Bridge displacement measurement through digital image correlation

Peddle, Jason; Goudreau, Adam; Carlson, Emily; Santini‐Bell, Erin

doi:10.3233/brs-2011-031

Cited by 28 publications

(16 citation statements)

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“…Inclement weather, particularly rain, poses a problem for DIC as precipitation in the photographs may interfere with pattern recognition (Bell, Peddle, & Goudreau, 2012) and potentially damage exposed hardware. A minimum amount of light is required (Bell, Gaylord, Goudreau, & White, 2015) and solar glare should be avoided (Bell et al, 2012;Peddle, Goudreau, Carlson, & Santini-Bell, 2011). The measurement area is limited by the FOV of the cameras (Busca, Cigada, Mazzoleni, & Zappa, 2014).…”

Section: Digital Image Correlation For Bridge Monitoringmentioning

confidence: 99%

Assessment of Bridges Subjected to Vehicular Collision

Wang¹,

Thrall²

2019

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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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“…Recently in the past decade, several studies and field applications explored the possibility of using non-contact visionbased systems for larger applications such as bridge monitoring and system identifications (e.g., Lee and Shinozuka, 2006;Yoneyama et al, 2007;Chiang et al, 2011;Peddle et al, 2011;Nonis et al, 2013;Murray et al, 2014;Ribeiro et al, 2014;Ye et al, 2015;Pan et al, 2016;Niezrecki et al, 2018). The related works of measuring 3D displacement of bridge structures could be found in Santos et al (2012) where 3D displacements of the bridge deck were measured.…”

Section: Introductionmentioning

confidence: 99%

System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation

Ngeljaratan

Moustafa

2019

Front. Built Environ.

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This paper characterizes the extensive research activities conducted in the Earthquake Engineering Laboratory of University of Nevada, Reno, in the field of dynamic monitoring and system identification of three 1/3-scale two-span bridges. The first part of the study briefly presents the verification of target-tracking Digital Image Correlation (DIC) results as compared to conventional sensors, e.g., string potentiometers and triaxial accelerometers from one of the three bridge tests. Structural system identification is presented in the second part for the other two bridges with a focus on determining structural model parameters based on the DIC measured response data. All bridges were tested under bidirectional earthquake loading using the multiple shake table array. However, the system identification used data collected from white noise runs before and after the seismic tests. A quasi-linear response of the system was assumed because of the low intensity white noise base excitations, and the modal parameters were estimated accordingly. Using the structural vibration data recorded by target-tracking DIC at various locations on the bridges, five system identification methods were applied to analyze the modal parameters of the tested bridges. The results were used to estimate the frequency, damping ratio, and mode shapes of the bridges in two states. The initial state is before seismic testing and the end state is the damaged state after the completion of the seismic tests. The results show that the applied methods provide a reasonable estimate of the natural frequency and damping ratio of the bridge systems in the original and damaged states.

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Bridge displacement measurement through digital image correlation

Cited by 28 publications

References 0 publications

Assessment of Bridges Subjected to Vehicular Collision

Assessment of Bridges Subjected to Vehicular Collision

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

System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation

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