• Smart Structures and Systems
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• v.18 no.3
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• pp.569-584
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• 2016

This paper presents a method for stochastic modelling of fatigue crack growth and optimising inspection and maintenance strategy for the structural members of steel bridges. The fatigue crack evolution is considered as a stochastic process with uncertainties, and the Gamma process is adopted to simulate the propagation of fatigue crack in steel bridge members. From the stochastic modelling for fatigue crack growth, the probability of failure caused by fatigue is predicted over the service life of steel bridge members. The remaining fatigue life of steel bridge members is determined by comparing the fatigue crack length with its predetermined threshold. Furthermore, the probability of detection is adopted to consider the uncertainties in detecting fatigue crack by using existing damage detection techniques. A multi-objective optimisation problem is proposed and solved by a genetic algorithm to determine the optimised inspection and maintenance strategy for the fatigue affected steel bridge members. The optimised strategy is achieved by minimizing the life-cycle cost, including the inspection, maintenance and failure costs, and maximizing the service life after necessary intervention. The number of intervention during the service life is also taken into account to investigate the relationship between the service life and the cost for maintenance. The results from numerical examples show that the proposed method can provide a useful approach for cost-effective inspection and maintenance strategy for fatigue affected steel bridges.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.2
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• pp.151-157
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• 2004

Ultrasonic testing is a kind of nondestructive test to detect a crack or discontinuity in materials or on material surfaces by sending ultrasound to it. This conventional ultrasonic technique has some limitations in reliably detecting crack or accurately assessing materials in the case of complex-shaped power plant components such as a turbine blade root. An alternative method for such a difficult inspection is highly needed. In this study, application of a phased array ultrasonic testing (UT) system to a turbine blade, one of the critical power plant components, has been considered, and the particular incident angle has been determined so that the greatest track detectability and the most accurate crack length evaluation nay be achieved. The response of ultrasonic phased array was also analyzed to establish a special method to determine the track )ength without moving the transducer. The result showed that the developed method for crack length assessment is a more accurate and effective method, compared with the conventional method.

• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.361-368
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• 2005

This study proposes an algorithm for detection and analysis of cracks in digital image of concrete surface to automate the measurement process of crack characteristics such as width, length, and orientation based on image processing technique. The special features of algorithm are as follows: (1) application of morphology technique for shading correction, (2) improvement of detection performance based on enhanced binarization and shape analysis, (3) suggestion of calculation algorithms for width, length, and orientation. A MATLAB code was developed for the proposed algorithm, and then test was performed on crack images taken with digital camera to examine validity of the algorithm. Within the limited test in the present study, the proposed algorithm was revealed as accurately detecting and analyzing the cracks when compared to results obtained by a human and classical method.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.18-25
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• 2021

In many developed countries, such as South Korea, efficiently maintaining the aging infrastructures is an important issue. Currently, inspectors visually inspect the infrastructure for maintenance needs, but this method is inefficient due to its high costs, long logistic times, and hazards to the inspectors. Thus, in this paper, a novel crack inspection approach for concrete bridges is proposed using integrated image processing techniques. The proposed approach consists of four steps: (1) training a deep learning model to automatically detect cracks on concrete bridges, (2) acquiring in-situ images using a drone, (3) generating orthomosaic images based on 3D modeling, and (4) detecting cracks on the orthmosaic image using the trained deep learning model. Cascade Mask R-CNN, a state-of-the-art instance segmentation deep learning model, was trained with 3235 crack images that included 2415 hard negative images. We selected the Tancheon overpass, located in Seoul, South Korea, as a testbed for the proposed approach, and we captured images of pier 34-37 and slab 34-36 using a commercial drone. Agisoft Metashape was utilized as a 3D model generation program to generate an orthomosaic of the captured images. We applied the proposed approach to four orthomosaic images that displayed the front, back, left, and right sides of pier 37. Using pixel-level precision referencing visual inspection of the captured images, we evaluated the trained Cascade Mask R-CNN's crack detection performance. At the coping of the front side of pier 37, the model obtained its best precision: 94.34%. It achieved an average precision of 72.93% for the orthomosaics of the four sides of the pier. The test results show that this proposed approach for crack detection can be a suitable alternative to the conventional visual inspection method.

• Steel and Composite Structures
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• v.46 no.3
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.

• 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.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.777-782
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• 2001

Occurrence of crack in the Half-loc of rubble mound breakwater under T.T.P (Tetrapod) can cause serious problems in structural safety. There, probing of such cracks in marine structures is an important process in evaluating the overall integrity of structures. Ultrasonic, SASW(Spectral-Analysis-of-Surface-Waves)and Impect-Echo methods were used for the inspection of pilot concrete and SFC (Steel Fiber Concrete) block in this study. The advantage and limitations of these methods for non-destructive inspection in concrete blocks are investigated. As a result, it has been verified that these methods proved to present effective solution for detecting the crack of the pilot concrete block.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.5
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• pp.39-45
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• 2006

Structural health monitoring has been conducted by non-destructive evaluation method when a turbine rotor system of an aircraft engine has cracks. Local stiffness of a turbine rotor system is degraded and critical speed is changed due to the presence of cracks in rotor. Critical speed which is affected by location and depth of crack, is obtained using compliance matrix of cracked rotor. The database of the obtained critical speed is used to evaluate structural health monitoring of a rotor system of a gas turbine engine.

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.591-598
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• 2002

Leakage magnetic flux is occurred in the cracked area of magnetized specimens, and also it changes the magnetic domain area of the magneto-optical film positioned on the specimen. It causes the change of the optical permeability of the magnetic domain on the crack area. So crack images can be obtained easily using this principle. On the other hand, utilizing a laser in this method makes possible to perform a remote sensing by detecting the light intensity contrast between cracked area and normal area. This paper shows the application of non-destructive inspection system taking advantage of magneto-optical method for micro-cracks and presents examples applied to the several types of specimens having fatigue cracks and fabricated cracks using this method. Also the authors prove the possibility of this method as a remote sensing system under the oscillation load considering application to real fields.

• Tribology and Lubricants
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• v.11 no.5
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• pp.114-121
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• 1995

An ultrasonic technique using normal-incident compressional waves was used to evaluate the surface and subsurface damage in ceramics produced by Hertzian indentation. Damage was produced by a blunt indenter (tungsten carbide ball) in glass-ceramic, green glass and silicon nitride. The damage was classified into two types; (1) Hertzian cone crack, in green glass and fine grain silicon nitride, and (2) distributed subsurface micro fractures, without surface damage, produced in glass ceramic. The ultrasonic technique was successful in detecting cone craks. The measurement results with the Hertzian cone cracks indicated that cracks perpendicular to the surface could be detected by the normal-incident compressional waws. Also shown is the capability of normal-incident compressional waves in detection distributed micro-sized cracks size of subsurface microfractures.