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Dynamic Monitoring of a Steel Footbridge Based on Computer Vision Techniques
The field of Structural Health Monitoring (SHM) aims to manage the built heritage, buildings and infrastructures, to first and foremost avoid collapse, detect early sign of damage and plan maintenance. As regards bridges and footbridges, structural health conditions are tightly coupled with modal properties, attainable from structural vibrations. The assessment of vibrations is traditionally obtained with measuring systems composed of sensors directly installed on the structure. Despite the large diffusion of these systems, their use may be unfeasible due to the necessity of an expensive and time-consuming installation of the acquisition system. A promising approach to characterize the footbridge dynamic behavior is represented by computer vision-based techniques, that require the installation of only one or more cameras together with, if necessary, some targets on the monitored structure. The vision-based approach is totally non-invasive, low-cost and enables the direct measurement of structural displacements. The aim of the present paper is to investigate the potential of vision-based techniques for the dynamic identification of structures, analyzing some preliminary results of dynamic tests performed on a steel footbridge. Structural vibrations caused by a jumping pedestrian were acquired from a camera as well as by an accelerometer-based monitoring system installed for comparison purposes. The paper presents and discusses the post-processing of the video frames with particular emphasis on vertical dynamic movements related to bending and torsional modes, comparing vision-based results with data obtained from accelerometers.
Dynamic Monitoring of a Steel Footbridge Based on Computer Vision Techniques
The field of Structural Health Monitoring (SHM) aims to manage the built heritage, buildings and infrastructures, to first and foremost avoid collapse, detect early sign of damage and plan maintenance. As regards bridges and footbridges, structural health conditions are tightly coupled with modal properties, attainable from structural vibrations. The assessment of vibrations is traditionally obtained with measuring systems composed of sensors directly installed on the structure. Despite the large diffusion of these systems, their use may be unfeasible due to the necessity of an expensive and time-consuming installation of the acquisition system. A promising approach to characterize the footbridge dynamic behavior is represented by computer vision-based techniques, that require the installation of only one or more cameras together with, if necessary, some targets on the monitored structure. The vision-based approach is totally non-invasive, low-cost and enables the direct measurement of structural displacements. The aim of the present paper is to investigate the potential of vision-based techniques for the dynamic identification of structures, analyzing some preliminary results of dynamic tests performed on a steel footbridge. Structural vibrations caused by a jumping pedestrian were acquired from a camera as well as by an accelerometer-based monitoring system installed for comparison purposes. The paper presents and discusses the post-processing of the video frames with particular emphasis on vertical dynamic movements related to bending and torsional modes, comparing vision-based results with data obtained from accelerometers.
Dynamic Monitoring of a Steel Footbridge Based on Computer Vision Techniques
Lecture Notes in Civil Engineering
Rainieri, Carlo (Herausgeber:in) / Gentile, Carmelo (Herausgeber:in) / Aenlle López, Manuel (Herausgeber:in) / Ponsi, Federico (Autor:in) / Buoli, Edoardo (Autor:in) / Varzaneh, Ghita Eslami (Autor:in) / Bassoli, Elisa (Autor:in) / Ghirelli, Giorgia (Autor:in) / Briseghella, Bruno (Autor:in) / Vincenzi, Loris (Autor:in)
International Operational Modal Analysis Conference ; 2024 ; Naples, Italy
Proceedings of the 10th International Operational Modal Analysis Conference (IOMAC 2024) ; Kapitel: 29 ; 292-303
22.06.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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