• 콘크리트학회논문집
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• 제28권6호
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• pp.713-722
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• 2016

이 연구는 지진하중을 받는 현장타설 및 조립식 물량저감 중공 철근콘크리트 교각의 내진성능을 파악하는데 그 목적이 있다. 개발된 물량저감 삼각망 철근상세는 경제적이고 합리적이며 공사기간을 단축할 수 있다. 정확하고 올바른 성능평가를 위하여 신뢰성 있는 비선형 유한요소해석 프로그램을 사용하였다. 이용된 해석기법은 조사된 중공 교각 실험체에 대하여 입력지진파에 따라 내진성능을 비교적 정확하게 예측하였다. 그 결과 개발된 삼각망 물량저감 철근상세는 기존 철근상세와 동등 이상의 소요성능을 보임을 확인하였다.

• 한국공간구조학회논문집
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• 제18권3호
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• pp.117-124
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• 2018

In the post-tensioned concrete member, additional reinforcement is required to prevent failure in the anchorage zone. In this study, the details of reinforcement suitable for the anchorage zone of the post-tensioned concrete member using circular anchorage was proposed based on the experimental results. The tests were conducted with the compressive strength of concrete and reinforcement types as variables. The experimental results indicated that the additional reinforcement for the anchorage zone is required when the compressive strength of concrete is less than 17.5 MPa. U-shaped reinforcement shows most effective performance in terms of maximum strength and cracks patterns.

• Computers and Concrete
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• 제1권1호
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• pp.15-30
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• 2004

In this study, a design process for reinforced concrete structures using the nonlinear FEM analysis is developed. Instead of using the nonlinear analysis to evaluate the required performance after design process, the nonlinear analysis is applied before designing the reinforcement arrangement inside the RC structures. An automatic reinforcement generator for computer aided reinforcement agreement is developed for this purpose. Based on a nonlinear FEM program for analyzing the reinforced concrete structure, a smart fictitious material model of steel, is proposed which can self-adjust the reinforcement to the required amount at the cracking location according to the load increment. Using this tool, the reinforcement ratio required at design load level can be decided automatically. In this paper, an example of RC beam with opening is used to verify the proposed process. Finally, a trial design process for a real size underground RC LNG tank is introduced.

• Geomechanics and Engineering
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• 제24권6호
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• pp.589-597
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• 2021

The present study evaluates geocell reinforced slope behavior. A three dimensional analysis is carried out to simulate soil and geocell elastoplastic behavior using the finite difference software FLAC3D. In order to investigate the geocell reinforcement effect, the geocell aperture size, thickness, geocell placement condition and soil compaction had been considered as variable parameters. Moreover, a comparison is evaluated between geocell reinforcing system and conventional planar reinforcement. The obtained results showed that the pocket size, thickness and soil compaction have considerable influence on the geocell reinforcement slope performance. Moreover, it was found that the critical sliding surface was bounded by the first geocell reinforcement and the slope stability increases, by increasing the vertical space between geocell layers. In addition, the comparison between geocell and geogrid reinforcement indicates the efficiency of using cellular honeycomb geosynthetic reinforcement.

• 대한토목학회논문집
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• 제42권5호
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• pp.607-616
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• 2022

전 세계적으로 이상 기후 및 자연재난 등으로 인하여 철근콘크리트 구조물의 부식 및 열화 현상이 빈번히 발생됨에 따라 구조물의 노후화가 가속화되고 있다. 건설 분야에서는 이러한 내하력 저하에 대응하기 위하여 최근 저 중량 고강도 재료 장점을 가진 유리섬유 복합재료(GFRP)를 활용하여 많은 노후 구조물에 대하여 보수·보강을 수행하고 있다. 본 연구에서는 유리섬유에 비하여 보다 경제적이고 친환경적인 바잘트 섬유 복합재료(BFRP)를 활용하여 콘크리트 압축부재의 내진보강을 위한 횡구속 효과를 더욱 효과적으로 제공할 수 있는 보강재를 개발하고 그 성능을 평가하였다. 실험 시 고려된 주요 변수로는 바잘트섬유 복합재료(BFRP) 시공 시 적용되는 함침 수지의 양생 온도와 대상 콘크리트 압축부재의 재료 특성을 고려하였다. 콘크리트 압축부재의 재료 특성에 따른 횡구속 보강효과를 조사하기 위하여 본 연구에서는 일반 콘크리트와 섬유 보강을 통하여 내구성능이 개선된 콘크리트 시험체를 각각 제작하여 성능을 평가하였다. 그 결과, 일반 콘크리트의 경우 3.15배, 섬유 보강 콘크리트의 경우 약 3.72배의 보강 효과가 나타났으며 압축부재 내구특성 개선에 따른 보강 효과의 차이는 크지 않음을 알 수 있었다. 마지막으로 GFRP 압축부재 보강재에 대한 선행연구 결과를 통하여 바잘트 보강 복합재료의 성능을 비교한 결과 BFRP 보강재의 횡구속 보강효과가 상대적으로 약 1.18배 GFRP 보강재에 비하여 성능이 우수한 것으로 나타났다.

• International Journal of Control, Automation, and Systems
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• 제5권6호
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• 한국생산제조학회지
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• 제19권5호
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• pp.691-697
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• 2010

Reliability-based topology optimization (RBTO) is to get an optimal topology satisfying uncertainties of design variables. In this study, reliability-based topology optimization method is applied to the inner reinforcement of vehicle's hood based on BESO. A multi-objective topology optimization technique was implemented to obtain optimal topology of the inner reinforcement of the hood. considering the static stiffness of bending and torsion as well as natural frequency. Performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints. To evaluate the obtained optimal topology by RBTO, it is compared with that of DTO of the inner reinforcement of the hood. It is found that the more suitable topology is obtained through RBTO than DTO even though the final volume of RBTO is a little bit larger than that of DTO. From the result, multiobjective optimization technique based on the BESO can be applied very effectively in topology optimization for vehicle's hood reinforcement considering the static stiffness of bending and torsion as well as natural frequency.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.1-8
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• 1998

본 논문에서는 유전자 프로그래밍의 성능을 향상시키기 위하여 강화학습법에 기반한 강화 유전자 프로그래밍을 제안한다. 트리구조와 프로그램을 염색체로 가지는 유전자 프로그래밍(GP)은 다른 진화 알고리즘에 비해 염색체의 크기에 제한이 없기 때문에 표현력에 융통성이 많다는 장점이 있다. 그러나 이러한 특징은 반대고 교차 및 돌연변이 연산에 있어서 수렴성을 떨어뜨리는 단점을 나타낸다. 따라서 유전자 프로그래밍은 다른 진화알고리즘에 비해 개체군의 크기 및 진화 세대수를 크게 잡는 것이 일반적이다. 본 논문에서는 유전자 프로그래밍의 이러한 성질을 개선하기 위해서 프로그램에 강화신호를 주어 이것의 보답/벌칙의 정도에 기반한 교차 및 돌연번이 연산을 실행하는 방법을 제안한다. 제안된 방법은 인공개미(Artificial Ant)문제에 적용하여 그 유효성을 입증한다.

• 한국공간구조학회논문집
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• 제21권2호
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.