• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.1
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• pp.38-47
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• 2007

The initiation of fatigue cracks in a simulated aircraft structure with a series of rivet holes was detected by acoustic emission(AE), then the crack length was determined by surface acoustic wave(SAW) technique. With the initiation and growth of fatigue cracks, AE events increased intermittently to form a stepwise incremental curve of cumulative AE events whereas the crack length increased more or less monotonically. With the SAW technique employed, the crack sizing for 13 different cracks including some short cracks was performed. With the reference to the measurement by traveling microscope, cracks in the range of $1{\sim}8mm$ long were reliably sized by the SAW technique. Although it was impossible to size the short fatigue cracks in the range shorter than 1 mm, the SAW technique still appeared practically useful for a range of crack lengths often found in aircraft structures.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.245-246
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• 2022

Typically, the methods of crack detection on concrete structures include some problems, such as a low accuracy and expensive. To solve these problems, we proposed a neural network-based crack search method. The proposed algorithm goes through three convolutions and is classified into crack and non-crack through the softmax layer. As a result of the performance evaluation, cracks can be detected with an accuracy of 99.4 and 99.34 % at the training model and the validation model, respectively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.147-153
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• 2018

Cracking in concrete structure must be examined according to appropriate methods, to ensure structural serviceability and to prevent structural deterioration, since cracks opened wide for a long time expedite corrosion of rebar. A site investigation is conducted in a regular basis to monitor structural deterioration by tracking growing cracks. However, the visual inspection are labor intensive. and judgment are subject. To overcome the limit of the on-site visual investigation image processing for identifying the cracks of concrete structures by analyzing 2D images has been developed. This study develops a unique 3D technique utilizing a line laser and its projection image onto concrete surfaces. Automated process of crack detection is developed by the algorithms of automatizing crack map generation and image data acquisition. Performance of the developed method is experimentally evaluated.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.3
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• pp.174-183
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• 1996

Acoustic emission(AE) response during stress corrosion cracking(SCC) of Inconel 600 alloy has been monitored to study the AE detectability of crack generation and growth by comparing the crack behavior with AE parameters processed, and to evaluate the applicability as a nondestructive evaluation(AE) by measuring the minimum crack size detectable with AE. Variously heat-treated specimens were tensioned by constant extension rate test(CERT) in various extension rate to give rise to the different SCC behavior of specimens. The AE amplitude level generated from intergranular stress-corrosion cracking(IGSCC) is higher than those from ductile fracture and mechanical deformation, which means the AE amplitude can be a significant parameter for distinguishing the An source. AE can also provide the effective means to identify the transition from the small crack initiation and formation of dominant cracks to the dominant crack growth. Minimum crack size detectable with AE is supposed to be approximately 200 to $400{\mu}m$ in length and below $100{\mu}m$ in depth. The test results show that AE technique has a capability for detecting the early stage of IGSCC growth and the potential for practical application as a NDE.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.693-700
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• 2006

To detect and evaluate the damage present in bridge, static identification method is known to be simple and effective, compared to dynamic method. In this study, the damage detection method in steel box girder bridge using static responses including displacement, slope and curvature is examined. The static displacement is calculated using finite element analysis and the slope and curvature are determined from the displacement using central difference method. The location of damage is detected using the absolute differences of these responses in intact and damaged bridge. Steel box girder bridge with corner crack is modeled using singular element in finite element method. The results show that these responses were significantly useful in detecting and predicting the location of damage present in bridge.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.729-736
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• 2017

In this study, the physics-based model and machine learning technique were used to conduct model-assisted probability of detection (MAPOD) experiments. The possibility of using in-service cracked parts was also investigated. Bolt hole shaped specimens with fatigue crack on the hole surface were inspected using eddy current inspection. Owing to MAPOD, the number of experimental factors decreased significantly. The uncertainty in the crack length measurement for in-service cracked parts was considered by the application of Monte Carlo simulation.

• Journal of the Korean Geotechnical Society
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• v.34 no.12
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• pp.145-154
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• 2018

The damage investigation and inspection methods performed in concrete facilities such as bridges, tunnels, retaining walls and so on, are usually visually examined by the inspector using the surveying tool in the field. These methods highly depend on the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, the new image processing techniques are necessary in order to automatically detect the cracks and objectively analyze the characteristics of cracks. In this study, deep learning and image processing technique were developed to detect cracks and analyze characteristics in images for concrete facilities. Two-stage image processing pipeline was proposed to obtain crack segmentation and its characteristics. The performance of the method was tested using various crack images with a label and the results showed over 90% of accuracy on crack classification and segmentation. Finally, the crack characteristics (length and thickness) of the crack image pictured from the field were analyzed, and the performance of the developed technique was verified by comparing the actual measured values and errors.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.307-314
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• 2004

A circumferential guided wave method was developed to detect the axial crack on the bent feeder pipe. Dispersion curves of circumferential guided waves were calculated as a function of curvature of the pipe. In the case of thin plate, i.e. infinite curvature, as the frequency increases, the $S_0$ and $A_0$ mode coincide and eventually become Rayleigh wave mode. In the case of pipe, however, as the curvature increases, the lowest modes do not coincide even in the high frequencies. Based on the analysis, a rocking technique using angle beam transducer was applied to detect an axial defect in the bent region of PHWR feeder pipe. Based on the analysis of experimenal data for artificial notches, the vibration modes of each signal were identified. It was found that the notches with the depth of )0% of wall thickness can be detected with the method.