Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation

Ngeljaratan, Luna; Moustafa, Mohamed A.

doi:10.1016/j.engstruct.2020.110551

Cited by 85 publications

(63 citation statements)

References 32 publications

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“…Both PSNR and MSE are computed based on pixel-to-pixel difference so they provide simple computation with clear physical meaning. Another metric selected in this study is the structural similarity index metric SSIM, which is computed using Equation (5). It measures the similarity between two images in terms of luminance, contrast, and structure.…”

Section: Compressed Image Quality Indexmentioning

confidence: 99%

“…The advancements of non-contact and vision-based sensors in the field of structural health monitoring (SHM) as well as the development of optics and computer vision algorithms have led to a growing demand, among the civil and construction engineering communities, for long-term continuous and real-time vision-based SHM. Currently, monitoring using vision-based sensors incorporates an offline camera calibration and a closed-range photogrammetry process while using either artificial markers [1][2][3][4][5][6][7] or relying on the natural features of the structure [8][9][10]. Several previous works have shown the robust development and promising future of vision-based sensors deployment for SHM purposes [9,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Ngeljaratan

Moustafa

2020

Sensors

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Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

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Section: Compressed Image Quality Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Ngeljaratan

Moustafa

2020

Sensors

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“…In terms of the computer vision‐based 3‐D field monitoring studies, Ngeljaratan and Moustafa [31] conducted the laboratory and field tests to investigate the 3‐D behavior of bridges under seismic excitation by use of target‐tracking digital image correlation approach. Chang and Xiao [32] investigated the 3‐D translation and rotation of a pedestrian bridge using a computer vision‐based approach with an attached planar target.…”

Section: Introductionmentioning

confidence: 99%

Computer vision‐based monitoring of the 3‐D structural deformation of an ancient structure induced by shield tunneling construction

Jin

Ang

et al. 2021

Struct Control Health Monit

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Summary Shield tunneling is an efficient method of the tunnel construction for the system of underground transportation, but it will disturb the surrounding soil and affect the security of nearby structures. The monitoring for deformation of the structures influenced by the tunneling construction is crucial for structural safety evaluation, especially for the ancient structures. The computer vision‐based structural deformation monitoring approach is a newly developed method that has advantages such as, long‐distance, noncontact, and so on, which provides a promising tool for deformation monitoring of ancient structures. This study applied a computer vision‐based method for field monitoring of three‐dimensional (3‐D) deformation of an ancient tower under the influence of shield tunneling. The target‐free strategy was adopted to eliminate the need for installation of artificial makers on the surface of the tower. The environmental factors during the long‐term monitoring were discussed, especially the influence of temperature variation. Background modification was employed to reduce the error induced by the microchange of camera position. The long‐term 3‐D deformations of the ancient tower including vertical settlement and horizontal deformation in two directions during the shield tunneling construction were obtained. The results indicate that the proposed computer vision‐based method offers an efficient and cost‐effective approach to monitor the 3‐D deformation of the ancient structure and further for structural safety evaluation.

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“…Using the advanced sensing technique, structural health monitoring (SHM) technique can diagnose the structural damage and assess the structural safety of bridges by using the different types of structural response [ 1 , 2 , 3 , 4 ]. The core mission of SHM is to detect potential damage of bridges; thus, some methods for damage detection have been proposed [ 5 , 6 , 7 , 8 ], and among them, vibration-based approaches have shown excellent potential.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

Wang¹,

Gao

Liu

2020

Sensors

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Principal component analysis (PCA)-based method is popular for detecting the damage of bridges under varying environmental temperatures. However, this method deletes some information when the damage features are projected in the direction of nonprincipal components; thus, the effectiveness of PCA-based methods will decrease if the deleted information is related to bridge damage. To address this issue, a hybrid method is proposed to detect the damage of bridges under environmental temperature changes. On one side, the PCA-based method is applied to deal with the nonprincipal components; on the other side, the Gaussian mixture method (GMM) is used to classify all the principal components into different clusters, and then the novel detection method is implemented to detect bridge damage for each cluster. In this way, all the damage feature information is saved and used to detect bridge damage. The numerical example and example of an actual bridge show that the proposed hybrid method is effective in detecting bridge damage under environmental temperature changes. The GMM is effective for classifying the natural monitoring frequency data of actual bridges, and the relationship between the natural frequencies of actual bridges and the environmental temperature is not always linear.

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Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation

Cited by 85 publications

References 32 publications

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Computer vision‐based monitoring of the 3‐D structural deformation of an ancient structure induced by shield tunneling construction

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

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