• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.64-77
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• 2012

In this study, the crack detecting system with digital image processing techniques based on the mathematical morphology method was developed to detect cracks in concrete structures. In the developed system, the image combining technique of reconstructing multiple images as an entire single image considering efficient management of analysis results was applied as an additional module. The developed system was verified through a field test with the cracked concrete culvert and the crack width of 0.2 mm was able to be detected in the 40m span. In the image analysis, the difference between calculated crack width and actual crack width were less than 0.08mm. For image combination in the stitching test of pattern images, the stitched image was identical with the original picture of entire subject in the visual perception level.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.162-169
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• 2012

This study detected SCC defects of dissimilar metal welded(STS304 and SA106 Gr. b) pipes using the ultrasonic infrared thermography method and the lock-in image treatment method among infrared thermography method. The infrared excitement equipment has 250 Watt of output and 20 kHz of frequency. By using the ultrasound infrared thermography method, the internal defects of dissimilar metal weld joints of pipes used at nuclear power plants could get detected. By an actual PT test, it was observed that the cracks inside the pipe existed not as a single crack but rather as a multiple cracks within a certain area and generated a hot spot image of a broad area on the thermography image. In addition, UT technology could not easily defects detected by the width of $10\;{\mu}m$ fine hair cracks. but, ultrasound infrared thermography technique was defect detected.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.173-182
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• 2007

Cracks in concrete structures should be measured to periodically assess potential problems in durability and serviceability. Conventional crack measurement systems depend on visual inspections and manual measurements of the crack features such as width, length, and direction using microscope and crack gage. However, conventional methods take long time as well as manpower, and lack quantitative objectivity resulted by inspectors. In this study, an evaluation technique for concrete surface cracks is developed using image processing and artificial neural network. Developed technique consists of three major parts: (1) crack detection (2) crack analysis and (3) pattern recognition. To examine validity of the technique developed in this study, crack analyzing tests were performed on the images obtained from various types of concrete surface cracks. The test results revealed that the system is highly effective in automatically analyzing concrete surface cracks in terms of features and patterns of cracks.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.192-197
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• 2012

In this paper, a non-contact microwave technique was presented to detect the surface crack of the metals. An open-ended coaxial cable line was used as a sensor at 11 GHz, and the reflection coefficients were measured by scanning along the metal surface including artificial surface cracks. A parameter, the K value which was defined as the difference between maximum and minimum reflection coefficients, was measured and used to estimate the crack depth. A linear relationship between the K value and crack depth was found. This study showed that non-contact detection of the surface cracks of metals is possible using the open-ended coaxial line sensor at X-band.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.263-266
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• 2004

본 논문에서는 터널 구조물에서 발생하는 균열의 인식과 이에 대한 정보를 획득하기 위한 방법에 대한 연구를 수행하였다. 터널 구조물에서 발생하는 균열의 경우, 건설 분야의 특수성 때문에 기존의 경계점 인식(edge detection) 알고리즘을 적용하는 경우 정밀도 저하의 문제가 발생한다. 이러한 문제점을 해결하고 더불어 정밀도를 향상시킬 수 있는 방법으로 영상에서 균열을 인식하는 균열 인식 단계와 인식된 균열의 길이, 두께 등을 측정하는 균열 정보 획득 단계로 구분하였다. 균열 인식 단계에서는 균열의 시작점과 끝점을 기준으로 휴리스틱(Heuristic) 알고리즘을 사용하여 픽셀 값의 분포에 따라 균열의 중심선을 계산하여 균열을 인식한다. 균열 정보 획득 단계에서는 균열 인식 단계에서 얻은 정보를 통해 균열의 길이, 진행 방향 정보, 그리고 균열의 두께를 계산한다. 균열의 길이 및 진행 방향 정보는 균열 인식 단계에서 얻어진 정보를 통하여 계산하며, 균열 두께 측정은 각 픽셀의 누적 값을 이용하여 계산하는 원형 검출기(Daugman 알고리즘)를 변형하여 사용하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.2
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• pp.125-131
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• 2002

we analyzed acoustic emission signals obtained from three kinds of brittle materials under compression load by Vickers hardness tester. The results obtained can be summarized as follows; The signal in each material could be divided into three signal based on the properties of load. All specimens were not detected acoustic emission signals in stage II which was load constant region., and were detected in stage I and stage III. Glass was detected high amplitude signals in stage III. $Al_2O_3\;and\;Al_2O_3/Sic$ were detected high amplitude signals in stage I.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.220-226
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• 2003

This paper presents the result of an investigation on the application of the acoustic emission method to the monitoring of fatigue crack initiation, growth and track location in welded joints. Fatigue test was carried out for a typical fillet welded joint of ship structure. AE parameter such as ring down count was analyzed in time domain and crack locations were examined by source location and cluster option which is one of the functions of AE signal processor The usability of AE mettled was confirmed for the detection of the initiation and location of through crack.

• 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.29 no.5
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• pp.493-500
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• 2009

An ultrasonic signal processing algorithm was developed for extracting the information of cracks generated around the keyway of a turbine rotor disk. B-scan images were obtained by using keyway specimens and an ultrasonic scan system with x-y position controller. The B-scan images were used as input images for 2-Dimensional signal processing, and the algorithm was constructed with four processing stages of pre-processing, crack candidate region detection, crack region classification and crack information extraction. It is confirmed by experiments that the developed algorithm is effective for the quantitative evaluation of cracks generated around the keyway of turbine rotor disk.