• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.61-70
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• 2023

This study presents an analytical study of investigating the effect of shock waves generated by the hydrogen detonation and damage to structures for the safety evaluation of hydrogen storage facilities against detonation. Blast scenarios were established considering the volume of the hydrogen storage facility of 10 L to 50,000 L, states of charge (SOC) of 50% and 100%, and initial pressures of 50 MPa and 100 MPa. The equivalent TNT weight for hydrgen detonation was determined considering the mechanical and chemical energies of hydrogen. A hydrogen detonation model for the converted equivalent TNT weight was made using design equations that improved the Kingery-Bulmash design chart of UFC 3-340-02. The hydrogen detonation model was validated for overpressure and impulse in comparison to the past experimental results associated with the detonation of hydrogen tank. A parametric study based on the blast scenarios was performed using the validated hydrogen detonation model, and design charts for overpressure and impulse according to the standoff distance from the center of charge was provided. Further, design charts of the three-stage structural damage and standoff distance of adjacent structures according to the level of overpressure and impact were proposed using the overpressure and impulse charts and pressure-impulse diagrams.

• Journal of the Korean Geotechnical Society
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• v.39 no.9
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• pp.13-24
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• 2023

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.515-521
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• 2023

The radiation dose received by the patient varies according to the tube current and time used during dental intraoral imaging. A large amount of tube current is required for image quality, but the radiation dose to the patient increases accordingly. Therefore, in this study, the optimal amount of tube current that can reduce the radiation dose received by the patient while securing the image quality was calculated through the evaluation of the image quality according to the tube current used during intraoral imaging through simulation. The average tube current, time, and tube voltage presented in the Guidelines for Diagnostic Reference Level for intraoral radiography were used as basic imaging conditions, and images were obtained when only the tube current was changed, and then the optimal tube current was compared and analyzed with the basic image quantity was calculated. Images were obtained by changing the tube current to 0.1, 0.5, 1, 2, 3, 4 and 5 mA under the basic conditions of 63 kV, 6 mA, and 0.29 s. The obtained image was evaluated for structural similarity index with the image taken under the condition of 6 mA using the ICY program. As a result, even under the condition of 0.5 mA tube current, the index of structural similarity with the image of 6 mA was evaluated to be high. Based on these results, it is considered that the radiation dose given to the patient can be greatly reduced if imaging is performed at 0.5 mA instead of 6 mA during dental intraoral imaging.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.67-76
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• 2023

In this study, in order to enhance the joint capacity between the existing reinforced concrete (R/C) frame and the reinforcement member, we proposed a novel concept of Internal Composite Seismic Strengthening Method (CSSM) for seismic retrofit of existing domestic medium-to-low-rise R/C buildings. The Internal CSSM rehabilitation system is a type of strength-enhancing reinforcement systems, to easily increase the ultimate horizontal shear capacity of R/C structures without seismic details in Korea, which show shear collapse mechanism. Two test specimens of full-size two-story R/C frame were fabricated based on an existing domestic R/C building without seismic details, and then retrofitted by using the proposed CSSM seismic system; therefore, one control test specimen and one test specimen reinforced with the CSSM system were used. Pseudo-dynamic testing was carried out to evaluate seismic strengthening effects, and the seismic response characteristics of the proposed system, in terms of the maximum shear force, response story drift, and seismic damage degree compared with the control specimen (R/C bare frame). Experiment results indicated that the proposed CSSM reinforcement system, internally installed to the existing R/C frame, effectively enhanced the horizontal shear force, resulting in reduced story drift of R/C buildings even under a massive earthquake.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.693-699
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• 2023

Ultrasound is widely used in the medical field for non-destructive and non-invasive disease diagnosis. In order to improve the disease diagnosis accuracy of diagnostic medical images, improving spatial resolution is a very important factor. In this study, we aim to model the super resolution convolutional neural network (SRCNN) algorithm in ultrasound images and analyze its applicability in the medical diagnostic field. The study was conducted as an experimental study using Field II simulation and open source clinical liver hemangioma ultrasound imaging. The proposed SRCNN algorithm was modeled so that end-to-end learning can be applied from low resolution (LR) to high resolution. As a result of the simulation, we confirmed that the full width at half maximum in the phantom image using a Field II program was improved by 41.01% compared to LR when SRCNN was used. In addition, the peak to signal to noise ratio (PSNR) and structural similarity index (SSIM) evaluation results showed that SRCNN had the excellent value in both simulated and real liver hemangioma ultrasound images. In conclusion, the applicability of SRCNN to ultrasound images has been proven, and we expected that proposed algorithm can be used in various diagnostic medical fields.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.1-8
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• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.37-46
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• 2024

This study investigates the evolution of concrete damage due to chloride-induced steel corrosion through Impact-echo (IE) testing. Three reinforced concrete specimens, each measuring 1500 mm in length, 400 mm in width, and 200 mm in thickness, were fabricated using three concrete mixture proportions of blended cement types: ordinary Portland cement, ground granulated blast-furnace slag and fly ash. Steel corrosion in the concrete was accelerated by impressing a 0.5 A current following a 35-day cycle of wet-and-dry saturation in a 3% NaCl solution. Initial IE data collected during the saturation phase showed no significant changes, indicating that moisture had a minimal impact on IE signals and highlighting the slow progress of corrosion under natural conditions. Post-application of current, however, there was a noticeable decline in both IE peak frequency and the P-wave velocity in the concrete as the duration of the impressed current increased. Remarkably, progressive monitoring of IE proves highly effective in capturing the critical features of steel-corrosion induced concrete deterioration, such as the onset of internal damages and the rate of damage propagation. These results demonstrate the potential of progressive IE data monitoring to enhance the reliability of diagnosing and prognosticating the evolution of concrete damage in marine environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.32-38
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• 2024

In the case of apartment building, it is difficult to conduct non-destructive testing due to the actual presence of people and the dust and noise generated during the core test, so inspections are performed each time in the common area and underground parking lot, and the tests are conducted on the finishing material rather than on the concrete surface due to low-cost orders. As the process progresses, poor inspection is inevitable. In addition, the proposed formulas for strength estimation have large fluctuations depending on the differences in test conditions and environments, and even if they show the same measured value, the deviation between each proposed formula is large, making it difficult to accurately estimate strength, making it difficult to use. Accordingly, we would like to select finishing materials mainly used in apartment complexes and compare and evaluate the compressive strength of concrete according to the type of finishing material by using non-destructive testing methods directly on the finishing materials without removing the finishing materials. The reliability evaluation results of the estimated compressive strength of concrete using the ultrasonic velocity method according to the type of finishing material are as follows. The error rate between the estimated compressive strength and compressive strength derived through the ultrasonic velocity method shows a wide range of variation, ranging from 21.83% to 58.89%. The effect of the presence or absence of finishing materials on the estimated compressive strength was found to be insignificant. Accordingly, it is necessary to select more types of finishing materials and study ultrasonic velocity methods according to the presence or absence of finishing materials, and to study estimation techniques that can increase reliability.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.5
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• pp.179-186
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• 2006

In this study, Multi-Layer Perceptron(MLP) among models of Artificial Neural Network(ANN) is used for the development of a model that evaluates the bending capacities of reinforced concrete beams strengthened by FRP Rebar. And the data of the existing researches are used for materials of ANN model. As the independent variables of input layer, main components of bending capacities, width, effective depth, compressive strength, reinforcing ratio of FRP, balanced steel ratio of FRP are used. And the moment performance measured in the experiment is used as the dependent variable of output layer. The developed model of ANN could be applied by GFRP, CFRP and AFRP Rebar and the model is verified by using the documents of other previous researchers. As the result of the ANN model presumption, comparatively precise presumption values are achieved to presume its bending capacities at the model of ANN(0.05), while observing remarkable errors in the model of ANN(0.1). From the verification of the ANN model, it is identified that the presumption values comparatively correspond to the given data ones of the experiment. In addition, from the Sensitivity Analysis of evaluation variables of bending performance, effective depth has the highest influence, followed by steel ratio of FRP, balanced steel ratio, compressive strength and width in order.

• Korean Journal of Construction Engineering and Management
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• v.25 no.5
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• pp.15-24
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• 2024

System support is a facility that is temporarily installed to support vertical loads at construction sites, and is assembled and installed by reused individual members. These characteristics are likely to lead to poor performance of installed system supports, and even though it is institutionalized to check structural safety at the their design phase, accidents continue to occur at the construction site. Accordingly, safety management of system support is implemented through various institutional methods, but the current system does not consider the performance degradation of temporary facilities due to the reuse of individual temporary members. Therefore, the purpose of this study is to verify the performance of assembled system support. In order to do achieve this purpose, the authors divided individual system supports into unused and used groups and performed compression performance test with defined models assembled with those two groups of system supports. The results of this study are expected to be meaningful as a research case that can quantitatively evaluate safety systems and standards for the performance of existing temporary facilities and suggest directions for improving the safety management system of temporary facilities in the future.