• Corrosion Science and Technology
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• v.4 no.4
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• pp.130-135
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• 2005

In this study, accelerated corrosion tests were conducted on concrete specimens with and without accelerated carbonation beforehand for the purpose of elucidating the effects of carbonation, cover depth, and water-cement ratio (W/C) on the reinforcement corrosion. During testing, the corrosion current between the anode steel and cathode stainless steel was measured to continuously monitor the progress of corrosion throughout the test period, thereby investigating the mechanism of reinforcement corrosion and the relationship between corrosion and crack width, as well as other parameters.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.94-96
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Geotechnical Engineering
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• v.12 no.3
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• pp.35-48
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• 1996

Reinforced soil structures may experience large local movements between soil and reinforcement. The failure modes of a reinforced structure depend on several factors which are governed by deformation and slipping of the reinforcement. In some cases, pulling out of the reinforcement may occur instead of rupturing, The growing use of geosynthetic liner system for storage of solid and liquid wastes has led to a number of slope instability problems where the synthetic liner may undergo a large amount of stretching and slipping as a result of the loading. The conventional finite element model for the soil-reinforcement interface uses a zero thickness joint element with normal and shear stiffnesses and can only accommodate a small amount of deformation. When a large slippage occurs, the model provides an i ncorrect mechanism for deformation. This paper presents a new interface finite element model which is able to simulate a large amount of slippage between soil and reinforcement. The formulation of the model is presented and the capability of the model is demonstrated using illustrative examples.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.3
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• pp.76-81
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• 2006

The reinforcement of soil shear strength by nylon net as substitute materials simulating a fine root system was evaluated by soil strength parameters(apparent cohesion(c) and internal friction angle(tan${\phi}$), using simple shear tester which clearly depicts shear deformation and controls soil suction. And the results of shear test by using bamboo as a substitute materials simulating a main root system and using nylon net as a substitute materials simulating a fine root system were compared. The reinforcement of soil strength by nylon net are expressed by apparent cohesion more than internal friction angle. In addition the increment of apparent cohesion by nylon net reached a peak in suction 60 $cmH_2O$. Different from with bamboo, the possibility of the change on internal friction angle(tan${\phi}$) caused by the soil water condition was shown in shear strain 20% condition. These results show that the mechanism of reinforcement by substitute materials simulating root system may be different in the condition of various soil water content.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.1-8
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• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.795-802
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• 2003

To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent's goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.276-284
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• 2009

Recently pile-supported embankments have emerged as an optimum method when the rapid construction and strict deformation of structures are required on soft soils. Especially geosynthetic-reinforced and pile-supported (GRPS) embankments are used worldwide as they can provide economic and effective solutions. However the load transfer mechanism in GRPS embankments is very complex, and not yet fully understood. Particularly the purpose and effect of geosynthetic inclusion are ambiguous and considered as an auxiliary measure assisting the arching effect of piles. Numerical parametric study using 3D finite element method has been conducted to investigate the effect of geosynthetic reinforcement on the load transfer mechanism of GRPS embankments. Numerical results suggested that as more stiffer geosynthetic is included, arching effect decreases considerably and the load concentration to the piles mostly caused by tension effect of geosynthetic. This finding is contradictory to the common understanding that geosynthetic inclusion only enhance the efficiency of load transfer. Consequently the design parameters determined from the numerical analyses are compared with those of three existing design methods. The problems of the existing methods are discussed.

• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.187-202
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• 2024

For the problems of high compressibility and low strength of peat soil formed by lake-phase deposition in Dianchi Lake, microbial-induced calcium carbonate deposition (MICP), phyto-urease-induced calcium carbonate deposition (EICP) and phyto-urease-induced calcium carbonate deposition combined with lignin (EICP combined with lignin) were used to reinforce the peat soil, the changes in mechanical properties of the soil before and after the reinforcement of the peat soil were experimentally investigated, and the effect and mechanism of peat soil reinforcing by the three reinforcing techniques were tested and analyzed using X-ray diffraction (XRD) and scanning electron microscope (SEM). The results show that: compared to the unreinforced remolded peat soil specimens, the unconfined compressive strength (UCS), cohesion and internal friction angle of the specimens reinforced by MICP, EICP and EICP combined with lignin techniques have been greatly improved, and the permeability resistance has been improved by two, two and three orders of magnitude, respectively; the different methods of reinforcing generate different calcium carbonate crystalline phases, with the EICP combined with lignin technique generating the most stable calcite, and the MICP and EICP techniques generating a mixed phase of calcite and spherulitic chalcocite. Analyses showed that for peat soil reinforcement, the acidic environment of peat soil inhibited the growth and reproduction of bacteria, EICP technology was superior to MICP technology, and the addition of lignin solved the defect of the EICP technology that did not have a "nucleation site", so EICP combined with lignin reinforcement was preferred for the improvement of peat soil.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.533-539
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• 2003

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.10a
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• pp.213-217
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• 1996

The electrochemical methods of early detection and analysis of corrosion related deterioration in concrete reinforcement structures are very useful techniques. The generally using procedure for corrosion monitoring of reinforced structures employs a method of half-cell potential measurement. Whilst the technique has provided a useful means of delineating areas of high or low corrosion risk, there are difficulties in its use and interpretation, particularly when assessing corrosion rates of reinforcement. The aim of this study is to describe the AC-impedance method being employed to monitor and assess corrosion rates, to estimate corrosion mechanism of reinfrocement in laboratory conditions. The AC-impedance monitoring technique applies a small amplitude(20mV) AC signal to embedded steel in concrete and reference electrode (Cu/$CuSo_4$). We obtained over a wide frequency range(10MHz~1mHz) to produce a complex plane plot or Nyquist plot.