Crack detection near the ends of a beam using wavelet transform and high resolution beam deflection measurement

Kumar, Ramnivas; Singh, Sachin

doi:10.1016/j.euromechsol.2021.104259

Cited by 26 publications

(13 citation statements)

References 37 publications

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“…Wavelet transforms have excellent properties to extract the localized information in the of measurement noise [ 19 ]. Kumar and Singh [ 20 ] used the continuous wavelet transform (CWT) to locate the crack in a beam. Nigam and Singh [ 21 ] used discrete wavelet transform to detect the crack in a beam.…”

Section: Introductionmentioning

confidence: 99%

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

Yang

Lin

et al. 2022

Sensors

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The image of expressway asphalt pavement crack disease obtained by a three-dimensional line scan laser is easily affected by external factors such as uneven illumination distribution, environmental noise, occlusion shadow, and foreign bodies on the pavement. To locate and extract cracks accurately and efficiently, this article proposes a three-stage asphalt pavement crack location and segmentation method based on traditional digital image processing technology and deep learning methods. In the first stage of this method, the guided filtering and Retinex methods are used to preprocess the asphalt pavement crack image. The processed image removes redundant noise information and improves the brightness. At the information entropy level, it is 63% higher than the unpreprocessed image. In the second stage, the newly proposed YOLO-SAMT target detection model is used to locate the crack diseases in asphalt pavement. The model is 5.42 percentage points higher than the original YOLOv7 model on mAP@0.5, which enhances the recognition and location ability of crack diseases and reduces the calculation amount for the extraction of crack contour in the next stage. In the third stage, the improved k-means clustering algorithm is used to extract cracks. Compared with the traditional k-means clustering algorithm, this method improves the accuracy by 7.34 percentage points, the true rate by 6.57 percentage points, and the false positive rate by 18.32 percentage points to better extract the crack contour. To sum up, the method proposed in this article improves the quality of the pavement disease image, enhances the ability to identify and locate cracks, reduces the amount of calculation, improves the accuracy of crack contour extraction, and provides a new solution for highway crack inspection.

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Section: Introductionmentioning

confidence: 99%

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

Yang

Lin

et al. 2022

Sensors

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“…At the location of damage, the induced abnormality in the displacement field (both static deflections and mode shapes) will result in a local peak value of wavelet coefficients [2][3][4][5][6][7]. Common methods to determine the location of these local peaks include the use of a 2D color map [2,3,8] or the use of combined wavelet coefficients through different scales [9,10]. Apart from the exploitation of wavelet scales in damage detection, the mode order is also utilized in this paper for the determination of DLI.…”

Section: Introductionmentioning

confidence: 99%

“…In the application of CWT for damage detection, the number of samples of the discrete space signal directly affects the damage location accuracy. Kumar and Singh [8] compares the use of 100, 200, and 1000 measurement points for the application of CWT for damage detection and indicates that the accuracy of the results is affected significantly. Hence, numerical studies on this topic consider usually a high number of measurement points and the experimental studies are conducted with measuring systems that can obtain a dense measurement grid, such as laser scanning [7,11] and photogrammetry measuring system [1,8,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Wavelet and Lipschitz exponent based damage identification method for beams using mode shapes

Solís²,

Rodríguez-Mariscal³

et al. 2022

Measurement

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“…The application of alternatives to finite differences for the post-processing of modal data has also been reported (e.g. Solis et al, 2018, Kumar and Singh, 2021, Oliver et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

Improvement on modal curvatures methods for multiple damage identification in beams

Cinque

Santos

Gabriele

et al. 2021

MMMS

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PurposeThe purpose of this paper is to present a study on the application of four damage factors to several single and multiple damage scenarios of aluminium beams. Each one of these damage factors is defined by the information given by modal curvatures of the beams.Design/methodology/approachThe methodology consisted of a first experimental stage in which the modal rotations were measured with shearography and a subsequent numerical analysis in order to obtain the modal curvatures. To this end, three finite difference formulae were applied. The modal curvatures were then used to calculate the damage factors.FindingsIt was found that the profile of the damage factors varies according to the finite difference formula used. In view of the findings, the differences among the damage factors analysed are highlighted and some final recommendations to improve damage identifications via modal curvature-based are presented.Originality/valueTo the best of the authors’ knowledge, the application and comparison of several finite difference formulae and corresponding optimal sampling has not been carried out before. With the proposed approach, it is possible to identify multiple damages, which is still a great challenge. The post-processing of shearography measurements with a numerical method, which is inherently a multidisciplinary approach, is also a substantial improvement upon other type of approaches found in the literature.

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Crack detection near the ends of a beam using wavelet transform and high resolution beam deflection measurement

Cited by 26 publications

References 37 publications

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

Wavelet and Lipschitz exponent based damage identification method for beams using mode shapes

Improvement on modal curvatures methods for multiple damage identification in beams

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