Deep learning-based fast detection of apparent concrete crack in slab tracks with dilated convolution

Ye, Wenlong; Deng, Shijie; Ren, Juanjuan; Xu, Xue-shan; Zhang, Kaiyao; Du, Wei

doi:10.1016/j.conbuildmat.2022.127157

Cited by 40 publications

(14 citation statements)

References 39 publications

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“…The research on concrete cracking is relatively extensive at home and abroad. It mainly focuses on the causes of cracks, crack control and numerical simulation, characteristics of cracks, and so on [10][11][12][13]. However, there are relatively few experimental studies on the carbonation law of concrete at the crack [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Carbonation Behavior of Cracked Concrete

et al. 2022

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The crack and carbonation of concrete pose a great challenge to its durability. Therefore, this paper studies the effect of cracks on the carbonation depth of cement paste under different factors. The relationship between carbonation and cracks was determined, and the carbonation mechanism of cement paste with cracks was clarified. The results show that a small water–binder ratio can effectively inhibit the carbonation process. The bidirectional carbonation enlarged the carbonation area around the crack. Within 21 days of the carbonation, the carbonation depth increased with carbonation time, and the Ca(OH)2 on the surface of the specimen was sufficient, allowing for a convenient chemical reaction with CO2. The influence of crack width on the carbonation process at the crack was greater than the influence of the crack depth. Carbonation influenced the hydration of cement-based materials, altering the types and quantities of hydration products. In conclusion, accurately predicting the regularity of carbonation in cracked structures is critical for improving the durability of concrete.

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Section: Introductionmentioning

confidence: 99%

Analysis of Carbonation Behavior of Cracked Concrete

et al. 2022

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“…Ye et al stated that the accuracy and f1 score ratio of their proposed method to detect cracks in railway concrete blocks using a deep learning network STCNet is 99.54% [19].…”

Section: Figure 2 Block Diagram For Proposed Approach [14]mentioning

confidence: 99%

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Santur¹,

Yilmazer²,

Karaköse³

et al. 2022

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Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

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“…Hsieh et al (2021) utilized CNN to automatically classify jointed plain concrete pavement slab conditions. Ye et al (2022) developed a fast detection algorithm with dilated convolution based on deep learning to detect the apparent cracks in high-speed railway slab tracks by classification. However, classification algorithms alone cannot determine the exact location of structural defects.…”

Section: Introductionmentioning

confidence: 99%

Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks

Ren

Zhang

et al. 2023

Computer aided Civil Eng

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Cracks are common defects in slab tracks, which can grow and expand over time, leading to a deterioration of the mechanical properties of slab tracks and shortening service life. Therefore, it is essential to accurately detect and repair cracks before they impact services. This study developed a systematic pixel‐level crack segmentation–quantification method suited for nighttime detection of slab tracks. To be specific, slab track crack network II, a pixel‐level segmentation network that aggregates multi‐scale information was proposed to extract the morphology of slab track cracks, and then their widths were calculated by an alternative quantification method proposed in the paper. The model performs best when the initial learning rate is 0.0001, with intersection over unions (IOUs) 84.94% and 83.84% observed on the training set and validation set, respectively. In the test set, the IOU value is 81.07%, higher than that derived from similar segmentation algorithms, indicating higher robustness and better generalization of the network architecture. In addition, the average errors in predicting crack widths resulting from the proposed method are 0.13 and 0.12 mm, compared to the results measured by a vernier caliper and a 3D scanner, respectively. The proposed pixel‐level segmentation–quantification system provides a new method and theoretical support for slab track maintenance and repair.

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Deep learning-based fast detection of apparent concrete crack in slab tracks with dilated convolution

Cited by 40 publications

References 39 publications

Analysis of Carbonation Behavior of Cracked Concrete

Analysis of Carbonation Behavior of Cracked Concrete

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks

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