scholarly journals Image-Based Automated Width Measurement of Surface Cracking

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7534
Author(s):  
Miguel Carrasco ◽  
Gerardo Araya-Letelier ◽  
Ramiro Velázquez ◽  
Paolo Visconti
Keyword(s):  
Crack Width ◽  
Crack Detection ◽  
Construction Materials ◽  
Structural Safety ◽  
Surface Cracking ◽  
Monitoring Task ◽  
Automated Method ◽  
Subjective Judgement ◽  
Width Measurement ◽  
Civil Engineering Infrastructure

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

CRACK WIDTH MEASUREMENT USING MULTISCALE LOG KERNEL

2018 ◽  
Vol 74 (2) ◽  
pp. I_30-I_39
Author(s):  
Kenji KAGITANI ◽  
Koji OSHIKIRI ◽  
Taro KIKUCHI ◽  
Shou MAKINO ◽  
Yoshito SEINO ◽  
...  
Keyword(s):  
Crack Width ◽  
Width Measurement

scholarly journals Image-Based Crack Detection Using Crack Width Transform (CWT) Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60100-60114 ◽  
Author(s):  
Hyunwoo Cho ◽  
Hyuk-Jin Yoon ◽  
Ju-Yeong Jung
Keyword(s):  
Crack Width ◽  
Crack Detection

scholarly journals Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Structural Safety ◽  
Building Structures ◽  
Adjustment Method ◽  
Building Management ◽  
Otsu Method ◽  
Gray Intensity

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

scholarly journals Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 717 ◽  
Author(s):  
Gang Li ◽  
Biao Ma ◽  
Shuanhai He ◽  
Xueli Ren ◽  
Qiangwei Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Width ◽  
Crack Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Fully Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Innovative Composites with Carbon Nanofillers for Self-Sensing Structural RC Beams

2018 ◽  
Vol 19 ◽  
pp. 12-22 ◽  
Author(s):  
A. D'Alessandro ◽  
A. Meoni ◽  
F. Ubertini
Keyword(s):  
Research Work ◽  
Construction Materials ◽  
Reinforced Concrete Beam ◽  
The Self ◽  
Electrical Performance ◽  
Structural Safety ◽  
Cement Pastes ◽  
The Matrix ◽  
Behavior Modifications ◽  
Carbon Nanofillers

The progress of nanotechnology resulted in the development of new instruments in the civil engineering and its applications. In particular, the use of carbon nanofillers into the matrix of construction materials can provide enhanced properties to the material in both of mechanical and electrical performance. In constructions, concrete is among the most used material. Due to the peculiarities of its components and its structure, it is suitable to modifications, at the nanometer level too. Moreover, to guarantee structural safety it is desirable to achieve a diffuse monitoring of structures in order to identify incipient situations of damages and possible risk for people. The ideal solution would be to realize structures able to identify easily and quickly their behavior modifications. This paper presents a research work about the characterization of the self-sensing abilities of novel cementitious composites with conductive carbon nanoinclusions and their application into a structural reinforced concrete beam. The self-sensing evidence is achieved through the correlation between the variation of strains and the variation of electrical resistance or resistivity. Nanomodified cement pastes with different carbon nanofillers has been tested. The experimental campaign shows the potentialities of this new types of sensors made of nanomodified concrete for diffuse Structural Health Monitoring.

scholarly journals Review on Buildings Health Monitoring System by Using Hybrid Data Optimization Based on Machine Learning Algorithm

2021 ◽  
pp. 25-29
Author(s):  
Shweta D Shenmare
Keyword(s):  
Crack Detection ◽  
Learning Algorithm ◽  
Structural Safety ◽  
Adjustment Method ◽  
Building Management ◽  
Otsu Method ◽  
Hybrid Data ◽  
Gray Intensity ◽  
Processing Techniques ◽  
Intelligent Model

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is pains takingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

scholarly journals Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera

2021 ◽  
Vol 11 (20) ◽  
pp. 9714
Author(s):  
Hoseong Jeong ◽  
Baekeun Jeong ◽  
Myounghee Han ◽  
Dooyong Cho
Keyword(s):  
Image Processing ◽  
Crack Width ◽  
Focal Length ◽  
Great Influence ◽  
Health And Safety ◽  
Digital Camera ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Width Measurement ◽  
Working Distance

Visual inspections are performed to investigate cracks in concrete infrastructure. These activities require manpower or equipment such as articulated ladders. Additionally, there are health and safety issues because some structures have low accessibility. To deal with these problems, crack measurement with digital images and digital image processing (DIP) techniques have been adopted in various studies. The objective of this experimental study is to evaluate the optical limit of digital camera lenses as working distance increases. Three different lenses and two digital cameras were used to capture images of lines ranging from 0.1 to 0.5 mm in thickness. As a result of the experiments, it was found that many elements affect width measurement. However, crack width measurement is dependent on the measured pixel values. To accurately measure width, the measured pixel values must be in decimal units, but that is theoretically impossible. According to the results, in the case of 0.3 mm wide or wider cracks, a working distance of 1 m was secured when the focal length was 50 mm, and working distances of 3 m and 4 m were secured when the focal length was 100 mm and 135 mm, respectively. However, for cracks not wider than 0.1 mm, focal lengths of 100 mm and 135 mm showed measurability within 1 m, but a focal length of 50 mm was judged to hardly enable measurement except for certain working positions. Field measurement tests were conducted to verify measurement parameters identified by the results of the indoor experiment. The widths of actual cracks were measured through visual inspection and used for the analysis. From the evaluation, it was confirmed that the number of pixels corresponding to the working distance had a great influence on crack width measurement accuracy when using image processing. Therefore, the optimal distance and measurement guidelines required for the measurement of the size of certain objects was presented for the imaging equipment and optical equipment applied in this study.

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