scholarly journals Crack Detection Method on Flexure Hinges of 3-RRR Compliant Mechanism

2015 ◽  
Author(s):  
Junjie Gu ◽  
Changli Liu ◽  
Xuejun Wang ◽  
Shouzhu Hu
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Compliant Mechanism ◽  
Flexure Hinges

scholarly journals A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinkang Wang ◽  
Xiaohui He ◽  
Shao Faming ◽  
Guanlin Lu ◽  
Hu Cong ◽  
...  
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Detection Method

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

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).

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

Sensors ◽  
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.

A new rail crack detection method using LSTM network for actual application based on AE technology

2018 ◽  
Vol 142 ◽  
pp. 78-86 ◽  
Author(s):  
Xin Zhang ◽  
Zhongxian Zou ◽  
Kangwei Wang ◽  
Qiushi Hao ◽  
Yan Wang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Actual Application ◽  
Lstm Network

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.

Large Stroke Series Compliant Mechanism

2012 ◽  
Vol 490-495 ◽  
pp. 1104-1108 ◽  
Author(s):  
Ming Cai Shan ◽  
Wei Ming Wang ◽  
Shu Yuan Ma ◽  
Shuang Liu
Keyword(s):  
Theoretical Model ◽  
Maximum Stress ◽  
Compliant Mechanism ◽  
Critical Parameters ◽  
Element Analysis ◽  
Flexure Hinges ◽  
Body Model ◽  
System A ◽  
Rigid Body Model ◽  
The Difference

To increase the stroke of precision positioning system, a novel series compliant mechanism is presented which is based on elliptical flexure hinges. Pseudo-rigid-body model and energy method are applied to establish the theoretical model of stiffness and maximum stress, which are critical parameters for the large stroke compliant mechanism. The relationships are analyzed between geometric parameters of the series complaint mechanism, stiffness and maximum stress. According that, the series compliant mechanism is designed with the stroke more than 5mm and stiffness less than 3.2N/mm. The difference is less than 5% between the results of finite element analysis and theoretical model computation, which proves the correctness of the application design.

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