• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.11a
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• pp.19-27
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• 2002

최근 경제구조가 고도화되고 생활수준이 향상됨에 따라 사회간접자본시설 및 각종 용도의 건축물에 대한 요구가 높으며, 특히 고층건물 건축시에 사용되는 리프트카의 수요 또한 증가되고 있다. 일반적으로 기계구조물은 사용기간동안 절대적 안전성을 확보하기 위하여 설계되고 있으나 실제 건설 현장에서 사용중인 피니언축에 균열이 발견되고 있으며 이로 인하여 피니언축 수리보수 기간동안의 공사중지로 공기지연 뿐만 아니라 리프트카 추락으로 인한 인명사고가 발생하는 등 그 위험성이 매우 높다. 이에 따라 각종 구조물, 설비 및 기기의 안전진단을 통한 유지보수가 중요시되고, 결함 검출의 정량화에 대한 연구, 기술 개발이 강력히 요구되고 있다.(중략)

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.415-423
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• 2001

In is well recognized that the ultrasonic method is one of the most common and reliable nondestructive testing(NDT) methods for the quantitative estimation of defects in welded structures. However, NDT techniques applying for adhesively bonded joints have not been clearly established yet. In this paper, the detection of interface crack by the ultrasonic method was applied for the measurement of interface crack length in the adhesively bonded joints of double-cantilever beam(DCB). The optimum condition of transmission coefficients and experimental accuracy by the ultrasonic method in the adhesively bonded joints have been investigated. The experimental values are in good agreement with the computed results by boundary element method(BEM) and Riplings equation.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.97-102
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• 2000

It is well recognized that the ultrasonic methods is one of the most common and reliable nondestructive testing(NDT) methods for the quantitative estimation of defects in welded structures. However, NDT techniques applying for adhesively bonded joints have not been clearly established yet. In this paper, the detection of interface crack by the ultrasonic method was applied for the measurement of interfacial crack length in the adhesively bonded joints of double-cantilever beam(DCB). The optimum condition of transmission coefficients in the adhesively bonded joints and it's experimental accuracy by the ultrasonic method have been investigated. The experimental values are in good agreement with the computed results by boundary element method(BEM) and Ripling's equation.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.1-7
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• 2020

In general, an inspection schedule is established based on the long-term fatigue life during the design stage. However, in the design stage, it is difficult to clearly identify the uncertainty factors affecting long-term fatigue life. In this study, the probabilistic fatigue life assessment was conducted in accordance with the methodology of DNV-GL. Firstly, The initial crack distribution estimated through the initial crack propagation analysis was updated by reflecting the results of crack inspection. Secondly, the updated crack distribution was compared with the initial crack distribution, and the probability of failure was updated with the effect of crack inspection.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.73-74
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• 2022

Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the 'InceptionV3'-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.97-98
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• 2023

Recently, the use of digital technology in architectural technology is gradually increasing with the development of various industrial technologies. There are artificial intelligence and drones in the field of architecture, and among them, deep learning technology has been introduced to conduct research in areas such as precise inspection of buildings, and it is expressed in a highly reliable way. When a building is deteriorated, various defects such as cracks in the surface and subsidence of the structure may occur. Since these cracks can represent serious structural damage in the future, the detection of cracks was conducted using artificial intelligence that can detect and identify surface defects by detecting cracks and aging of buildings.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.215-220
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• 2015

As a nondestructive inspection technology currently in use, infrared thermography has gradually expanded its application range to industry. The method detects only defect areas by grafting ultrasound on a technique of detecting infrared energy emitted from all objects with absolute temperature of 0 K and converting this energy into thermography for inspection. Ultrasound infrared thermography has merits including the ability to inspect a wide area in a short time without contacting the target object. This study investigated the applicability of the technique for defect detection using variable ultrasound excitation inspection methods on samples of Terfenol-D, a magnetostrictive material with a tunable natural resonant frequency.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1
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• pp.5-9
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• 1997

If small cracks in stud bolts are not detected early enough, they grow rapidly and cause total fracture. It is difficult to detect, prior to failure, flaws such as stress-corrosion cracking in thread roots and corrosion wastages using conventional ultrasonic testing methods during inservice inspection. This study show a method of detecting a small crack by digital signal processing. When ultrasonic beams travels into threads in parallel way, the echoes from each successive threads has almost the same intervals between any two signals. We can estimate the next thread signal based on previous thread signal by the predictive distance. The optimized operator is used to remove the predicted successive thread signals so that a small crack signal can be detected.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.5-24
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• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.