• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.125-137
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• 2009

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.5
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• pp.501-509
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• 2001

For the enhancement of ECT reliability on the primary water stress corrosion cracks of nuclear steam generator tubes, of which the occurrence is on the increase, it is important to comprehend the signal characteristics on crack morphology and to select an appropriate probe type. In this paper, the sizing accuracy and the detectability for the inner wall axial cracks of tubes were quantitatively evaluated using the following specimens: the electric discharge machined notches and the corrosion cracks which were developed on the operating steam generator tubes. The difference of eddy current signal characteristics between pancake and axial coil were also Investigated. The results obtained from this study provide a useful information for more precise evaluation on the inner wall axial tracks oi stram generator tubes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.458-465
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• 2018

Purpose: Concrete track that affects on railway safety can detect cracks using image processing technique. However, since a condition of concrete track and surface noisy are obstructed to detect cracks, there is a need for a way to remove them effectively. Method: In this study, we proposed an image processing to detect cracks effectively for Korean railway and verified its performance through experiment. We developed image acquisition system for capture a railway concrete track and acquired railway concrete track images, randomly selected 2000 images and detected cracks in the image process using proposed Gabor Filter Bank methods. Results: As a result, 94% of detection rate are matched to the actual cracks in same quality and format railway concrete track image. Conclution: The crack detection method using Garbor Filter Bank was confirmed to be effective for crack image including noise in the Korean railway concrete track. This system is expected to become an automated maintenance system in the existing human-centered railway industry.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.303-310
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• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.482-482
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• 2023

상수도관은 사용년수가 경과함에 따라 노후화가 진행되며, 노후화된 상수관은 내부적으로 부식, 이물질 퇴적, 균열 등의 현상이 발생하게 되고, 이는 결국 수질문제로 연결되어, 탁수사고 발생 확률증가의 주요 원인이 되고 있다. 국내 상수도관의 경우 매설년수의 증가로 인해 내구연한이 도래한 상수관망의 비중이 점차 증가하고 있으며, 2019년 서울시 문래동 수질사고, 2019년 인천 붉은 수돗물 사고, 2022년 안양 동안구 탁수사고, 2022년 여수시 웅천 탁수사고 등 관의 노후화로인한 탁수 사고가 빈번하게 발생되고 있어 수도 사용자에게 불편함을 끼치고 있다. 현재 정수장 및 상수관망에 설치된 탁도계를 통해 수질에 대한 감시를 진행하고 있지만, 경제적인 문제로 인해 모든 상수도관에 탁도계를 설치하기에는 현실적으로 불가능하며, 제한적인 탁도계의 개수를 통해 수질에 대한 감시 및 관리를 진행하고 있는 실정이다. 이러한 상황으로 인해 탁수사고 발생 시 발생 원인분석 및 최초 발생위치 결정이 쉽지 않으며, 보수 보강을 통한 상수도관의 정상화까지 오랜 시간이 걸리게 된다. 이에 본 연구에서는 상수관망에서 탁수 발생 시 최초 발생 위치를 결정할 수 있는 기법을 개발하였으며, 이를 실제 상수도관망에 적용하여 탁수발생 파이프를 탐지하였다. 탁수사고 발생 시 실측된 수질 데이터의 부족으로 인해 임의의 파이프에서 탁수가 발생하였다고 가상의 탁수 발생시나리오를 가정하였으며, 완전혼합농도식을 통해 관망에 설치된 탁도계의 NTU(Nethelometric Paultity Unit) 농도를 계산하여 가상의 탁수발생 시나리오를 상수도관망에 적용하였다. 이후, 역추적 계산기법을 통해 파이프의 초기 NTU 농도를 변화시켜주며 관망내 설치된 탁도계의 NTU 농도를 계산하였으며, 가상 시나리오를 적용하여 계산된 탁도계의 NTU 농도와 역추적 계산법을 적용하여 계산된 탁도계의 NTU 농도의 Percentage Error를 비교/분석하여 탁수 발생 파이프를 탐지하였다. 분석결과, 가상 시나리오의 최초 탁수발생 파이프와 역추적 계산법을 적용하여 탐지한 최초 탁수발생 파이프의 위치가 일치하는 것으로 나타났다. 본 연구에서 개발된 역추적계산을 통한 탁수발생 파이프 탐지기법을 실제 관로 교체사업에 활용한다면 파이프의 개선 우선순위를 보다 명확하게 판단할 수 있으며, 더 나아가 상수도 관망의 유지관리에 활용하여 경제적이고 효율적인 상수관망 시스템관리를 할 수 있을 것으로 판단된다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.30-38
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• 2023

As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.731-738
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• 2015

This study proposed the method of the multi-crack detection using the sensitivity coefficient matrix which is calculated from the change of eigenvalues and eigenvectors before and after the crack. Each crack is modeled by a rotational springs. The method is applied to the cantilever beam with miulti-crack. The eigenvalues and eigenvectors are determined for different crack locations and depths. The prediction of multi-crack detection are in good agreement with the results of structural reanalysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.103-104
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• 2022

Cracks may occur in reinforced concrete (RC) structures due to various physical and chemical factors, and the growth of cracks causes deterioration of the structure's performance. It is important to prevent the expansion of cracks through periodic diagnosis of cracks in structures. In order to enable free crack exploration even in a narrow space, a construction robot using a Mecanum wheel that can move up, down, left and right and rotate in place was designed. High-quality crack images were periodically collected through the camera, and the image fragments stored during the exploration were combined into a single photo after the exploration was completed. The robot detected cracks with a width of 0.2 mm or more on the concrete probe surface with an accuracy of about 90% or more.