• Steel and Composite Structures
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• v.50 no.1
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• pp.89-105
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• 2024

Multichannel analysis of surface waves (MASW) method has exhibited broad application prospects in the nondestructive detection of interfacial debonding in steel-concrete composite structures (SCCS). However, due to the structural diversity of SCCS and the high stealthiness of interfacial debonding defects, the feasibility of MASW method needs to be investigated in depth. In this study, synthetic parametric study on MASW nondestructive debonding detection for SCCSs is performed. The aim is to quantitatively analyze influential factors with respect to structural composition of SCCS and MASW measurement mode. First, stress wave composition and propagation process in SCCS are studied utilizing 2D numerical simulation. For structural composition in SCCS, the thickness variation of steel plate, concrete core, and debonding defects are discussed. To determine the most appropriate sensor arrangement for MASW measurement, the effects of spacing and number of observation points, along with distances between excitation points, nearest boundary, as well as the first observation point, are analyzed individually. The influence of signal type and frequency of transient excitation on dispersion figures from forwarding analysis is studied to determine the most suitable excitation signal. The findings from this study can provide important theoretical guidance for MASW-based interfacial debonding detection for SCCS. Furthermore, they can be instrumental in optimizing both the sensor layout design and signal choice for experimental validation.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.48-51
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• 2004

This paper is to study the response of flat plate slab-column connections consisting of various types of shear reinforcement and steel plate subjected to gravity loadings, mainly punching shear forces using the non-destructive testing, spectral analysis of surface waves and structural experiments. The base specimen failed due to punching shear generated from the gravity. The three other types of slab shear reinforcement and steel plate showed effective in resisting punching shear for these types of connections under gravity loading. This study has focused in evaluating the velocity response of a Surface wave during the early age as the poured concrete specimens have been hardened, the possibility of damage detection in the slab-column connection and the relationship between the punching shear forces and the surface wave velocities under the condition that the punching shear forces had gradually increased until the flat plate slab in slab-column connection had been failed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1479-1486
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• 2011

In this paper, a simple and robust algorithm is proposed for detecting straight line segments in a steel plate image. Line detection from a steel plate image is a fundamental task for analyzing and understanding of the image. The proposed algorithm is based on small eigenvalue analysis. The proposed approach scans an input edge image from the top left comer to the bottom right comer with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Before calculating the eigenvalue, each line segment is separated from the edge image where several line segments are overlapped to increase the accuracy of the line detection. Additionally, unnecessary line segments are eliminated by the number of pixels and the directional information of the detected line edges. The respects of the experiments emphasize that the proposed algorithm outperforms the existing algorithm which uses small eigenvalue analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.195-198
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• 2001

Backside defect of plate structure may grow due to fatigue or overload to cause critical failure during operation, so it is important to detect this kind of defect in line. For this purpose, nondestructive, non-contact and highly sensitive method is required. ESPI and Shearography are considered as useful method to satisfy these requirements. In this paper, the possibility of application of ESPI and Shearography to detect the backside defect of steel plate and to quantify the defect size was tested. For the experiment, some steel plates with defect on the backside were prepared. Experimental results for these plates showed that location and size of defect could be detected correctly by both of ESPI and Shearography.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.1
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• pp.139-148
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• 2015

In this paper, a new algorithm is proposed for detecting the product number of each thick plate and extracting each character of the product number from a image which contains several thick plates. In general, a image of thick plates contains several steal plates. To obtain the product number from the image, we first need to separate each plate. To do so, we use the line edges of thick plates and a clustering algorithm. After separating each plate, background parts are eliminated from the image of each plate. Background parts of an individual thick plate image consist of the dark part of steel and the white part of paint which is used for printing the product number. We propose a two-tiered method where dark background parts are first eliminated and then white parts are eliminated. Finally, each character is extracted from the product number image using the characteristics of product number. The results of the experiments on the various steal plates images emphasize that the proposed algorithm detects each thick plate and extracts the product number from a image effectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.75-82
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• 2000

This paper presents a method of detecting surface flaw of cold-rolled steel plate using image processing technique and a neural network classifier. The amount of steel plate surface image data is reduced by the wavelet transform. Features are extracted from the co-occurence matrix of the partial image corresponding to the low-frequency region, and a MLP neural network classifies into predetermined surface flaw categories. Simulations show the neural network classifier outperforms conventional vector quantization method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.254-261
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• 2021

This study was intended to develop an inspection method to detect defects in closed-cell steel members in steel girder bridges. The ultrasonic pulse velocity method was selected as a rapid and effective method to identify thickness changes of steel specimens caused by corrsion. This study developed an algorithm to expedite the process and improve the accuracy in the prediction of steel plate thickness. Also, both static and continuous scanning methods were compared to each other to identify the difference in accuracy, but the results revealed that both methods produce almost the same results. This study also provided the idea to calculate the height of water contained in the closed-cell steel member and results of laboratory experimental results. The water heights which is thicker than the steel plate thickness were detectable and predicted using the idea suggested by this study, but the water heights lower than the steel plate thickness were not possible. However, the results showed whether the steel member contains water or not.

• Steel and Composite Structures
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• v.51 no.6
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• pp.713-727
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• 2024

Damage detection in structures using the change of modal parameters (modal shapes and natural frequencies) has achieved satisfactory results. However, as modal shapes and natural frequencies alone may not provide enough information to accurately detect damages. Therefore, a hybrid singular value decomposition and deep belief network approach is developed to effectively identify damages in aluminum plate structures. Firstly, damage locations are determined using singular value decomposition (SVD) to reveal the singularities of measured displacement modal shapes. Secondly, using experimental modal analysis (EMA) to measure the natural frequencies of damaged aluminum plates as inputs, deep belief network (DBN) is employed to search damage severities from the damage evaluation database, which are calculated using finite element method (FEM). Both simulations and experimental investigations are performed to evaluate the performance of the presented hybrid method. Several damage cases in a simply supported aluminum plate show that the presented method is effective to identify multiple damages in aluminum plates with reasonable precision.

• Journal of Magnetics
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• v.20 no.3
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• pp.312-316
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• 2015

Unlike conventional Eddy Current Test (ECT), Pulsed Eddy Current (PEC) uses a multiple-frequency current pulse through the excitation coil. In the present study, the detection of subsurface cracks using a specially designed probe that allows the detection of a deeper crack with a relatively small current density has been attempted using the PEC technique. The tested sample is a piece of 304 stainless steel (SS304) with a thickness of 30mm. Small electrical discharge machining (EDM) notches were put in the test sample at different depths from the surface to simulate the subsurface cracks in a pipe. The designed PEC probe consists of an excitation coil and a Hall sensor and can detect a subsurface crack as narrow and shallow as 0.2 mm wide and 2 mm deep. The maximum distance between the probe and the defect is 28 mm. The peak amplitude of the detected pulse is used to evaluate the cracks under the sample surface. In time domain analysis, the greater the crack depth the greater the peak amplitude of the detected pulse. The experimental results indicated that the proposed system has the potential to detect the subsurface cracks in stainless steel plates.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.742-747
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• 2015

The quality of steel plate products is determined by the number of defects and the process problems are estimated by shapes of defects. Therefore pinholes defects of cold rolled steel have to be controlled. In order to improve productivity and quality of products, within each production process, the product is inspected by an adequate inspection system individually in the lines of steelworks. Among a number of inspection systems, we focus on the pinholes detection system. In this paper, we propose an embedded system using FPGA which can detect pinholes defects. The proposed system is smaller and more flexible than a traditional system based on expensive frame grabbers and PC. In order to detect consecutive defects, FPGAs acquire two dimensional image and process the image in real time by using correlation of lines. The proposed pinholes detection algorithm decreases arithmetic operations of image processing and also we designed the hardware to shorten the data path between logics due to decreasing propagation delay. The experimental results show that the proposed embedded system detects the reliable number of pinholes in real time.