• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.260-261
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• 2019

Corrosion of rebar embedded reinforced concrete is the main cause of collapse and degradation of reinforced concrete structure that many researches are recently focused on these works. Methods of evaluating rebar corrosion are divided into physical and electrochemical methods. However, the result of Conventional methods are less reliable due to effect of internal and external environments. In this study, rebar corrosion detection sensor for embedded rebar of RC structures is evaluated through immersion test in NaCl solustion for 160hours. From the results, Rebar corrosion was ongoing and corrosion products are produced on rebar surface. The voltage is decreased as amount of corrosion production increased.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.4
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• pp.285-289
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• 2000

This study suggests a rebar detection technique for simultaneous detection of size and cover of embedded reinforcement in concrete. The structure of the probe made in this study is somewhat different from commercial ones. This probe has three sensing coils. Rebar size and cover depth can be evaluated by detecting and analyzing the signal from them. Amplitude and phase variation of each coil in the probe was investigated using an impedance analyzer and the loci of transfer functions of the coils were analyzed. The locus of transfer function from the sensing coil positioned inside excitation coil was simple as well known, but the others from the coils outside excitation coil were not so. Actual experiment on rebar detection was performed with our probe and an eddy current test system for various rebar sizes and depths. The signal shape according to variation of cover depths showed the same tendency with the transfer function loci acquired by impedance analyzer. The different variation pattern of signal enabled to evaluate rebar size and cover depth simultaneously.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.98-107
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• 2012

Reinforcing bars (rebar) comprise an integral part of a concrete structure, and play a major role in the safety and durability of the building. However, the actual placement or installation of rebar is not planned and controlled by the detailer. Recently, 4D simulations, using 3D model and scheduling software, have been used to improve the efficiency of the construction phrase. However, 4D simulators have not been introduced at the detailed level of work, such as rebar placement. Therefore, this paper suggests a BIM-based simulator for rebar placement to determine the sequence with which rebar is placed into the form. The system using Autodesk Revit API automatically generates rebar placement plans for a building structure, and labels the placement sequence of each individual bar or set of bars with ascending numbers. The placement sequence is then visualized using Autodesk Revit Structure 2012. This paper provides a short description of a field assessment and limits.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.675-678
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• 1999

Rebar corrosion in concrete containing both chloride ions and calcium nitrite inhibitors were investigated by the various electrochemical methods. Rebar corrosion was accelerated by applying the impressed current to the rebar in concrete. Effect of chloride content and inhibitors on rebar corrosion were evaluated. Accelerated corrosion technique is the method to measure the time to the initiation of cracks of reinforced concretes, by applying constant voltage between rebar and the stainless steel cathedes. The increase of concentration of chloride ions in concrete result in the increase in anodic currents and the reduction of time to crack. However addition of inhibitors did not improve corrosion resistance of rebar in concrete. Rebar corrosion in concrete with chloride ions and inhibitors was also analyzed by the immersed tests though the mesurement of corrosion potentials.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.69-80
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• 2024

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawing-based method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.255-256
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• 2023

The purpose of this study is to confirm the feasibility of exploring the location of reinforcing bars using the resistivity method in concrete structures.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.383-397
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• 2015

This article identifies and addresses current limitations on the use of numerical models for validation and/or calibration of damage detection methodologies that are based on the analysis of the high frequency response of the structure to identify the occurrence of abrupt anomalies. Distributed-plasticity non-linear fiber-based models in combination with experimental data from a full-scale reinforced concrete column test are used to point out current modeling techniques limitations. It was found that the numerical model was capable of reproducing the global and local response of the structure at a wide range of inelastic demands, including the occurrences of rebar ruptures. However, when abrupt sudden damage occurs, like rebar fracture, a high frequency pulse is detected in the accelerations recorded in the structure that the numerical model is incapable of reproducing. Since the occurrence of such pulse is fundamental on the detection of damage, it is proposed to add this effect to the simulated response before it is used for validation purposes.

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.371.2-371
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• 1999

• Journal of Surface Science and Engineering
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• v.54 no.4
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• pp.194-199
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• 2021

In this study, rebar corrosion detection sensor was fabricated using multi-walled carbon nanotubes (MWCNTs). MWCNTs were pre-treated in the acid electrolytes to attach the carboxylic acid to the surface of MWCNTs. The fabricated sensor was attached on the surface of rebar and it detected the corrosion of steel using LCR meter with variation of capacitance. The surface morphology and electrical properties were characterized using scanning electron microscope (SEM) and electrical test equipment, respectively. To verify the corrosion detection characteristics, comparison experiment using plastic bar was performed. Moreover, mechanism of corrosion detection sensor was discussed.