Vision-Based Measurements to Quantify Bridge Deformations

Ghyabi, Mehrdad; Timber, Luke; Jahangiri, Gholamreza; Lattanzi, David; Shenton, Harry W.; Chajes, Michael J.; Head, Monique

doi:10.1061/(asce)be.1943-5592.0001960

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…It is shown here the effect of the application of a low-pass filter (LPF) with cutoff frequency 15 Hz to the measurements of the video cameras. The displacements from camera A in Figures 17-20 are significantly improved by the application of LPF as compared to the unfiltered signals in Figures 12,14,and 16. Similar considerations can be repeated for the other camera, although in this case the noise has a lesser impact.…”

Section: Analysis Of Measured Displacementsmentioning

confidence: 64%

“…The experimental measure of displacements through video processing for the purpose of structural monitoring in civil engineering infrastructures has attracted much attention in the past decade. Hundreds of research articles, mostly focusing on the dynamic response monitoring of bridges, were reviewed in recent comprehensive analyses of the state-of-theart [1][2][3][4][5] and more articles continue to appear, e.g., [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The reasons for such interest can be explained by considering the appealing characteristics of such technology, namely:…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Structural Monitoring: Application to a Medium-Span Post-Tensioned Concrete Bridge under Vehicular Traffic

Micozzi,

Morici,

Zona

et al. 2023

Infrastructures

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Video processing for structural monitoring has attracted much attention in recent years thanks to the possibility of measuring displacement time histories in the absence of stationary points close to the structure, using hardware that is simple to operate and with accessible costs. Experimental studies show a unanimous consensus on the potentialities of vision-based monitoring to provide accurate results that can be equivalent to those obtained from accelerometers and displacement transducers. However, past studies mostly involved steel bridges and footbridges while very few applications can be found for concrete bridges, characterised by a stiffer response with lower displacement magnitudes and different frequency contents of their dynamic behaviour. Accordingly, the attention of this experimental study is focused on the application of a vision-based structural monitoring system to a medium-span, post-tensioned, simply supported concrete bridge, a very common typology in many road networks. The objective is to provide evidence on the quality of the results that could be obtained using vision-based monitoring, understanding the role and influence on the accuracy of the measurements of various parameters relevant to the hardware settings and target geometry, highlighting possible difficulties, and providing practical recommendations to achieve optimal results.

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Section: Analysis Of Measured Displacementsmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Vision-Based Structural Monitoring: Application to a Medium-Span Post-Tensioned Concrete Bridge under Vehicular Traffic

Micozzi,

Morici,

Zona

et al. 2023

Infrastructures

View full text Add to dashboard Cite

show abstract

“…Peng et al (2016) presented computer vision's detection algorithms, which include an accurate measurement technique for internal/sectional diameter and a classification algorithm for surface defects, to maintain the quality of O-rings. In order to compare the phase-based optical flow approach with the digital image correlation (DIC) methodology, two sets of field tests were carried out in order to assess structural deformations on bridges (Ghyabi et al, 2023). Unlike the phase-based optical flow approach, the proposed approach employing Harris corner detection is computationally economical, rapid, generates rich feature information even on big images, and detects corners accurately even in noisy images.…”

Section: Introductionmentioning

confidence: 99%

Computational Metrology for Measuring Industrial Component Dimensions

Singh,

Gupta

2023

Int. j. math. eng. manag. sci.

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To target the problem of dimension measurement of objects in industry, a new computer vision method is proposed based upon Harris-corner detection. The proposed research delivers an alternative to the requirement of various precise measurement devices and skilled labour. In order to measure the various dimensions of an object, the proposed algorithm separates the corner points from the background based on variations in pixel intensity. An algorithm has been proposed to analyze captured object images and perform measurements and inspection processes. The aim of this paper is to utilize computer vision detection algorithms to control the quality of manufactured parts by sorting them on size tolerance. The length of various objects such as screws, bolts, and a rectangular iron piece was determined from the images captured using smartphone camera (Samsung Galaxy F62). The evidence for a total of eight different measurements is presented, and the accuracy of the method is proved up to 99 percent against the dimensions measured using the Vernier calliper.

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“…Structural health monitoring (SHM) technology is gradually emerging as an important means to monitor the "health" of engineering objects [4,5]. The use of intelligent sensing systems for real-time monitoring, dynamic management, and trend research of engineering structures [6,7]. With the development in the last decade, structural health inspection technology has been widely used in the maintenance and management of civil infrastructure such as bridges, tunnels, and buildings [8,9].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data

Zhang

et al. 2023

Applied Sciences

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Structural health inspection systems are widely used to manage and maintain infrastructure that involves massive sensor devices. However, these sensors receive the natural environment or external factors in the long-term exposure to the outdoor environment, resulting in the failure of the sensors, which causes multiple categories of abnormal data in the collected data. The data often is unbalanced due to the random nature of failures. This unbalanced anomaly data poses a major challenge to existing anomaly detection methods and will affect the effectiveness of the information provided by the structural health monitoring system. In the paper, a data migration method is proposed to migrate bridge data to the target bridge dataset for expansion so that the number of images of different categories in the target bridge dataset increases. This method can be divided into three steps: firstly, to classify the data; secondly, to determine the suitability of the data and to construct the dataset; and finally, to train the data. The comparative validation is used to compare the training performance of the dataset using data migration with the dataset only using the target bridge to analyze the abnormal data identification in each category. In the experiment, the recall of some categories of data reached a significant increase of more than 30%, achieving better identification of various categories of abnormal data. Adopting the method of data migration between different bridges can solve the impact of imbalanced data and improve the recognition performance of categories with fewer images.

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Vision-Based Measurements to Quantify Bridge Deformations

Cited by 16 publications

References 32 publications

Vision-Based Structural Monitoring: Application to a Medium-Span Post-Tensioned Concrete Bridge under Vehicular Traffic

Vision-Based Structural Monitoring: Application to a Medium-Span Post-Tensioned Concrete Bridge under Vehicular Traffic

Computational Metrology for Measuring Industrial Component Dimensions

Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data

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