A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification

Ye, X.W.; Ni, Yiqing; Wai, TT; Wong, K.Y.; Zhang, X.M.; Xu, Feiyun

doi:10.12989/sss.2013.12.3_4.363

Cited by 114 publications

(64 citation statements)

References 20 publications

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“…al. [48] introduced a real time, long range vision-based displacement measurement system. The method was applicable up to the distance of 1000m's.…”

Section: Displacement By Vision-based Methodsmentioning

confidence: 99%

Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

Ramana

Cha

2017

Conference Proceedings of the Society for Experimental Mechanics Series

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Numerous damage detection methods that use data obtained from contact sensors, physically attached to structures and human inspection methods have been developed. However, damage sensitive features used for these methods such as modal properties of steel and reinforced concrete structures are sensitive to environmental conditions such as temperature and humidity. Besides, human inspection is cost, labor extensive, and is controlled by the technical understanding of an individual. The uncertainties of the contact sensor methods are difficult to address with a regression model or any other temperature compensation method, and these are primary causes of false alarms. In order to address some of these challenges of the traditional sensing system, a vision-based remote sensing system can be one of the alternatives as it gives the explicit intuitions of structural conditions. In addition, bolted connections are common engineering practices, and very few vision-based techniques are developed for loosened bolt detection. Thus, this thesis proposes an automated vision-based method for detecting loosened structural bolts using the Viola-Jones algorithm and support vector machines. The test images of bolt connections are taken with a digital single lens reflex camera. The Viola-Jones algorithm is trained on two datasets of images with and without bolts. The trained algorithm localizes and crops all the bolts on test images. The SVM is trained on another dataset of loose and tight bolts to generate decision boundary for classification of the loosened and tight bolts. The cropped bolt images are binarized to calculate bolt features as head dimensions and exposed shank length. The extracted features are fed into a trained support vector machine to classify the loosened and tight bolts. We test our method on images taken by digital single lens reflex and smartphone cameras.iii A special feeling of gratitude to my loving parents. Their words of encouragement, support, and prayers helped me to come this far.iv

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“…al. [48] introduced a real time, long range vision-based displacement measurement system. The method was applicable up to the distance of 1000m's.…”

Section: Displacement By Vision-based Methodsmentioning

confidence: 99%

Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

Ramana

Cha

2017

Conference Proceedings of the Society for Experimental Mechanics Series

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“…Efforts have been spent on developing advanced vision-based systems to provide accurate and robust displacement measurement primarily of high-rise buildings, [8] short-span bridges, [9][10][11][12] and long-span bridges. [10,[13][14][15][16] Previous studies have indicated the significant potential of vision-based systems for structural condition evaluation, especially for system identification. [17][18][19] Other applications based on the camera measured displacement include finite element model calibration, [20] damage detection, [21] and bridge weigh-in-motion where another camera is used for the traffic monitoring.…”

Section: Review Of Vision-based Approachesmentioning

confidence: 99%

“…• Correlation-based template matching is a classic and widely used technique, [8,9,13,14,16] which is realised by searching for an area in a new frame most closely resembling the reference (or template) that is predefined as a rectangular subset in the initial frame.…”

Section: Target Tracking Techniquesmentioning

confidence: 99%

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Brownjohn

Kong

2018

Struct Control Health Monit

164

106

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SummaryVision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement, for example, at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multipoint displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cablestayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few centimetres induced, in this application, by pedestrian passing. The measured data enable accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads. KEYWORDSbridge displacement, cable vibration, cable-stayed bridge, pedestrian loads, vision-based system | INTRODUCTIONStructural health monitoring is aimed at providing valuable information about structural performance and characterisation of structural defects to the asset owners, especially for those civil infrastructures beyond the design life. Vibrationbased modal tests are a common way for structural condition and serviceability assessment, providing a direct view about the structural stiffness, mass properties, and their distributions. [1] As well as for validating designs of civil structures, modal parameters extracted from vibration data obtained in short-term or long-term measurements are widely believed to have potential for identifying changes in structural condition or "damage." [2] The sensitivity of these parameters to damage depends on the nature of the damage, for example, local deterioration of material, for example, due to corrosion, may not be detectable against background effects of environmental variability, whereas boundary conditions are known to have a relatively strong effect, for example, fixity of bridge supports.[3]This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…in the Tacoma Narrows Bridge [3] and the Tagus Bridge [4], among the earliest applications of opto-electronic visionbased continuous structural deformation monitoring using charge-coupled device (CCD) arrays was to Humber Bridge and Severn Bridge in the UK [5,6]. Since then a number of systems have been developed and evaluated for structural deformation monitoring in high-rise buildings [7], shortspan bridges [8][9][10][11] and long-span bridges [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Review of machine-vision based methodologies for displacement measurement in civil structures

Brownjohn

2017

J Civil Struct Health Monit

232

125

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Vision-based systems are promising tools for displacement measurement in civil structures, possessing advantages over traditional displacement sensors in instrumentation cost, installation efforts and measurement capacity in terms of frequency range and spatial resolution. Approximately one hundred papers to date have appeared on this subject, investigating topics like system development and improvement, the viability on field applications and the potential for structural condition assessment. The main contribution of this paper is to present a literature review of vision-based displacement measurement, from the perspectives of methodologies and applications. Video-processing procedures in this paper are summarised as a three-component framework: camera calibration, target tracking and structural displacement calculation. Methods for each component are presented in principle, with discussions about the relative advantages and limitations. Applications in the two most active fields, bridge deformation and cable vibration measurement, are examined followed by a summary of field challenges observed in monitoring tests. Important gaps requiring further investigation are presented, e.g. robust tracking methods, non-contact sensing and measurement accuracy evaluation in field conditions.

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A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification

Cited by 114 publications

References 20 publications

Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Review of machine-vision based methodologies for displacement measurement in civil structures

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