Research on Crack Detection Method of Ballastless Track Slab Based on Infrared Thermometer

The mechanical performance of China Railway Track System type II (CRTS II) ballastless track suitable for High-Speed Railway (HSR) bridges is investigated in this project by testing a one-quarter-scaled three-span specimen under thermal loading. Stress analysis was performed both experimentally and numerically, via finite-element modeling in the latter case. The results showed that strains in the track slab, in the cement-emulsified asphalt (CA) mortar and in the track bed, increased nonlinearly with the temperature increase. In the longitudinal direction, the zero-displacement section between the track slab and the track bed was close to the 1/8L section of the beam, while the zero-displacement section between the track slab and the box girder bridge was close to the 3/8L section. The maximum values of the relative vertical displacement between the track bed and the bridge structure occurred in the section at three-quarters of the span. Numerical analysis showed that the lower the temperature, the larger the tensile stresses occurring in the different layers of the track structure, whereas the higher the temperature, the higher the relative displacement between the track system and the box girder bridge. Consequently, quantifying the stresses in the various components of the track structure resulting from sudden temperature drops and evaluating the relative displacements between the rails and the track bed resulting from high-temperature are helpful in the design of ballastless track structures for high-speed railway lines.

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Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Advances in Structural Engineering ◽

10.1177/1369433220986637 ◽

2021 ◽

pp. 136943322098663

Author(s):

Diana Andrushia A ◽

Anand N ◽

Eva Lubloy ◽

Prince Arulraj G

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Detection Method ◽

Concrete Structures ◽

Vital Role ◽

Subjective Knowledge ◽

Thermal Cracks ◽

Reinforced Cement ◽

Novel Method ◽

Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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CRTSII Track Slab Crack Detection Based on Improved YOLOv3 Algorithm

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce51767.2020.00315 ◽

2020 ◽

Author(s):

YaoXing Zhang ◽

Wenju Li ◽

Huiling Chen ◽

Maoxian He

Keyword(s):

Crack Detection ◽

Track Slab

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An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽

10.3390/s21051581 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1581

Author(s):

Xiaolong Chen ◽

Jian Li ◽

Shuowen Huang ◽

Hao Cui ◽

Peirong Liu ◽

...

Keyword(s):

Crack Detection ◽

Detection Method ◽

Multiple Scales ◽

Point Clouds ◽

Depth Image ◽

Detection Accuracy ◽

Detection Techniques ◽

Concrete Crack ◽

3D Point Clouds ◽

2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

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A robust crack detection method for non-uniform distributions of coloured and textured image

2011 IEEE International Conference on Imaging Systems and Techniques ◽

10.1109/ist.2011.5962232 ◽

2011 ◽

Cited By ~ 2

Author(s):

S. Nashat ◽

A. Abdullah ◽

M.Z. Abdullah

Keyword(s):

Crack Detection ◽

Detection Method ◽

Uniform Distributions

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Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

Applied Sciences ◽

10.3390/app11114756 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4756

Author(s):

Gaoran Guo ◽

Xuhao Cui ◽

Bowen Du

Keyword(s):

Random Forest ◽

High Speed ◽

Recognition Rate ◽

Operation Time ◽

Deformation Monitoring ◽

Vibration Monitoring ◽

Railway Track ◽

Ballastless Track ◽

Train Operation ◽

Track Slab

High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

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