Displacement measurements of highway bridges using digital image correlation methods

Chiang, Chih‐Hung; Shih, Ming‐Hsiang; Chen, Welltin; Yu, Chih-Peng

doi:10.1117/12.904303

Cited by 22 publications

(9 citation statements)

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“…quantify damage on the bridge. To address this challenge, a number of studies adopting a diversity of imaging analysis algorithms, including pattern recognition (Dervilis et al 2014;Jahanshahi et al 2013;Kelm et al 2007), Digital Image Correlation (DIC) (Ackermann 1984;Chiang et al 2011;Manicka et al 2019;Murray 2013;Murray et al 2015;Nonis et al 2013;Reagan et al 2017a;Yoneyama et al 2007;Yoneyama and Ueda 2012), and pixel-based damage quantification (Duque et al 2018b;Seo et al 2018c) were conducted. For better quantification of bridge damage of using UAV images, significant research efforts in conjunction with these algorithms should be made.…”

Section: © Ascementioning

confidence: 99%

New Bridge Inspection Approach with Joint UAV and DIC System

Jeong¹,

Seo²,

Wacker³

2020

Structures Congress 2020

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This research aims to develop a new bridge inspection approach using unmanned aerial vehicle (UAV) coupled with digital image correlation (DIC) system. The DIC system incorporating UAV images can measure displacements or strains by analyzing patterns of reference and deformed images. As part of this research, a commercially available UAV, DJI Matrice 210, was integrated with the DIC system using a 3D printed mounting plate, and the joint UAV-DIC system was utilized to inspect a timber bridge girder in the Structure Lab. Then, the UAV-DIC system inspected an existing timber slab bridge in Pipestone, Minnesota, but the system was not able to efficiently identify critical damage due to its instability caused by windy conditions. Therefore, only the UAV equipped with a gimbal camera was operated to perform the bridge inspection. A significant number of images from the UAV were used and analyzed through a conventional image analysis algorithm within ImageJ software for damage quantification. The major conclusion from this research was that the UAV-DIC system was only able to detect and quantify damage (i.e., crack) on the considered girder under almost zero ambient wind conditions, and the UAV integrated with the image analysis algorithm was capable of damage identification and quantification for the inspected bridge.

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Section: © Ascementioning

confidence: 99%

New Bridge Inspection Approach with Joint UAV and DIC System

Jeong¹,

Seo²,

Wacker³

2020

Structures Congress 2020

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“…Displacements under a load test were measured and used to evaluate the continuous behavior of the bridge. Other applications for field monitoring bridges using DIC include measuring displacements of steel girder bridges under truck loading (Chiang, Shih, Chen, & Yu, 2011), displacements and vibrations of railroad bridges subjected to train loads (Hoag, Hoult, Take, & Le, 2015;Busca et al, 2014) and tension and dynamic characteristics of hanger cables (Kim & Kim, 2013). Among other applications, are the local effects of bridge components (i.e., riveted girders, stay cable anchorages, steel plates in a box girder), global effects of a multi-span twin steel box bridge (Winkler, Hendy, & Waterfall, 2015) and field spalling, crack size, and long-term strain of concrete bridge piers (Nonis, Niezrecki, Yu, Ahmed, Su, & Schmidt, 2013).…”

Section: Digital Image Correlation For Bridge Monitoringmentioning

confidence: 99%

Assessment of Bridges Subjected to Vehicular Collision

Wang¹,

Thrall²

2019

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“…The reader is referred to Pan et al (2009) [40] and Orteu (2009) [41] for reviews of this technology and its applications. Applications in structural engineering are reviewed by McGinnis et al (2012) [42] and McCormick and Lord (2012) [43] and include investigations of the behavior of reinforced concrete walls [44,45], reinforced and unreinforced masonry walls [46], wood buildings [47], bridges [48][49][50][51][52][53], and fiber-reinforced polymer confinement coverings for concrete columns [54], among others.…”

Section: Dicmentioning

confidence: 99%

Full-field measurement of residual strains in cold bent steel plates

Gerbo

Thrall

Smith

et al. 2016

Journal of Constructional Steel Research

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With the recent American Association of State Highway and Transportation Officials bridge design code acceptance of cold bent fracture-critical and nonfracture-critical plates and the increasing use of cold bending by the bridge industry, there is a need to better understand and predict residual strains in cold bent steel. However, measuring residual strains is a challenging task since the bending process typically interferes with conventional instrumentation. This paper introduces three-dimensional Digital Image Correlation (DIC) as a new, non-destructive approach to measuring residual strains in cold bent steel that captures fullfield strain data with high accuracy. DIC was used to measure residual strains in 18 cold bent 12.7 mm thick ASTM A36 steel plates, bent using a press-brake to a constant radius of 102 mm with varying angles (10, 20, and 30 degrees) and plate widths (76.2 mm and 203 mm). The measured residual strains are compared to analytical predictions and both twoand three-dimensional finite element models. A parametric study, using the validated finite element models, is completed for additional widths (12.7 mm and 140 mm) and thicknesses (6.35 mm and 19.1 mm) to investigate the effect of these parameters on circumferential strains, contact pressure, strain state, and to make modeling recommendations. Results are used to develop a means of predicting peak residual strains based on the final plate dimensions-a useful quality control measure.

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Displacement measurements of highway bridges using digital image correlation methods

Cited by 22 publications

References 0 publications

New Bridge Inspection Approach with Joint UAV and DIC System

New Bridge Inspection Approach with Joint UAV and DIC System

Assessment of Bridges Subjected to Vehicular Collision

Full-field measurement of residual strains in cold bent steel plates

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