• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.115-135
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• 2021

This paper focuses on examining the effects of span arrangements on displacement responses of plan-symmetric RC frame buildings designed using the direct displacement-based design (DDBD) method by employing non-linear analyses and the skew seismic attack. In order to show the desired performance of DDBD design approach, the force-based design approach is also used to examine the seismic performance of the selected structures. To realize this objective, 8-story buildings with different plans are selected. In addition, the dynamic behavior of the structures is evaluated by selecting 3, 7, and 12-story buildings. In order to perform non-linear analyses, OpenSees software is used for modeling buildings. Results of an experimental model are used to validate the analytical model implemented in OpenSees. The results of non-linear static and non-linear dynamic analyses indicate that changing span arrangements does not affect estimating the responses of structures designed using the DDBD approach, and the results are more or less the same. Next, in order to apply the earthquake in non-principle directions, DDBD structures, designed for one-way performance, are designed again for two-way performance. Time history analyses are performed under a set of artificial acceleration pairs, applied to structures at different angles. It is found that the mean maximum responses of earthquakes at all angles have very good agreement with the design-acceptable limits, while the response of buildings along the height direction has a relatively acceptable and uniform distribution. Meanwhile, changes in the span arrangements did not have a significant effect on displacement responses.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.655-669
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• 2022

Passive vibration control devices like tuned liquid column dampers (TLCD) not only significantly reduce buildings' vibrations but also can serve as a water storage facility. The recently introduced modified form of TLCD known as tuned liquid column ball damper (TLCBD) suppressed external vibration efficiently compared to traditional TLCD. For excellent performance, the mass ratio of TLCBD should be in the range of 5% to 7%, which does not include the mass of the ball. This additional mass of the ball increases the overall structure mass. Therefore, in this paper, an effort is made to reduce the mass of TLCBD. For this purpose, a new modified version of TLCBD known as tuned liquid column hollow ball damper (TLCHBD) is proposed. The existing mathematical modeling of TLCBD is used for this new damper by updating the numerical values of the mass and mass moment of the ball. Analytically the optimal design parameters are obtained. Numerically the TLCHBD is investigated with a single degree of freedom structure under harmonic and seismic loadings. It is found that TLCHBD performance is similar to TLCBD in both loadings' cases. To validate the numerical results, an experimental study is conducted. The mass of the ball of TLCHBD is reduced by 50% compared to the ball of TLCBD. Both the arrangements are studied with a multi-degree of freedom structure under harmonic and seismic loadings using a shake table. The results of the experimental study confirm the numerical findings. It is found that the performance behavior of both the dampers is almost similar under harmonic and seismic loadings. In short, the TLCHBD is lighter in weight than TLCBD but has a similar vibration suppression ability.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.1
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• pp.37-47
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• 2023

A new clamped mechanical splice system was proposed to develop structural performance and constructability for precast concrete connections. The proposed mechanical splice resists external loading immediately after the engagement. The mechanical splices applicable for both large-scale rebars for plants and small-scale rebars for buildings were developed with the same design concept. Quasi-static lateral cyclic loading tests were conducted with reinforced and precast concrete members to verify the seismic performance. Also, shaking table tests with three types of seismic wave excitation, 1) random wave with white noise, 2) the 2016 Gyeongju earthquake, and 3) the 1999 Chi-Chi earthquake, were conducted to confirm the dynamic performance. All tests were performed with real-scale concrete specimens. Sensors measured the lateral load, acceleration, displacement, crack pattern, and secant system stiffness, and energy dissipation was determined by lateral load-displacement relation. As a result, the precast specimen provided the emulative performance with RC. In the shaking table tests, PC frames' maximum acceleration and displacement response were amplified 1.57 - 2.85 and 2.20 - 2.92 times compared to the ground motions. The precast specimens utilizing clamped mechanical splice showed ductile behavior with energy dissipation capacity against strong motion earthquakes.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• The Journal of Korean Physical Therapy
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• v.16 no.4
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• pp.90-114
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• 2004

This study was conducted to examine the influence and effect of muscle enforcement program on Activity of daily living(ADL) improvement and posture balance of the old, and to provide more effective muscle enforcement program and educational data. The muscle enforcement exercise program was performed on the old(institution, 16 men, 10 women) for 8 weeks from April 22, 2002 through June 17,2002. Programed Exercise 1 - Exercise 10 were practised 8 times per program for 3 days a week. The load of exercise was increased per two weeks. The methods of measurement were questionnaire, Indiana 47903(action-response analysis machine) and Sample exercise protocol for KAT 2000(balance training device). SAS/PC statistic analysis was used for data analysis. T-test was used for analysis of change before and after exercise in this study. The summary and conclusions are as follows. 1. On subjectively recognized health states, the healthy were $42.3\%$. On the satisfaction with health states, the satisfied were $50.0\%$. On the factors of effects on daily-life behavior performance, the group who had troubles was $50\%$ and the group who was so and so was $34.6\%$ compared with the old of the same age. On prospect about health states in the future, the group who would be better was $38.\%$. On effective methods for problem solving, exercise was $42.3\%.\;88.5\%$ of respondents answered the need of health care. The participation intention in health program was $92.3\%$. 2. On the change of psychological emotion and behavior aspects, the group who had repeated complaints or anxieties and reduced activities or interests was effective(P<0.01). 3. On the improvement effects of IADL difficulties, the group who had difficulties in doing daily-life indoors was improved effectively compared with before and after exercise(P<0.01). On medication management, the effects of improvement after exercise were high compared with before exercise(P<0.01), the effects of improvement was high on the whole. 4. On the effects of ADL function improvement, putting on upper clothing and lower clothing was improved effectively(P<0.05), toilet use and individual sanitation was improved effectively(P<0.05). 5. On the effects of action-response, the results of 8weeks regular exercise program were not different significantly compared with before and after exercise. The behavior quickness of the old by muscle enforcement program was not increased. This means that the old needs much time for exercise sense training because of the regression of cognition sense. 6. In the effect of posture balance, the whole grades were effective from 1272.69 before excercise to 476.92 after exercise(P<0.01). Especially right balance 657.65 was lowered to 208.57 after exercise most effectively(P<0.01). Rear balance 776.34 before exercise was lowered to 136.65 after exercise. The results of measurement were significant(P<0.05).

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.1-17
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• 2024

Analyzing the tunnel excavation behavior and its effect on the surrounding ground involves large deformation behavior. Therefore, in order to properly simulate the tunnel excavation process and rigorously investigate the actual effect of excavation on surrounding ground and tunnel structure large deformation analysis method is required. In this study, two major numerical approaches capable of considering large deformations behavior were applied to investigate the effect of tunnel boring machine excavation on the surrounding ground: coupled Eulerian-Lagrangian (CEL) and the automatic remeshing (AR) method. Relative performance of both approaches was evaluated through the ground response due to TBM excavation. The ground response will be quantified by estimating the range of the excavation damaged zone (EDZ). By comparing the results, the range of the EDZ will be suggested on the vertical and horizontal direction along the TBM excavation surface. Based on the computed results, it was found that the size of EDZ around the excavation surface and the tendencies was in good agreement among the two approaches. Numerical results clearly show that the size of the EDZ around the tunnel tends to be larger for rock with higher RMR rating. The size of the EDZ is found to be direct proportional to the tunnel diameter, whereas the depth of the tunnel is inversely proportional due to higher confinement stress around the excavation surface.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.4
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• pp.73-86
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• 1998

The purpose of this paper is to evaluate the shear stiffness, hysteretic behavior, and ultimate behavior of HDRB(High Damping Rubber Bearing), which will be included in the seismic isolation design guideline as requirements. To do this, two 1/8 scaled HDRB are designed, fabricated, and tested to show the mechanical characteristics. The shear stiffness obtained from the proposed equation of the shear stiffness shows a good agreement with those of the experiments. For analysis of the hysteretic behavior of HDRB using the modified rate model, the parameter equations are obtained from the experiments. Using the obtained parameter equations for the modified rate model, the seismic response analyses are carried out for 1-D system. The results of analysis well follow the hysteretic behavior of HDRB obtained from the experiments. To evaluate the ultimate behavior of HDRB used in this paper, the analyses are carried out using the modified macro model, which can consider the large shear deflection. The critical shear strain(CSS) is defined to express the maximum allowable shear strain and vertical load. From the analyses, the CSS, showing the instability, decreases significantly as increased the vertical loads. The CSS is not appeared for the design vertical load in the used HDRB. In analysis using about 5 times of design vertical load, the HDRB start to show the instability transient and for about 7 times, the CSS is about 350%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.5
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• pp.339-349
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• 2020

Double-leaf blast-resistant doors consisting of steel box and slab are application-specific structures installed at the entrances of protective facilities. In these structural systems, certain spacing is provided between the door and wall. However, variation in the boundary condition and structural behavior due to this spacing are not properly considered in the explosion analysis and design. In this study, the structural response and failure behavior based on two variables such as the spacing and blast pressure were analyzed using the finite element method. The results revealed that the two variables affected the overall structural behavior such as the maximum and permanent deflections. The degree of contact due to collision between the door and wall and the impact force applied to the door varied according to the spacing. Hence, the shear-failure behavior of the concrete slab was affected by this impact force. Doors with spacing of less than 10 mm were vulnerable to shear failure, and the case of approximately 15-mm spacing was more reasonable for increasing the flexural performance. For further study, tests and numerical research on the structural behavior are needed by considering other variables such as specifications of the structural members and details of the slab shear design.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.57-65
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• 2022

This study investigated the electromechanical response of smart ultra-high performance fiber reinforced concretes (S-UHPFRCs) under flexural loading to evaluate the self-sensing capacity of S-UHPFRCs in both tension and compression region. The electrical resistivity of S-UHPFRCs under flexural continuously changed even after first cracking due to the deflection-hardening behavior of S-UHPFRCs with the appearance of multiple microcracks. As the equivalent bending stress increased, the electrical resistivity of S-UHPFRCs decreased from 976.57 to 514.05 kΩ(47.0%) as the equivalent bending stress increased in compression region, and that did from 979.61 to 682.28 kΩ(30.4%) in tension region. The stress sensitivity coefficient of S-UHPFRCs in compression and tension region was 1.709 and 1.098 %/MPa, respectively. And, the deflection sensitivity coefficient of S-UHPFRCs in compression region(30.06 %/mm) was higher than that in tension region(19.72 %/mm). The initial deflection sensing capacity of S-UHPFRCs was almost 50% of each deflection sensitivity coefficient, and it was confirmed that it has an excellent sensing capacity for the initial deflection. Although both stress- and deflection-sensing capacity of S-UHPFRCs under flexural were higher in compression region than in tension region, S-UHPFRCs are sufficient as a self-sensing material to be applied to the construction field.