• Journal of the Korean Society for Nondestructive Testing
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• v.5 no.1
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• pp.9-23
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• 1985

Boshin-Kak Bell, which is one of the largest bells, is located at Chong-Ro Square in the center of Seoul. The bell has been struct early morning and late evening for time-report since the 14th century in Lee Era. Therefore Boshin-Kak Bell has been an intimate old friend of Seoul citizen more than 500 years. Unfortunately, motal large cracks were found inside this bell in the horizontal and vertical directions in 1979. The present paper has investigated the propagated bell-crack by ultrasonic flaw detecting method, and the stress level, bell vibration and weight measurement by electric wire resistance strain gauge method. The results indicate that they are useful for further study of Korean bell by nondestructive test.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.181-206
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• 2022

This paper presents experimental modal analysis and static load testing results to validate the accuracy of dynamic parameters-based damage locating indices in RC structures. The study investigates the accuracy of different dynamic-based damage locating indices compared to observed crack patterns from static load tests and how different damage levels and scenarios impact them. The damage locating indices based on mode shape curvature and mode shape fourth derivate in their original forms were found to show anomalies along the beam length and at the supports. The modified forms of these indices show higher sensitivity in locating single and multi-cracks at different damage scenarios. The proposed stiffness reduction index shows good sensitivity in detecting single and multi-cracks. The proposed anomalies elimination procedure helps to remove the anomalies along the beam length. Also, the adoption of the proposed weighting method averaging procedure and normalization procedure help to draw the overall crack pattern based on the adopted set of modes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1353-1358
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• 2010

In this paper, we propose a detection method that automatically detects concrete surface cracks using fuzzy method in the image of concrete surface cracks. First, the proposed method detecting concrete surface cracks detects the candidate crack areas by applying R, G, B channel values of the concrete crack image to fuzzy method. We finally detect cracks by the density information about the detected candidate areas after we remove the detailed noises on the image of the concrete surface cracks. The experiments using real concrete images showed that the proposed method is greatly improved of crack detection compared with the conventional methods.

• Composites Research
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• v.24 no.3
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• pp.25-30
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• 2011

The studies on the non-destructive testing methods of the composite materials are very important for improving their reliability and safety. AE(Acoustic Emission) can evaluate the defects by detecting the emitting strain energy when elastic waves are generated by the generation and growth of a crack, plastic deformation, fiber breakage, matrix cleavage or delamination. In this paper, the AE signals of the filament wound composite cylinder and sandwich cylinder during the pressure test were measured and analyzed. The signal characteristics of PVDF sensors were measured, and an AE signal analyzer which had the band-pass filter and L-C resonance filter were designed and fabricated. Also, the crack detection capability of the fabricated AE signal analyzer wes evaluated during the pressure tests of the filament wound composite cylinder and the sandwich cylinder.

• 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.

• Journal of the Korean Society for Railway
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• v.10 no.1 s.38
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• pp.1-6
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• 2007

Railway wheels in service are regularly checked by ultrasonic testing, acoustic emission and eddy current testing method and so on. However, ultrasonic testing is sometimes inadequate for sensitively detecting the cracks in railway wheel which is mainly because of the fact of crack closure. Recently, many researchers have actively fried to improve precision for defect detection of railway wheel. The development of a nondestructive measurement tool for wheel defects and its use for the maintenance of railway wheels would be useful to prevent wheel failure. The induced current focusing potential drop(ICFPD) technique is a new non-destructive tasting technique that can detect defects in railway wheels by applying on electro-magnetic field and potential drops variation. In the present paper, the ICFPD technique is applied to the detection of surface and internal defects for railway wheels. To defect the defects for railway wheels, the sensor for ICFPD is optimized and the tests are carried out with respect to 4 surface defects and 6 internal defects each other. The results show that the surface crack depth of 0.5 mm and internal crack depth of 0.7 mm in wheel tread could be detected by using this method. The ICFPB method is useful to detect the defect that initiated in the tread of railway wheels

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.7-13
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• 2022

Digital image correlation (DIC) is a method used to measure the displacement and strain of structures. It involves transforming and analyzing images before and after deformation using correlation coefficients from irregular light and shade on the surface of structures. In the present study, a microspeckle pattern was applied to the surface of a specimen to identify initial cracking. The test specimen constituted CFRP composites laminated on a curved Al liner The specimen was manufactured by stacking 100 ply of CFRP prepregs in the 0° and 90° directions in a three-point bending test. The equivalent strain was evaluated through DIC analysis after monitoring deformation using a CCD camera. Fracture shape was observed using a microscope. The equivalent strain contour distribution was checked until the maximum load fracture occurred at the center of the test specimen. Variations in the strain indicated the initial occurrence and progression of microcracks. These results can be used to improve the accuracy of detecting micro crack initiation and to achieve structural stability.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.90-105
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• 2004

Machines for crack sealing automation have been continually developed since the early 1990's because of the effectiveness of crack sealing that would be able to improve safety, quality and productivity. It has been considered challenging problem to detect crack network in pavement which includes noise (oil marks, skid marks, previously sealed cracks and inherent noise). Moreover, it is required to develop crack network mapping and modeling algorithm in order to accurately inject sealant along to the middle of cut crack network. The primary objective of this study is to propose machine vision algorithms (digital image processing algorithm and path planning algorithm) for fully automated pavement crack sealing. It is anticipated that the effective use of the proposed machine vision algorithms would be able to reduce error rate in image processing for detecting, mapping and modeling crack network as well as improving quality and productivity compared to existing vision algorithms.