704 A Crack Detection Method Based on Natural Frequency Change (2^<nd> Report)

This paper presents an analytical study on the vibration response of tubular T-joints for detecting the existence of cracks along their intersections. The ABAQUS finite element program was utilized for carrying out the analysis. Frequency response functions were obtained for a joint with and without cracks. The joint was modeled with 8-node degenerate shell elements having 5 degrees of freedom per node. Line spring elements were used to model the crack. The exact crack configuration (semielliptical shape, Fig. 5(b)), as observed from numerous experimental fatigue crack investigations at the critical location, has been achieved through a mapping function, that allows a crack in a planar element to be mapped on to the tube surface. The natural frequency changes with respect to crack depth show little changes, being 4.82 percent for a 83-percent crack depth for the first mode. On the other hand, significant changes have been observed for bending moment and curvature as a function of crack depth. For an 83-percent chord thickness crack, a 97-percent change in bending moment at points around the crack vicinity, and 34.15 to 78 percent change in bending moments, for those locations far away from the crack location, have been observed. Natural frequency change should be combined with other modal parameters such as “bending moment (or bending strain)” and “curvature” changes for crack detection. The presence of the crack can be detected at locations far away from the crack location using such sensors as strain gages.

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Multi-crack detection in general cross-section swept wings based on natural frequency changes

Journal of Vibration and Control ◽

10.1177/1077546320964010 ◽

2020 ◽

pp. 107754632096401

Author(s):

Fatemeh Barzegar ◽

Saeedreza Mohebpour ◽

Hekmat Alighanbari

Keyword(s):

Mathematical Model ◽

Natural Frequency ◽

Cross Section ◽

Crack Detection ◽

Natural Frequencies ◽

Detection Method ◽

Mode Shapes ◽

Modal Strain Energy ◽

Torsional Mode ◽

Frequency Changes

In this article, a multi-crack detection method, which is based on natural frequency changes and the concept of modal strain energy, is for the first time developed for the general cross-section swept tapered wings under coupled bending-torsional vibration and applied to the solid and thin-walled airfoil cross-section wings. The presented method is able to handle the problems with an unknown number of cracks and predicts the number of existent cracks, their locations and depths by optimization of an appropriate objective function. The stress intensity factors of airfoil-shaped crack surfaces are obtained using an approximation method. Inputs of the detection method are natural frequencies of uncracked and cracked wings which are calculated by using a mathematical model and finite element method software ANSYS, respectively, and validated by comparison with former research studies. In the mathematical model, the Rayleigh–Ritz method is used to calculate the coupled bending-torsional mode shapes of the uncracked wing and their corresponding natural frequencies. Results demonstrate that the proposed method has precisely predicted the number, locations and depths of cracks in all case studies.

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

IEEE Access ◽

10.1109/access.2021.3093210 ◽

2021 ◽

pp. 1-1

Author(s):

Jinkang Wang ◽

Xiaohui He ◽

Shao Faming ◽

Guanlin Lu ◽

Hu Cong ◽

...

Keyword(s):

Real Time ◽

Crack Detection ◽

Detection Method

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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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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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