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Motion magnification analysis applied to the dynamic identification of historic constructions
- Publication Year :
- 2018
- Publisher :
- Institute of Physics Publishing, 2018.
-
Abstract
- We applied a new methodology, namely the motion magnification (MM), to the dynamic identification of historic constructions. MM acts like a microscope amplifying small motions in video sequences, therefore tiny motion patterns are made visible with the naked eye. This technique provides advantages of particular interest to the dynamic identification: no wires, no physical contact, simplicity and low costs. In this paper, we investigated the ambient vibration monitoring of historic structures in urban environments, which is a relevant issue for the health surveying and early damage detection, particularly for ancient buildings. We give an introduction to the MM methodology and describe its practical application to the frequency domain through two case-studies: the so-called Temple of Minerva Medica of Rome and the Ponte delle Torri of Spoleto. Since in the outdoor environment the MM is much more prone to noise because of the wind, shadows, atmospheric refraction, light reflection, distance from the object, weak vibration sources, the case study of the Ponte delle Torri represents a hard test-bed for MM. However, the MM estimates of the first modes showed a good agreement with the experimental data of contact velocimeters. © Published under licence by IOP Publishing Ltd.
- Subjects :
- Microscope
Computer science
Acoustics
Motion (geometry)
02 engineering and technology
law.invention
Vibration
Identification (information)
law
020204 information systems
Simplicity (photography)
Frequency domain
0202 electrical engineering, electronic engineering, information engineering
Atmospheric refraction
Noise (video)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....500081b83f2f88674544eba05b43c7f8
- Full Text :
- https://doi.org/10.1088/1757-899X/364/1/012001&partnerID=40&md5=eeaf391a9764cb9172d2499673cae98c