• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.782-791
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• 2009

This study was carried out the laboratory tests and field plate load test in order to evaluate the reinforcement effect of geocell for road construction. The geocell-reinforced subgrade shows the increment of cohesion and friction angle with comprison of non-reinforced subgrade. In addition, the field plate load test was performed on the geocell-reinforced subgrade to estimate the bearing capacity of soil. The direct shear test was conducted with utilizing a large-scale shear box to evaluate the internal soil friction angle with geocell reinforcement. The number of cells in the geocell system is varied to investigate the effect of soil reinforcement. The theoretical bearing capacity of subgrade soil with and without geocell reinforcement was estimated by using the soil internal friction angle. The field plate load tests were also conducted to estimate the bearing capacity with geocell reinforcement. It is found out that the bearing capacity of geocell-reinforced subgrade gives 2 times higher value than that of unreinforced subgrade soil. In the future, the reinforcement effect of the geocell rigidity and load-balancing effect of the geocells should be evaluated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.123-130
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• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.

• Structural Engineering and Mechanics
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• v.69 no.4
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• pp.399-405
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• 2019

The present study assessed the reliability-based reinforcement ratio of FRP reinforced concrete structure applying recycled coarse aggregate (RCA) concrete. The statistical characteristics of FRP bars and RCA concrete were investigated from the previous literatures and the mean value and standard deviation were employed for the reliability analysis. The statistics can be regarded as the material uncertainty for configuring the probability distribution model. The target bridge structure is the railway bridge with double T-beam section. The replacement ratios of RCA were 0%, 30%, 50%, and 100%. From the probability distribution analysis, the reliability-based reinforcement ratios of FRP bars were assessed with four cases according to the replacement ratio of RCA. The reinforcement ratio of FRP bars at RCA 100% showed about 17.3% higher than the RCA 0%, where the compressive strength at RCA 100% decreased up to 27.5% than RCA 0%. It was found that the decreased effect of the compressive strength of RCA concrete could be compensated with increase of the reinforcement ratio of FRP bars. This relationship obtained by the reliability analysis can be utilized as a useful information in structural design for FRP bar reinforced concrete structures applying RCA concrete.

• Computers and Concrete
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• v.27 no.4
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• pp.385-393
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• 2021

Reinforcement corrosion is the main cause of the durability failure of reinforced concrete (RC) structure. In this paper, a three-dimensional (3D) numerical model of macro-cell corrosion is established to reveal the corrosion mechanisms of steel reinforcement in RC structure. Modified Direct Iteration Method (MDIM) is employed to solve the system of partial differential equations for reinforcement corrosion. Through the sensitivity analysis of electrochemical parameters, it is found that the average corrosion current density is more sensitive to the change of cathodic Tafel slope and anodic equilibrium potential, compared with the other electrochemical parameters. Furthermore, both the anode-to-cathode (A/C) ratio and the anodic length have significant influences on the average corrosion current density, especially when A/C ratio is less than 0.5 and anodic length is less than 35 mm. More importantly, it is demonstrated that the corrosion rate of semi-circumferential corrosion is much larger than that of circumferential corrosion for the same A/C ratio value. The simulation results can give a unique insight into understanding the detailed electrochemical corrosion processes of steel reinforcement in RC structure for application in service life prediction of RC structures in actual civil engineer.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.33-42
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• 2002

Recently, various experiments to apply reinforcement learning method to the self-learning intelligent control of continuous dynamic system have been reported in the machine learning related research community. The reports have produced mixed results of some successes and some failures, and show that the success of reinforcement learning method in application to the intelligent control of continuous control systems depends on the ability to combine proper function approximation method with temporal difference methods such as Q-learning and value iteration. One of the difficulties in using function approximation method in connection with temporal difference method is the absence of guarantee for the convergence of the algorithm. This paper provides a proof of convergence of a particular function approximation method based on \"barycentric interpolator\" which is known to be computationally more efficient than multilinear interpolation .

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.3
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• pp.186-195
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• 2010

This study has attempted to suggest a proper reinforcement method by strengthening unbonded post -tensioning through height of an anchorage hole, form of a saddle, and loading time point as parameters and evaluating the reinforcement method through a bending experiment. The result of this experiment indicated effects of reinforcement since the maximum strength ratio(the ratio of an experimental value to theoretical value) of SC composite beams before prestressed was 0.97 and after prestress were 1.00~1.21. As a result of analysis on displacement and strain, irrespective of height of an anchorage hole and loading time point, the D120-series specimen where an anchorage hole was installed on the neutral axis after reinforcement showed that its deflection continuously increased without sudden load reduction after maximum load and it stably behaved with relatively low strain of each part. In terms of reinforcement effects, the maximum strength of SCR-UD120 specimen prestressed after pre-loading was increased 1.72 times comparing to SC composite beams so SCR-UD120 specimen prestressed after pre-loading was shown to be the best.

• Journal of Technologic Dentistry
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• v.36 no.1
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• pp.1-7
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• 2014

Purpose: The aim of this study was analyzed by comparing the effect of glass fiber reinforcement addition on the strength of resin denture base. It was intended to provide a reference data useful for clinical application. Methods: The test specimens (length $64.0{\pm}0.1mm$, width $10.0{\pm}0.1mm$, thickness $1.0{\pm}0.1mm$, $1.5{\pm}0.1mm$, and $2.0{\pm}0.1mm$ respectively) were made. In the experimental groups resin denture base reinforced with glass fiber were fabricated. In the control groups resin denture base were fabricated by conventional method. After specimen fabrication was completed, transverse test was performed using a universal testing machine. Results: The transverse strength value in CON group was $83.08{\pm}9.07MPa$ for 1.0 mm, which ranked the highest in value. On the other hand, the value was $56.07{\pm}5.15MPa$ for 2.0mm, which ranked the lowest in value. And CON+SES group was $119.80{\pm}30.70MPa$ for 1.0mm, which ranked the highest in value. On the other hand, the value was $84.00{\pm}7.97MPa$ for 2.0mm, which ranked the lowest in value. Also, the flexural modulus value in CON group was $2,983.10{\pm}506.92MPa$ for 1.0mm, which ranked the highest in value. On the other hand, the value was $1,257.64{\pm}230.48MPa$ for 2.0mm, which ranked the lowest in value. And CON+SES group was $4,679.41{\pm}1578.29MPa$ for 1.0mm, which ranked the highest in value. On the other hand, the value was $2,512.36{\pm}527.09MPa$ for 2.0mm, which ranked the lowest in value. Conclusion: The reinforced glass fiber increased the strength of resin denture base, effected to reduce the thickness of resin denture base.

• Korean Journal of Construction Engineering and Management
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• v.3 no.4 s.12
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• pp.93-103
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• 2002

The reinforcement concrete work is the work affected by structural safety, durability, and schedule with form work. The domestic reinforcement concrete works have mainly worked the process of re-barfabrication/assembly on site. Finally it have low productivity. Then this paper analyzed waste factors and the process of re-bar fabrication/assembly on site for the productivity improvement and value-added productivity improvement. Waste factor analysis aims at maximizing value-added by the value analysis of re-bar fabrication and assembly on site. Finally, Value-AddingActivity(VAA)ismuchlessthan non-value adding activity. Especially, Non-Value-Adding Activity(NVAA) generates waste such as the activity steps, labors, equipments, materials, time, and soon. And it was non-flow production, over production, and analyzed into having to shift value. This paper aimsat maximizing value-addingactivity and minimizingnon value-addingactivity through waste factor analysis in process for the improvement of value added productivity.

• Progress in Superconductivity and Cryogenics
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• v.5 no.3
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• pp.15-19
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• 2003

For the purpose of standardization of the critical current measurement, it is meaningful to describe how $I_{c}$ will behave as the stress/strain level changes. In this study, strain dependencies of the critical current $I_{c}$ in Ag-alloy sheathed multifilamentary Bi(2212) and Bi(2223) superconducting tapes were evaluated at 77K, 0T. The external reinforcement was accomplished by soldering AgMgNi alloy tapes onto single or both sides of the sample. With the external reinforcement to the Bi(2212) tape, the strength of the tapes increased but the critical current at the strain free state, $I_{c0}$ decreased in some cases. The strain for onset of the $I_{c}$ degradation, $\varepsilon$$_{\irr}$, increased with an increase of the reinforcing volume and then saturated to a certain value. The effect of external reinforcement on the degradation of $I_{c}$ due to the bending strain in the Bi(2223) tape was also examined. Contrary to the expectation, it showed a significant $I_{c}$ degradation even at a small strain of 0.4 %. The observations of damage morphologies gave a good explanation to the $I_{c}$ behavior.c/ behavior.r.

• Tunnel and Underground Space
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• v.14 no.4
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• pp.275-286
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• 2004

It is studied to select the reinforcement method of an abandoned tunnel which are intersected under the new roadway line. In the various decision makings, the reasonability for the reinforcement method of an abandoned tunnel was estimated using the pair-wise comparison and the fuzzy approximate reasoning to simplify the process of survey research. And there is reflected all the qualitative and quantitative characterizations by investigation items. In order to select the reinforcement method of an abandoned tunnel, 4 characteristic factors of construction, economical efficiency, safety and maintenance were used. Using the simple survey research and pair-wise comparison matrix, the weight of 4 factors was decided. The fuzzy approximate reasoning was used to calculate the quantitative value of each factor And then reflecting each weight to these results, the final reinforcement method of an abandoned tunnel could be determined.