Computer vision-based displacement and vibration monitoring without using physical target on structures

Khuc, Tung; Çatbaş, F. Necati

doi:10.1201/9781351208796-8

Cited by 22 publications

(35 citation statements)

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“…displacement is different in this proposed work. In Khuc and Catbas' 28,29 work, after feature extraction of both of the two consecutive images, feature matching is performed between these two images using the minimum Euclidean distance (for SIFT) or shortest Hamming distance (for FREAK) of the feature points' descriptor vectors. Feature matching is a critical step to determine the location changing of the selected regions in the two consecutive images for displacement measurement, while in the proposed method, after feature extraction, the optical flow vectors at the locations of feature points are obtained with bidirectional calculation (forward and backward).…”

Section: Methodsmentioning

confidence: 99%

“…Feng et al 27 introduced an orientation code matching (OCM)-based displacement measurement method and compared the performance of different target types including target panel (actually QR codes), feature, rivet, and LED. Khuc and Catbas 28,29 proposed a new vision-based displacement measurement method that did not require installation of manual markers and instead used robust features extracted from the image as virtual makers. The displacement measurement was achieved using feature matching between the consecutive images.…”

Section: Introductionmentioning

confidence: 99%

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Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Dong

Celik

Çatbaş

2018

Structural Health Monitoring

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In this study, a vision-based multi-point structural dynamic monitoring framework is proposed. This framework aims to solve issues in current vision-based structural health monitoring. Limitations are due to manual markers, single-point monitoring, and synchronization between a multiple-camera setup and a sensor network. The proposed method addresses the first issue using virtual markers—features extracted from an image—instead of physical manual markers. The virtual markers can be selected according to each scenario, which makes them versatile. The framework also overcomes the issue of single-point monitoring by utilizing an advanced visual tracking algorithm based on optical flow, allowing multi-point displacement measurements. Besides, a synchronization mechanism between a multiple-camera setup and a sensor network is built. The proposed method is first tested on a grandstand simulator located in the laboratory. The experiment is to verify the performance of displacement measurement of the proposed method and conduct structural identification of the grandstand through multi-point displacement records. The results from the proposed method are then compared to the data gathered by traditional displacement sensors and accelerometers. A second experiment is conducted at a stadium during a football game to validate the feasibility of field application and the operational modal identification of the stadium under human crowd jumping through the measured displacement records. From these experiments, it is concluded that the proposed method can be employed to identify modal parameters for structural health monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Dong

Celik

Çatbaş

2018

Structural Health Monitoring

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“…There are a variety of approaches used to determine the scaling factor for converting pixels to physical distance. In [20], a pretesting calibration method is demonstrated. This involves setting up the camera in the laboratory in an identical manner to that of the field test to be carried out, i.e.…”

Section: System Developmentmentioning

confidence: 99%

Development and Laboratory Testing of a Multipoint Displacement Monitoring System

Lydon

Taylor

Robinson

et al. 2019

Advances in Intelligent Systems and Computing

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This paper develops a synchronised multi-camera contactless vision based multiple point displacement measurement system using wireless action cameras. Displacement measurements can provide a valuable insight into the structural condition and service behaviour of bridges under live loading. However conventional displacement gauges or GPS based systems have limitations in terms of access and accuracy. Computer Vision systems have been validated as a means of displacement calculation, however existing systems in use are limited in scope by their inability to reliably track multiple points on a long span bridge structure. The system introduced in this paper provides a low cost durable alternative which is rapidly deployable. Commercial action cameras were paired with an industrially validated solution for synchronisation to provide multiple point displacement readings. The performance of the system was evaluated in a series of controlled laboratory tests. This included the development of robust displacement identification algorithms which were tested and validated against displacement measurements obtained using a fibre optic displacement gauge. This research will significantly advance current vision based Structural health monitoring (SHM) systems which can be cost prohibitive and provides rapid method of obtaining data which accurately relates to measured bridge deflections.

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“…In addition to the direct‐measurement methods of ground reaction force (i.e., force plate and pressure‐insole sensor), and indirect human inertial forces measurements using accelerometers (i.e., wireless IMUs and wired accelerometers), noncontact vision‐based methods have been extensively studied in recent years. Many algorithms are available for the measurement of dynamic displacement of bridge structures, which can be divided into several categories, such as feature matching (Khuc and Catbas, ; Guo and Zhu, ; Hu et al., ; Feng et al., ), digital image correlation (DIC) (Molina‐Viedma et al., ), 2D‐PTV (Machicoane et al., ), and optical flows (Guo et al., ). Oh et al.…”

Section: Introductionmentioning

confidence: 99%

Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

Tian

Zhang

2018

Computer aided Civil Eng

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The rapid impact testing of bridges contains unique advantages. For example, structural parameters, including frequency response function and structural flexibility matrix can be identified; however, additional impact-testing instruments are required to excite a bridge, restricting the efficiency of the measurement strategy in terms of experimental cost and time. In this paper, a particle image velocimetry-based method is proposed for the rapid impact testing and system identification of footbridges under pedestrian excitations. The proposed method has shown promising features: (1) pedestrian load is utilized for the impact excitation of footbridges, which is more convenient than the conventional impacttesting method with additional excitation devices; (2) the human-induced impact forces under varying jumping scenarios are calculated from image sequences of human motions acquired by a single camera with its noncontact and target-less characteristics; and (3) both humaninduced impact forces (inputs) and structural responses (outputs) are employed to identify more modal parameters (i.e., scaling factors, modal mass, and structural flexibility). The robustness of the proposed method was successfully validated by a laboratory test of a simply supported beam and field testing of a cable-stayed footbridge. The proposed method not only could improve

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Computer vision-based displacement and vibration monitoring without using physical target on structures

Cited by 22 publications

References 1 publication

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Development and Laboratory Testing of a Multipoint Displacement Monitoring System

Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

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