaza, Efe Sezer, Efe Kaya
Mentor(s): Masoud Malekzadeh
Institution SUU
Cameras have become essential tools in a variety of applications, including traffic monitoring, security surveillance, and structural health monitoring. By combining high-speed cameras with advanced computer vision techniques, we can push the boundaries of what is possible in real-time vibration monitoring of structures. This research explores the intersection of structural health monitoring and computer vision, aiming to create a system capable of detecting and analyzing movement in structures in real-time. In our study, we use a high-speed camera to capture detailed video footage of small-scale structures under different stress conditions. Using computer vision algorithms, specifically point-tracking methods, we can accurately detect even minute movements or vibrations. These movements are then analyzed to gather information about the structure's response to applied loads or environmental factors, such as wind or vibrations from nearby machinery. The ability to continuously monitor structural behavior in real-time offers significant advantages. It allows for the early detection of potential structural issues, providing critical information that can be used to predict maintenance needs or identify points of concern before they develop into major problems. For instance, unusual or excessive movement detected by the system could indicate weakening or damage, prompting immediate inspection and intervention. This proactive approach to structural health monitoring can enhance the safety and longevity of critical infrastructure. Our project focuses on testing and refining these capabilities on various small-scale structural models, allowing us to validate the effectiveness of our method in controlled settings. By applying computer vision techniques such as optical flow and feature tracking, we aim to develop a robust system that can operate reliably in different conditions. The ultimate goal is to extend this technology to real-world applications, where it could be used in bridges, buildings, and other structures to provide real-time feedback on their health and stability. This research highlights the potential for integrating computer vision into structural health monitoring systems, offering a new level of insight into how structures respond to external stresses. With further development, this technology could become a vital tool in the field of civil engineering, contributing to safer, more resilient infrastructure.