Motion Magnification (MM) is an emerging video processing methodology that acts like a microscope for motion in digital videos. Hardly visible motions are magnified leaving unchanged the general topology of the image. Therefore, the micro-displacements produced by vibrations can be amplified greatly and made available to the standard frequency domain analysis. The MM was recently successfully explored as a viable method to perform modal identification, at least in laboratory. In outdoor environment ambient vibration acquisitions are unavoidably affected by significant noise disturbing the modes identification. However, the first three or four modes, which are usually the most relevant to the dynamic behaviour of most structures, can be identified with little supervision, possibly reducing the calculation requirements as much as possible. All these tasks may be accomplished using MM together with the Blind Source Separation (BSS) algorithm. BSS allows the separation of mixed signals without previously known information about the mixture. MM provides the data while the BSS improves the identification of the modes by separating their contribution within the mixed noisy signals. A case-study is proposed to explore the application of the methodology to large ancient masonry structures, which represent very challenging objects for their structural complexities and heterogeneities. In particular, the studied structure was represented by an ancient bridge, the Ponte delle Torri, Spoleto. Due to the outdoor environmental difficulties, to the state of damage of the bridge and to the high level of noise in the video footages, this case-study has to be considered a very demanding one, nevertheless the modes were identified with good approximation in comparison to the results by Operational Modal Analysis (OMA) techniques, applied to ambient vibration data from seismographs equipped with accurate triaxial velocimeters.

Modal identification from motion magnification of ancient monuments supported by blind source separation algorithms

Roselli, I.;de Canio, G.
2019

Abstract

Motion Magnification (MM) is an emerging video processing methodology that acts like a microscope for motion in digital videos. Hardly visible motions are magnified leaving unchanged the general topology of the image. Therefore, the micro-displacements produced by vibrations can be amplified greatly and made available to the standard frequency domain analysis. The MM was recently successfully explored as a viable method to perform modal identification, at least in laboratory. In outdoor environment ambient vibration acquisitions are unavoidably affected by significant noise disturbing the modes identification. However, the first three or four modes, which are usually the most relevant to the dynamic behaviour of most structures, can be identified with little supervision, possibly reducing the calculation requirements as much as possible. All these tasks may be accomplished using MM together with the Blind Source Separation (BSS) algorithm. BSS allows the separation of mixed signals without previously known information about the mixture. MM provides the data while the BSS improves the identification of the modes by separating their contribution within the mixed noisy signals. A case-study is proposed to explore the application of the methodology to large ancient masonry structures, which represent very challenging objects for their structural complexities and heterogeneities. In particular, the studied structure was represented by an ancient bridge, the Ponte delle Torri, Spoleto. Due to the outdoor environmental difficulties, to the state of damage of the bridge and to the high level of noise in the video footages, this case-study has to be considered a very demanding one, nevertheless the modes were identified with good approximation in comparison to the results by Operational Modal Analysis (OMA) techniques, applied to ambient vibration data from seismographs equipped with accurate triaxial velocimeters.
978-618828445-6
Blind Source Separation, Modal Identification, Motion Magnification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/54583
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