Image analysis method for crack distribution and width estimation for reinforced concrete structures

Yang, Yuan–Sen; Wu, Chiun-Lin; Hsu, Thomas T. C.; Yang, Hui; Lu, Hu-Jhong; Chang, Chang-Ching

doi:10.1016/j.autcon.2018.03.012

Cited by 45 publications

(30 citation statements)

References 27 publications

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“…is phenomenon can be explained by the fact that the edges obtained from different algorithms have different thicknesses. Moreover, the window size parameter employed by the Canny algorithm (W � [3,3]), which is automatically identified by DFP, is lower than those of other algorithms (W � [5,5]) because the Gaussian filter has been used in the Canny algorithm to partially smooth the image sample.…”

Section: Model Prediction Results and Performance Comparisonmentioning

confidence: 99%

“…A large number of previous works have particularly focused on detecting cracks in concrete structures [3][4][5]. e reason is that cracks are a major concern when considering the safety, durability, and serviceability of structures [1,6].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Yang et al [5] investigated the performance of a novel approach that employs image matching based on the optical flow and subpixel to analyze slight concrete surface displacements; this approach has shown good performance in detecting thin cracks. An image processing-based model for detection of surface cracks in building structures using an improved Otsu method for image thresholding has been proposed by Hoang [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Hoang

Nguyen

2018

Advances in Civil Engineering

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Crack detection is a crucial task in the periodic survey of high-rise buildings and infrastructure. Manual survey is notorious for low productivity. is study is aimed at establishing an image processing-based method for detecting cracks on concrete wall surfaces in an automatic manner. e Roberts, Prewitt, Canny, and Sobel algorithms are employed as the edge detection methods for revealing the crack textures appearing in concrete walls. e median filtering and object cleaning operations are used to enhance the image and facilitate the crack recognition outcome. Since the edge detectors, the median filter, and the object cleaning operation all require the appropriate selection of tuning parameters, this study relies on the differential flower pollination algorithm as a metaheuristic to optimize the image processing-based crack detection model. Experimental results point out that the newly constructed approach that employs the Prewitt algorithm can achieve a good prediction outcome with classification accuracy rate � 89.95% and area under the curve � 0.90. erefore, the proposed metaheuristic optimized image processing approach can be a promising alternative for automatic recognition of cracks on the concrete wall surface.

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Section: Model Prediction Results and Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Hoang

Nguyen

2018

Advances in Civil Engineering

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“…e changing sizes and depths of the cracks in time are a barometer of predicting the safety of a structure. Hence, the demands of measuring the geometrical parameters of the cracks accurately are ever increasing [4][5][6][7][8], and the number of articles for crack measurements has also been increased [9][10][11][12][13][14][15][16][17]. In this process, many methods such as using ultrasounds [18][19][20][21], X-rays [22], and eddy current (EC) [23] sources are developed, and the images are reconstructed in 3D (three-dimensional) [24] form.…”

Section: Introductionmentioning

confidence: 99%

“…In the photos, the cracks are easily identified because of their darkness in comparison with the concrete surface. Software and hardware tools can easily determine the width and length of the cracks with sufficient accuracy from the image in each photo [17,[26][27][28]. Added on this, the photographing distances can be varied significantly with use of a telephoto lens [29].…”

Section: Introductionmentioning

confidence: 99%

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Vashpanov

Son

Heo

et al. 2019

Advances in Civil Engineering

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The 8-bit RGB image of a cracked concrete surface, obtained with a high-resolution camera based on a close-distance photographing and using an optical microscope, is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth, and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively, for the crack samples used for the experiments. It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

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Novel moiré‐based crack monitoring system with smartphone interface and cloud processing

Ratnam

Ooi

Yen

2019

Struct Control Health Monit

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Summary A popular optical‐mechanical method used to monitor crack growth in concrete structures is based on the moiré effect. The moiré patterns are usually interpreted manually using a read‐out chart or by off‐line processing using custom‐made application software installed in a personal computer. Such methods are not convenient for on‐site crack inspection and measurement. In this work, cloud‐based application software has been developed that can be used to process moiré pattern images sent via a standard smartphone. The moiré pattern resulting from the overlapping of two circular gratings mounted across a crack is captured using the smartphone camera, cropped, and sent via Wi‐Fi or mobile data to cloud server. The cloud server software receives the moiré pattern image and processes it using an image processing algorithm to return the displacement magnitude and direction to the mobile device within a few seconds. The communication between the mobile software and cloud software is via Representational State Transfer protocol that allows further development to accommodate other client platforms. The accuracy of the algorithm has been tested by comparing the displacement data returned by the cloud server using controlled experiment. In the experiment, the gratings were subjected to in‐plane displacement along two orthogonal axes using 0.001‐mm resolution digital micrometers. The results show that the displacement magnitude and displacement direction can be determined within an accuracy of ±0.05 mm and ±5°, respectively. The smartphone interface with cloud processing of moiré patterns is shown to be a promising tool for on‐site crack inspection and monitoring.

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Image analysis method for crack distribution and width estimation for reinforced concrete structures

Cited by 45 publications

References 27 publications

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Novel moiré‐based crack monitoring system with smartphone interface and cloud processing

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