Motion Magnification Analysis for structural monitoring of ancient constructions

Fioriti, Vincenzo; Roselli, Ivan; Tatì, Angelo; Romano, Roberto; Canio, Gerardo De

doi:10.1016/j.measurement.2018.07.055

Cited by 63 publications

(33 citation statements)

References 6 publications

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“…Moreover, the algorithm implies a certain noise amplification too [8]. Physical sources of noise are lighting conditions, presence of shadows, camera unwanted vibrations, poor pixel resolution, presence of unexpectedly large motions, excessive camera-object distance.…”

Section: The Magnified Motion Algorithmmentioning

confidence: 99%

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Modal Identification From Motion Magnification of Ancient Monuments Supported by Blind Source Separation Algorithms

Fioriti¹,

Roselli²,

Canio³

2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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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.

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Section: The Magnified Motion Algorithmmentioning

confidence: 99%

“…Ambient vibration was measured by SL06 recorders (SARA Instruments) equipped with triaxial electrodynamic velocimeters, set to 200 Hz sampling frequency [8]. The instruments were positioned along the bridge deck, on top of each pier and in the middle between piers.…”

Section: Modal Identification By Oma Of Velocimeters Datamentioning

confidence: 99%

Modal Identification From Motion Magnification of Ancient Monuments Supported by Blind Source Separation Algorithms

Fioriti¹,

Roselli²,

Canio³

2019

Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

Self Cite

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“…Combining the multi-point displacement response with structural input data extracted from vehicle tracking, structural identification can be carried out using traditional structural indicators such as the unit influence line (UIL) and unit influence surface (UIS) [25,26,27]. Without the need for the deployment of conventional sensor networks, operational modal analysis can be performed using vision-based displacement measurement methods, which may provide multi-point synchronization and, therefore, a much denser spatial resolution than is practical with conventional sensors [12,28,29,30,31,32,33]. Full field motion estimation and instantaneous mode shapes can even be obtained with high spatial and temporal resolution [21,34,35,36].…”

Section: Introductionmentioning

confidence: 99%

“…Full field motion estimation and instantaneous mode shapes can even be obtained with high spatial and temporal resolution [21,34,35,36]. Reference [33] introduced an application based on motion magnification for modal identification of an on-the-field, full-scale, large historic masonry bridge by using videos taken from a common smartphone device. Modal properties and other indices derived from vision-based displacement time histories can be turned into sensitive indicators for structural damage detection and model updating [37,38,39].…”

Section: Introductionmentioning

confidence: 99%

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation

Dong

Celik

Çatbaş

et al. 2019

Sensors

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Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference.

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“…Moreover, the vision-based systems can be applied to SHM for modal analysis through monitoring of modal parameters, e.g., modal frequencies [33,34]. In such a context, a recent Motion Magnification (MM) algorithm provided promising results in modal identification of a full-scale historic bridge by using videos taken from a common smartphone device [35].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

Zhang

Zeinali

Story

et al. 2019

Sensors

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Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.

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Motion Magnification Analysis for structural monitoring of ancient constructions

Cited by 63 publications

References 6 publications

Modal Identification From Motion Magnification of Ancient Monuments Supported by Blind Source Separation Algorithms

Modal Identification From Motion Magnification of Ancient Monuments Supported by Blind Source Separation Algorithms

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

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