Vision-based algorithms for damage detection and localization in structural health monitoring

Dworakowski, Ziemowit; Kohut, Piotr; Gallina, Alberto; Holak, Krzysztof; Uhl, Tadeusz

doi:10.1002/stc.1755

Cited by 84 publications

(50 citation statements)

References 38 publications

(50 reference statements)

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“…By applying subpixel methods, vision sensors are capable of simultaneous multi‐target displacement monitoring with only one video camera (Luo et al., ). Various vision‐sensing techniques have been developed and applied for displacement measurement such as digital image correlation (DIC) (Mazzoleni and Zappa, ; Cigada et al., ; Dworakowski et al., ), phase‐based method (Chen et al., ), orientation‐code‐matching (OCM) technique, optical flow, binary thresholding (Lee and Shinozuka, ), and others (Bartilson et al., ; Oh et al., ; Santos et al., ).…”

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

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Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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“…In order to properly validate the data mining regression performance, the well‐known k ‐fold cross‐validation method was executed, repeating five times a fivefold and in each run performing grid search parameters. The following parameters were analyzed with an (a) original database and (b) new databases without declared outliers: Kernel type: [Lineal, Polynomial, RBF] Gamma:[2 −5 , 2 −4 , 2 −3 , 2 −2 , 2 −1 , 2 0 , 2 1 , 2 2 , 2 3 , 2 4 , 2 5 ] C: [2 −5 , 2 −4 , 2 −3 , 2 −2 , 2 −1 , 2 0 , 2 1 , 2 2 , 2 3 , 2 4 , 2 5 ] NU: [0.015, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] Polynomial degree in Polynomial kernel: …”

Section: Experimental Evaluation Of Developed Prototypementioning

confidence: 99%

Experimental image and range scanner datasets fusion in SHM for displacement detection

Rivera-Castillo

Flores-Fuentes

Rivas-López

et al. 2016

Struct. Control Health Monit.

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Summary Optical images and signals can be used to detect displacement in civil engineering structures. This paper presents a technical experimentation of a vision‐based technology and artificial intelligence algorithms methodology for structural health monitoring of new and aging structures, by a noncontact and nondestructive system. The experimental study emphasis is on the outdoor urban environment, by the detection of spatial coordinate displacement on the structures, in order to perform a damage assessment. Also, the experimental study contains both theoretical and experimental aspects of the fusion of image and range scanner datasets created using intelligent algorithms. A camera and an optical scanning system were used to generate high resolution and quality images for 2D imaging, and 3D accuracy range data from optoelectronic sensor signals. Scans at a specific area of an engineering structure were performed to measure spatial coordinates displacements, successfully verifying the effectiveness and the robustness of the proposed non‐contact and non‐destructive monitoring approach.

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“…For cable‐supported bridges, the efficiency and convenience of tension measurement significantly depend on the kinds of sensors used. The kinds of sensors used to measure the cable tension can be generally and largely divided into contact‐type sensors (e.g., load cells, hydraulic jacks, accelerometers, and elastomagnetic sensors) and noncontact‐type sensors (e.g., laser vibrometers and vision‐based systems). They can also be divided into the wired and wireless types depending on the presence or absence of electric or data wires .…”

Section: Introductionmentioning

confidence: 99%

Image‐based back analysis for tension estimation of suspension bridge hanger cables

Kim

Cheung

Park³

et al. 2020

Struct Control Health Monit

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Summary In suspension bridges, hanger cables are the main load‐supporting members. The tension of the hanger cables of a suspension bridge is a very important parameter for assessing the integrity and safety of the bridge. In general, indirect methods are used to measure the tension of the hanger cables of a suspension bridge in use. A representative indirect method is the vibration method, which extracts modal frequencies from the cables' responses and then measures the cable tension using the cables' geometric conditions and the modal frequencies. In this study, ambient vibration tests were conducted on a suspension bridge in use to verify the validity of the image‐based back analysis method, which can estimate the tension of remote hanger cables using the modal frequencies as a parameter. The tension estimated through back analysis, which was conducted to minimize the difference between the modal frequencies calculated using finite element analysis of the hanger cables and the measured modal frequencies, was compared with that measured using the vibration method. It was confirmed that reliable tension estimation is possible even with low‐order modal frequencies when the image‐based back analysis method is used.

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Vision-based algorithms for damage detection and localization in structural health monitoring

Cited by 84 publications

References 38 publications

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Experimental image and range scanner datasets fusion in SHM for displacement detection

Image‐based back analysis for tension estimation of suspension bridge hanger cables

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