Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks

2021 ◽  
Author(s):  
Said Quqa ◽  
Panagiotis Martakis ◽  
Artur Movsessian ◽  
Sai Pai ◽  
Yves Reuland ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fatigue Crack ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Steel Bridges ◽  
Fatigue Crack Detection

Advanced fatigue-crack detection system in steel bridges

1995 ◽  
Author(s):  
Marvin F. Fleming ◽  
S. Hersh ◽  
Steven B. Chase
Keyword(s):  
Fatigue Crack ◽  
Crack Detection ◽  
Detection System ◽  
Steel Bridges ◽  
Fatigue Crack Detection

scholarly journals SCCDNet: A Pixel-Level Crack Segmentation Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5074
Author(s):  
Haotian Li ◽  
Zhuang Yue ◽  
Jingyu Liu ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Feature Map ◽  
Connected Structure

Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the feature map shaving the same resolution in the Encoder and Decoder. We used depthwise separable convolution to improve the accuracy of crack segmentation while reducing the complexity of the model. In this paper, a dataset containing cracks collected in different scenes was established, and SCCDNet was trained and tested on this dataset. Compared with the advanced models, SCCDNet obtained the best crack segmentation performance, while F-score reached 0.7763.

Non-contact visualization of nonlinear ultrasonic modulation for reference-free fatigue crack detection

2014 ◽  
Author(s):  
Hyung Jin Lim ◽  
Byeongju Song ◽  
Byeongjin Park ◽  
Peipei Liu ◽  
Hoon Sohn
Keyword(s):  
Fatigue Crack ◽  
Crack Detection ◽  
Nonlinear Ultrasonic ◽  
Fatigue Crack Detection ◽  
Contact Visualization
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