• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.5-12
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• 2021

The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

• Composites Research
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• v.27 no.6
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• pp.219-224
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• 2014

Nancomposites manufacture has been developed rapidly, because of reinforcing effects of CNT in terms of mechanical, electrical and thermal properties. In this study, 10 wt% CNT paste was fabricated with good dispersion state and easy processability. Damage sensing and reinforcing effect of CNT paste were investigated in nanocomposites. 10 wt% CNT paste exhibited better tensile and flexural properties than those of general 1 wt% CNT nanocomposites. To observe the healing effect of CNT paste, a crack was made artificially with 30wt% CF30wt%/PP composites, and the CNT paste was filled inside the crack. The damage sensing of CNT paste in CF30wt%/PP composites was investigated by electrical resistance measurement and mechanical tests. CNT paste exhibited good reinforcing effect in mechanical properties of CF30wt%/PP composites, and this reinforcing effect was getting better with larger cracks. The reason was because CNT paste had good interfacial adhesion with CF30wt%/PP composites to resist crack propagation. In electrical resistance measurement, there was a jump in electrical resistance signal at the adhesion interface. The jumping signal could be used to predict fracture of CF/PP composites. CNT nanocomposites for damage sensing had crack reducing effect and damage detection using electrical resistance method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.67-74
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• 2023

A large number of concrete structures are built and used around the world. To ensure their safe and continuous use, these structures require constant inspection and maintenance. While man-powered inspection and maintenance techniques are efficient, they can only provide intermittent status checks at the time of on-site inspection. Therefore, there is a growing need for a system that can continuously monitor the condition of the structure. A study was conducted to detect cracks and damage by installing a conductive coating on the surface of a concrete structure. A parallel resistance pattern that can monitor the occurrence and progression of cracks was developed by reflecting the structural characteristics of concrete structure. An empirical study was conducted to veryfy the application of the proposed method. The crack detection pattern was installed on the reinforced concrete beams, and the crack monitoring method was verified through applying a load on the beams.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.79-91
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• 2006

A new methodology of guided wave based nondestructive testing (NDT) is developed to detect crack damage in civil infrastructures such as steel bridges without using prior baseline data. In conventional guided wave based techniques, damage is often identified by comparing the "current" data obtained from a potentially damaged condition of a structure with the "past" baseline data collected at the pristine condition of the structure. However, it has been reported that this type of pattern comparison with the baseline data can lead to increased false alarms due to its susceptibility to varying operational and environmental conditions of the structure. To develop a more robust damage diagnosis technique, a new concept of NDT is conceived so that cracks can be detected without direct comparison with previously obtained baseline data. The proposed NDT technique utilizes the polarization characteristics of the piezoelectric wafers attached on the both sides of the thin metal structure. Crack formation creates Lamb wave mode conversion due to a sudden change in the thickness of the structure. Then, the proposed technique instantly detects the appearance of the crack by extracting this mode conversion from the measured Lamb waves even at the presence of changing operational and environmental conditions. Numerical and experimental results are presented to demonstrate the applicability of the proposed technique to crack detection.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.303-304
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• 2019

본 논문에서는 자연재해나 노후화로 인해 많은 건물의 외벽에 균열(Crack)이 생기고 있고, YOLO 딥러닝 기법을 이용하여 텐서플로우(Tensorflow)기반 균열 데이터의 학습 과정을 거쳐 가중치 파일을 획득하고, 이를 기반으로 효율적으로 건물 관리를 할 수 있는 드론(Drone)에 장착된 카메라를 이용한 실시간 영상으로 건물 외벽 균열을 촬영하고 균열을 감지하여 사용자 모니터에 감지된 균열을 경계 상자를 통해 검출하고, 검출 사진과 위치를 기록하도록 시스템을 개발하였다.

• International Journal of Highway Engineering
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• v.4 no.2 s.12
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• pp.33-42
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• 2002

The cracks in the pavements result from drying shrinkage, temperature change, repeated traffic loadings and so on. The reduction of soil support, spatting and many local failures are caused by water and incompressible foreign materials infiltrated into the cracks. In order to reduce this kind of problems the crack width must be controlled and managed by the accurate measurement. The current method is a visual survey using a microscope, which requires traffic blocking. The purpose of this study is to find the best condition to measure accurate crack width using automated pavement condition survey equipment running at the similar speed as other vehicles. In this study pavement surfaces are filmed on an enlarged scale by the camera with a zoom lens, and then the proper focal distance is determined according to the crack width through a pilot survey. The conditions for measurement of the accurate crack width using the image processing technique are suggested by comparing crack widths surveyed using a microscope in the field with those computed by various factors in the image processing program, STADI-2. In conclusion, the camera with a focal distance of 75m could detect crack range of 0.5mm$\sim$1.2mm In width with an accuracy of 80% for CRCP. The camera with a focal distance of 12.5mm could detect crack range of 1.8mm$\sim$3.3mm in width with an accuracy of 90% for asphalt pavement.

• 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.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• 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.