• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.327-335
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• 2021

Microcracks can rapidly grow and develop in high-strength steels used in offshore structures. It is important to render these microcracks harmless to ensure the safety and reliability of offshore structures. Here, the dependence of the aspect ratio (As) of the maximum depth of harmless crack (a_hlm) was evaluated under three different conditions considering the threshold stress intensity factor (Δk_th) and residual stress of offshore structural steel F690. The threshold stress intensity factor and fatigue limit of fatigue crack propagation, dependent on crack dimensions, were evaluated using Ando's equation, which considers the plastic behavior of fatigue and the stress ratio. a_hlm by peening was analyzed using the relationship between Δk_th obtained by Ando's equation and Δk_th obtained by the sum of applied stress and residual stress. The plate specimen had a width 2W = 12 mm and thickness t = 20 mm, and four value of As were considered: 1.0, 0.6, 0.3, and 0.1. The a_hlm was larger as the compressive residual stress distribution increased. Additionally, an increase in the values of As and Δk_th(l) led to a larger a_hlm. With a safety factor (N) of 2.0, the long-term safety and reliability of structures constructed using F690 can be secured with needle peening. It is necessary to apply a more sensitive non-destructive inspection technique as a non-destructive inspection method for crack detection could not be used to observe fatigue cracks that reduced the fatigue limit of smooth specimens by 50% in the three types of residual stresses considered. The usefulness of non-destructive inspection and non-damaging techniques was reviewed based on the relationship between a_hlm, a_NDI (minimum crack depth detectable in non-destructive inspection), a_{cr N} (crack depth that reduces the fatigue limit to 1/N), and As.

• Smart Structures and Systems
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• v.2 no.2
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• pp.101-113
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• 2006

Structural health monitoring results obtained with the electro-mechanical (E/M) impedance techniqueand Lamb wave transmission methods during fatigue crack propagation of an Arcan specimen instrumented with piezoelectric wafer active sensors (PWAS) are presented. The specimen was subjected in mixed-mode fatigue loading and a crack was propagated in stages. At each stage, an image of the crack and the location of the crack tip were recorded and the PWAS readings were taken. Hence, the crack-growth in the specimen could be correlated with the PWAS readings. The E/M impedance signature was recorded in the 100 - 500 kHz frequency range. The Lamb-wave transmission method used the pitch-catch approach with a 3-count sine tone burst of 474 kHz transmitted and received between various PWAS pairs. Fatigue loading was applied to initiate and propagate the crack damage of controlled magnitude. As damage progressed, the E/M impedance signatures and the waveforms received by receivers were recorded at predetermined intervals and compared. Data analysis indicated that both the E/M impedance signatures and the Lamb-wave transmission signatures are modified by the crack progression. Damage index values were observed to increase as the crack damage increases. These experiments demonstrated that the use of PWAS in conjunction with the E/M impedance and the Lamb-wave transmission is a potentially powerful tool for crack damage detection and monitoring in structural elements.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.4
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• pp.352-357
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• 2008

The detection of micro cracks in materials at the early stage of fracture is important in many structural safety assurance problems. The nonlinear ultrasonic technique (NUT) has been considered as a positive method for this, since it is more sensitive to micro crack than conventional linear ultrasonic methods. The basic principle is that the waveform is distorted by nonlinear stress-displacement relationship on the crack interface when the ultrasonic wave transmits through, and resultantly higher order harmonics are generated. This phenomenon is called the contact acoustic nonlinearity (CAN). The purpose of this paper is to prove the applicability of CAN experimentally by detection of micro fatigue crack artificailly initiated in Aluminum specimen. For this, we prepared fatigue specimens of Al6061 material with V-notch to initiate the crack, and the amplitude of second order harmonic was measured by scanning along the crack direction. From the results, we could see that the harmonic amplitude had good correlation with COD and it can be used to detect the crack depth in more accurately than the common 6 dB drop echo method.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.250-253
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• 2001

Recently, based on the smart structure concept, optical fiber sensors have been increasingly applied to monitor the various engineering and civil structural components. Repairs based on adhesively bonded fiber reinforce composite patches are more structurally efficient and much less damaging to the parent structure than standard repairs based on mechanically fastened metallic patches. As a result of the high reinforcing efficiency of bonded patches fatigue cracks can be successfully repaired. However, when such repairs are applied to primary structures, it is needed to demonstrate that its loss can be immediately detected. This approach is based on the "smart patch" concept in which the patch system monitors its own health. The objective of this study is to evaluate the potentiality of application of transmission-type extrinsic Fabry-Perot optical fiber sensor (TEFPI) to the monitoring of crack growth behavior of composite patch repaired structures. The sensing system of TEFPI and the data reduction principle for the detection of crack detection are presented. Finally, experimental results from the tests of center-cracked-tension aluminum specimens repaired with bonded composite patch is presented and discussed.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• 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 for structural maintenance and inspection
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• v.3 no.4
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• pp.215-220
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• 1999

Concrete micro-cracks that are grown while the structures are under construction or in service, propagate gradually or rapidly by external forces and environmental effects. As described above, almost concrete structures generally have cracks, so for the safety and durability of structures, studies to detect cracks using nondestructive tests have been treated in great deal. The purpose of this study is to evaluate characteristics of AE signals detected from notched concrete beams bending test with different loading using one of nondestructive test, Acoustic Emission (AE) method. Furthermore this study predicts the location of initial crack and measures direction of crack propagation for on-line monitoring before the crack really grows in structures by using two-dimensional AE source location based on rectangular method with three-point bending test. This will allow efficient maintenance of concrete structures through monitoring of internal cracking based on acoustic emission method.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• 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.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.