• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.353-359
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• 2010

Cementitious matrix based composites are vulnerable to the drying shrinkage crack during the curing process. In this study, the drying shrinkage induced fracture behavior of the fiber reinforced concrete is simulated and the effects of the fiber reinforcement conditions on the fracture characteristics are analysed. The numerical model is composed of conduit elements and rigid-body-spring elements on the identical irregular lattice topology, where the drying shrinkage is presented by the coupling of nonmechanical-mechanical behaviors handled by those respective element types. Semi-discrete fiber elements are applied within the rigid-body-spring network to model the fiber reinforcement. The shrinkage parameters are calibrated through the KS F 2424 free drying shrinkage test simulation and comparison of the time-shrinkage strain curves. Next, the KS F 2595 restrained drying shrinkage test is simulated for various fiber volume fractions and the numerical model is verified by comparison of the crack initiating time with the previous experimental results. In addition, the drying shrinkage cracking phenomenon is analysed with change in the length and the surface shape of the fibers, the measurement of the maximum crack width in the numerical experiment indicates the judgement of the crack controlling effect.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.33-39
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• 2024

Inspecting cracks to determine a structure's condition is crucial for accurate safety diagnosis. However, visual crack inspection methods can be subjective and are dependent on field conditions, thereby resulting in low reliability. To address this issue, this study automates the detection of concrete cracks in image data using ResNet, FPN, and the Mask R-CNN components as the backbone, neck, and head of a convolutional neural network. The performance of the proposed model is analyzed using the intersection over the union (IoU). The experimental dataset contained 1,203 images divided into training (70%), validation (20%), and testing (10%) sets. The model achieved an IoU value of 95.83% for testing, and there were no cases where the crack was not detected. These findings demonstrate that the proposed model realized highly accurate detection of concrete cracks in image data.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.568-573
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• 2007

Environmental information (temperature, humidity, vibration, $CO_2$, gas leakage, etc.) of building is an essential item to manage and monitor a building. For intelligent building, it is necessary to get temperature and illumination information to save energy and crack information to prevent structural problems. Moreover, temperature and gas leakage information to alarm a tire precaution, or humidity information to maintain comfortable environment. However, there have not been many researches on systems for gathering environmental information and building maintenance due to high costs. In this paper, wireless sensor network technology is applied to collecting building environmental information. Wireless sensor network is one of the latest issues and has low-power consumption, low-cost, self-configuration features.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.152-165
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• 1998

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaoral post-process system for precision rate enhancement of ultrasonic pattern recognition. Chaos features extracted from time series data for analysis quantitatively weld defects For this purpose, feature extraction objectives in this study are fractal dimension, Lyapunov exponent, shape of strange attrator. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shifts such as nearby 0.5, 1.0 skip distance. Such difference in chaoticity enables the evaluation of unique features of defects in the weld zone. In quantitative chaos fenture extraction, feature values of 0.835 and 0.823 in the case of slag inclusion and 0.609 and 0.573 in the case of crack were suggested on the basis of fractal dimension and Lyapunov exponent. Proposed chaoral post-process system in this study can enhances precision rate of ultrasonic pattern recognition results from defect signals of weld zone, such as slag inclusion and crack.

• Steel and Composite Structures
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• v.26 no.2
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• pp.241-253
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• 2018

Mode II fracture toughness, $K_{IIC}$, of single-ply triaxially woven fabric (TWF) composite due to tow waviness and anisotropy effects were numerically and experimentally studied. The numerical wavy beam network model with anisotropic material description denoted as TWF anisotropic was first validated with experimental Mode II fracture toughness test employing the modified compact tensile shear specimen configuration. 2D planar Kagome and TWF isotropic models were additionally constructed for various relative densities, crack lengths, and cell size parameters for examining effects due to tow waviness and anisotropy. $K_{IIC}$ generally increased with relative density, the inverse of cell size, and crack length. It was found that both the waviness and anisotropy of tow inflict a drop in $K_{IIC}$ of TWF. These effects were more adverse due to the waviness of tow compared to anisotropy.

• Transactions of Materials Processing
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• v.27 no.2
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• pp.100-106
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• 2018

In the continuous casting process, the incidence of transverse surface cracks on the piece may occur by multiple and diverse variables. It is noted that mathematical models may predict only the occurance of the transverse surface cracks, but can require a lot of time (more than three days) to produce a result with this process. This study applied neural networks to predict whether the cracks on the piece surface occurs or does not occur. The computation time was shortened to three minutes, making it applicable to an on-line program, which predicts the non-cracks or cracks of the piece surface in the actual continuous casting process. In addition, the operating conditions to prevent the occurrence of the transverse surface cracks, using decision boundaries were also suggested.

• Structural Engineering and Mechanics
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• v.8 no.3
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• pp.273-283
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• 1999

Two physical experiments are performed to verify the effectiveness of beam-particle model for simulating the progressive failure of particulate composites such as sandstone and concrete. In the numerical model, the material is schematized at the meso-level as an assembly of discrete, interacting particles which are linked through a network of brittle breaking beams. The uniaxial compressive tests of cubic and parallelepipedal specimens made of carbon steel rod assembly which are glued together by a mixture are represented. The crack patterns and load-displacement response observed in the experiments are in good agreement with the numerical results. In the application respect of beam-particle model to the particulate composites, the influence of defects, particle arrangement and boundary conditions on crack propagation is approached, and the correlation existing between the cracking evolution and the level of loads imposed on the specimen is characterized by fractal dimensions.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.