• 콘크리트학회논문집
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• 제16권1호
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• pp.111-124
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• 2004

축방향철근의 연결상세에 따라 7개 그룹 총 21개의 원형나선철근 기둥 시험체를 제작하여 준정적 실험을 수행하였다. 축방향철근 연결상세(단일철근, 겹침이음 및 기계적연결), 심부구속철근비, 축력비 등을 주요 실험변수로 채택하였으며 실험결과 축방향철근 연결상세에 따라 다른 파괴거동을 나타내었고, 내진성능에서도 차이를 나타내었다. 축방향철근이 겹침이음된 시험체의 실험결과, 모든 축방향철근이 겹침이음된 시험체는 내진성능이 상당히 저하되는 것으로 나타났으나, 축방향철근의 $50\%$가 겹침이음된 시험체의 경우 제한적이지만 한정적인 연성능력을 나타내었다. 또한, 축방향철근을 커플러를 사용하여 기계적으로 연결한 시험체는 축방향철근이 단일철근으로 구성된 시험체와 유사한 파괴거동 및 강도저감거동을 나타내었다.

• 한국지진공학회논문집
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• 제28권4호
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• pp.193-203
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• 2024

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R²), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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• pp.561-568
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• 1997

Conventional confinement reinforcement for rectangular columns consist of closely spaced perimeter hoops, overlapping hoops, and crossties. One of the potential alternatives to conventional reinforcement is a welded reinforcement grid, prefabricated to required size and volumetric ratio of transverse reinforcement. An experimental investigation was carried out to investigate the strength and deformability of reinforced concrete columns confined with welded reinforcement grids. The comparisons of the envelopes of experimental force-displacement hysteric relationships for columns confined by welded grids with analytically generated force-displacement curves for columns confined with conventional ties. In general, inelastic deformability beyond the peak, as indicate by the slope of the cuvers, was similar to those indicated by columns with conventional ties. The superior performance of columns with welded grids may be attributed to the improved confinement characteristics of grids associated with increased rigidity of welded ties.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.127-130
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• 2017

In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.

• 한국공간구조학회논문집
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• 제19권4호
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• pp.61-68
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• 2019

This paper discusses the influence of transverse reinforcement spacing and support width of concrete wide beam on shear performance. In order to evaluate the shear performance, a total of thirteen specimens were constructed and tested. The transverse reinforcement spacing, the number of legs and support width were considered as variables. From the test results, the shear strength equation of concrete wide beam is proposed for prediction of shear strength of concrete wide beam to consider the transverse reinforcement spacing and support width. It is shown that the proposed equation is able to predict shear strength reasonably well for concrete wide beam.

• Structural Engineering and Mechanics
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• 제67권1호
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• pp.93-104
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• 2018

The seismic behavior of reinforced concrete (RC) short columns with limited transverse reinforcement is investigated in this paper through an experimental program. The experimental program consists of four small-scale RC columns with an aspect ratio of 1.7, which are tested to the axial failure stage. The cracking patterns, hysteretic responses, strains in reinforcing bars, displacement decomposition and cumulative energy dissipation of the tested specimens are reported in detail in the paper. The effects of column axial load are investigated to determine how this variable might influence the performance of the short columns with limited transverse reinforcement. Brittle shear failure was observed in all tested specimens. Beneficial and detrimental effects on the shear strength and drift ratio at axial failure of the test specimens due to the column axial load are found in the experimental program, respectively.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.23-31
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• 2018

Recently, there have been many studies on machine learning. Among them, studies on reinforcement learning are actively worked. In this study, we propose a controller to control bicycle using DDPG (Deep Deterministic Policy Gradient) algorithm which is the latest deep reinforcement learning method. In this paper, we redefine the compensation function of bicycle dynamics and neural network to learn agents. When using the proposed method for data learning and control, it is possible to perform the function of not allowing the bicycle to fall over and reach the further given destination unlike the existing method. For the performance evaluation, we have experimented that the proposed algorithm works in various environments such as fixed speed, random, target point, and not determined. Finally, as a result, it is confirmed that the proposed algorithm shows better performance than the conventional neural network algorithms NAF and PPO.

• 전기전자학회논문지
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• 제25권4호
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• pp.728-734
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• 2021

최근 심층강화학습을 활용한 종단간 자율주행에 대한 관심이 크게 증가하고 있다. 본 논문에서는 차량의 종방향 주행 성능을 개선하는 잠재 SAC 기반 심층강화학습의 보상함수를 제시한다. 기존 강화학습 보상함수는 주행 안전성과 효율성이 크게 저하되는 반면 제시하는 보상함수는 전방 차량과의 충돌위험을 회피하면서 적절한 차간거리를 유지할 수 있음을 보인다.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.192-198
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• 2024

This paper investigates the application of multi-agent deep reinforcement learning in the fighting game Samurai Shodown using Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) algorithms. Initially, agents are trained separately for 200,000 timesteps using Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) with LSTM networks. PPO demonstrates superior performance early on with stable policy updates, while A2C shows better adaptation and higher rewards over extended training periods, culminating in A2C outperforming PPO after 1,000,000 timesteps. These findings highlight PPO's effectiveness for short-term training and A2C's advantages in long-term learning scenarios, emphasizing the importance of algorithm selection based on training duration and task complexity. The code can be found in this link https://github.com/Lexer04/Samurai-Shodown-with-Reinforcement-Learning-PPO.

• 한국구조물진단유지관리공학회 논문집
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• 제21권2호
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• pp.60-68
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• 2017

본 연구에서는 지내력이 기초판에 미치는 영향을 충분히 고려할 수 있도록 현장여건과 동일한 옥외의 지반에서 실험할 수 있는 시스템을 구축하였으며, 대상 실험체는 경제성 및 시공성 향상을 위하여 강판을 "ㄷ"자형으로 절곡하여 단면 2차모멘트를 극대화 하고 현장조립이 가능하도록 제안 하였다. 대상 실험체는 무보강 실험체 1개, 강판 두께를 동일하게 하여 보강 길이를 달리한 실험체 3개, 강판 두께를 달리하고 위험단면 부근에 스티프너 보강한 실험체 2개 총 6개의 실험체를 대상으로 비교 검토 한다. 실험 결과 스티프너 보강에 의한 효과는 없는 것으로 나타났으며, 전단보강재의 보강길이는 확장된 위험단면에서 전단력을 지내력으로 나타낸 값과 위험단면에서 보강재가 받을 수 있는 전단내력을 지내력으로 환산여하여 두 선의 교차점을 유효보강 길이로 산정하는 강판두께별 유효보강길이 산정방법을 제안하였다.