• Journal of Korean Association for Spatial Structures
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• v.17 no.1
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• pp.59-67
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• 2017

In this study, load transfer tests based on KCI-PS101 were conducted to verify the performance of spiral anchorage zone reinforcement for banded post-tensioning (PT) monostrands. With results, the compressive strength of spiral reinforcement was increased by about 20% than that of specimens with two horizontal steel bars and 8% than that of U-shaped bars. Advanced spiral reinforcement for corner increases compressive strength and can resist the spalling forces or fall-out effect at the corner by shear. The ratio of maximum load to amount of steel of the spiral reinforcement is about twice than that of U-shaped reinforcement. With increase of compressive strength capacity and improvement of constructability, the spiral reinforcement is considered to have advantages of promoting the performance of PT anchorage zone compared to conventional methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.633-640
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• 1999

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like Q-learning and TD(Temporal Difference)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present COMREL(COMpressed REinforcement Learning) algorithm for finding the shortest path fast in a maze environment, select the candidate states that can guide the shortest path in compressed maze environment, and learn only the candidate states to find the shortest path. After comparing COMREL algorithm with the already existing Q-learning and Priortized Sweeping algorithm, we could see that the learning time shortened very much.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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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.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.583-598
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• 2022

The Compaction effect is important for evaluating the subgrade construction. However, there is little research exploring the compaction quality of deep soil using hydraulic compaction. According to reinforcement effect analysis, dimensional analysis is adopted in this work to analyze subgrade compactness within the effective reinforcement depth, and a prediction model is obtained. A hydraulic compactor is then employed to carry out an in-situ reinforcement test on gravel soil subgrade, and the subgrade parameters before and after reinforcement are analyzed. Results show that a reinforcement difference exists inside the subgrade, and the effective reinforcement depth is defined as increasing compactness to 90% in the depth direction. Layered compactness within the effective reinforcement depth is expressed by parameters including the drop distance of the rammer, peak acceleration, tamping times, subgrade settlement, and properties of rammer and filler. Finally, a field test is conducted to verify the results.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.3
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• pp.329-345
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• 2023

This paper described a method for analyzing the structural performance of a metal container used for disposing radioactive waste generated during the decommissioning of a nuclear power plant, and numerical analysis results of a method for reinforcing the container. The containers to be analyzed were those that can be used in near-surface and landfill disposal facilities scheduled to be operated at the Gyeongju radioactive waste disposal facility. Structural reinforcement of the container was performed by lattice reinforcement, column reinforcement, and bottom plate reinforcement. Accordingly, a total of 14 reinforcement cases were modeled. The external force causing damage to the container was set equivalent to the impact of a 9-m fall, accounting for the height of the vault at the near-surface disposal facility. The reinforcement methods with a high contribution to the structural performance of the container were concluded to be lattice and column reinforcements.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.06a
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• pp.83-91
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• 2003

Always slope according to construct road exist danger because of environment unstability factor in slope, Since this research an inclination of slope is gentle slope (1：1.5∼1：9.0) but falling happened by conduct of continuous ground movement. And this study considered more economical and efficient reinforcement method for slope stability. The various reinforcement methods are applied to execute examination of slope stability. Applied reinforcement methods satisfied safety factor And this research region is performing continuous measurement about ground movements and displacements.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.445-448
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• 1999

The minimum shear and torsional reinforcement provisions in ACI 318-95 are still empirical. This paper describes the derivation of a rational approach for minimum shear and torsional reinforcement in beams so as to preclude brittle failure in shear and torsion. This is ensured by specifying that the beam's ultimate capacity of shear and torsion should be greater than its cracking shear and torsion. The formula presented herein for computing minimum shear and torsional reinforcement shows the need for modification of current provision for the minimum shear and torsion reinforcement.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.04b
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• pp.421-426
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• 1998

Current code provisions for the development of positive moment reinforcement is reviewed and criticized in this paper. Both the flexural bond and development length concepts are neccesary to consider anchorage requirement of reinforcement at beam ends. The curent design codes show unconservatism for the detailing of reinforcement at the beam ends. This study proposes a new design formula for the development of positive moment reinforcement.