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Structural Dynamics Identification via Computer Vision Approaches.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Structural Dynamics Identification via Computer Vision Approaches./
Author:	Sarrafi, Aral.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22621044
ISBN:	9781687938534

Structural Dynamics Identification via Computer Vision Approaches.
Sarrafi, Aral.

Structural Dynamics Identification via Computer Vision Approaches. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 168 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--University of Massachusetts Lowell, 2019.

This item must not be sold to any third party vendors.

Digital cameras have been widely used to record structural vibrations due to external dynamic loadings. The structural dynamics information of the vibrating structure is embedded in the subtle pixel variations of the recorded video, and the challenge is to recover the imperceptible structural motion from the video data. Estimating and processing the structural dynamics from videos, i.e. a sequence of images, have the advantages over other traditional sensing modalities, such as being non-contact, having no mass loading effect, and reduced testing time given the full-field observability. This information may be used for a variety of applications such as system identification (SI), model validation, model updating, structural health monitoring (SHM), and facilitating the structural testing, and certification procedures.In this dissertation, several novel computer vision and signal processing approaches for optical-sensing-based structural dynamics identification on a diverse range of structures are developed and studied. The methodologies are validated through both simulated signals as well as videos recorded from real structures, including lab-scale benchmark structures and industrial large-scale structures. Phase-based motion estimation and motion magnification (video magnification) are extensively studied for structural dynamics identification and SHM, on four different structures with complex geometry and material properties. In order to map the motion magnified videos to quantified operating deflection shape (ODS) vectors automatically, a novel hybrid computer vision approach is developed in the dissertation work. This approach reduces the human supervision for estimating the quantified ODS vectors significantly.In the real practice, camera data are more prone to noise; therefore, the uncertainty quantification of estimated motion is crucial for having a measured confidence in any subsequent analysis. A method which is a combination of analytical and numerical approaches has been developed to predict the effect of noise and contamination on the estimated motion. Moreover, the feasibility of an end-to-end data-driven method for structural health monitoring is investigated, to establish the optimal filters only based on learning from the observed data. This approach replaces the feature design, and feature selection stages with tuning the learnable filters during the training, and shows promising performance.

ISBN: 9781687938534Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Computer Vision

Structural Dynamics Identification via Computer Vision Approaches.
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500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
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506 $a This item must not be sold to any third party vendors.
520 $a Digital cameras have been widely used to record structural vibrations due to external dynamic loadings. The structural dynamics information of the vibrating structure is embedded in the subtle pixel variations of the recorded video, and the challenge is to recover the imperceptible structural motion from the video data. Estimating and processing the structural dynamics from videos, i.e. a sequence of images, have the advantages over other traditional sensing modalities, such as being non-contact, having no mass loading effect, and reduced testing time given the full-field observability. This information may be used for a variety of applications such as system identification (SI), model validation, model updating, structural health monitoring (SHM), and facilitating the structural testing, and certification procedures.In this dissertation, several novel computer vision and signal processing approaches for optical-sensing-based structural dynamics identification on a diverse range of structures are developed and studied. The methodologies are validated through both simulated signals as well as videos recorded from real structures, including lab-scale benchmark structures and industrial large-scale structures. Phase-based motion estimation and motion magnification (video magnification) are extensively studied for structural dynamics identification and SHM, on four different structures with complex geometry and material properties. In order to map the motion magnified videos to quantified operating deflection shape (ODS) vectors automatically, a novel hybrid computer vision approach is developed in the dissertation work. This approach reduces the human supervision for estimating the quantified ODS vectors significantly.In the real practice, camera data are more prone to noise; therefore, the uncertainty quantification of estimated motion is crucial for having a measured confidence in any subsequent analysis. A method which is a combination of analytical and numerical approaches has been developed to predict the effect of noise and contamination on the estimated motion. Moreover, the feasibility of an end-to-end data-driven method for structural health monitoring is investigated, to establish the optimal filters only based on learning from the observed data. This approach replaces the feature design, and feature selection stages with tuning the learnable filters during the training, and shows promising performance.
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