• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.234-237
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• 2003

Previous researches about headed reinforcement have not been concerned about bond failure which is quite important is some cases. In this paper, failure mechanism including bond failure was presented in order to define the contribution of bond stress at the time failure occurs. Examined with design codes and test results, it is proved to be rational to consider the contribution of bond stress in determining the ultimate pull-out capacity of headed reinforcement. Direct adaptation of design code for anchor bolt without modification for the contribution of bond stress will lead to underestimate the capacity of headed reinforcement.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1235-1239
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• 2006

Fractal manufacturing system (FrMS) distinguishes itself from other manufacturing systems by the fact that there is a fractal repeated at every scale. A fractal is a volatile organization which consists of goal-oriented agents referred to as AIR-units (autonomous and intelligent resource units). AIR-units unrestrictedly reconfigure fractals in accordance with their own goals. Their goals can be dynamically changed along with the environmental status. Since goals of AIR-units are represented as fuzzy models, an AIR-unit itself is a fuzzy logic controller. This paper presents a goal regulation mechanism in the FrMS. In particular, a reinforcement learning method is adopted as a regulating mechanism of the fuzzy goal model, which uses only weak reinforcement signal. Goal regulation is achieved by building a feedforward neural network to estimate compatibility level of current goals, which can then adaptively improve compatibility by using the gradient descent method. Goal-oriented features of AIR-units are also presented.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1228-1230
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• 1996

A neural network(NN) mechanism is proposed to modify the gait of a biped robot that walks on sloping surface using sensory inputs. The robot starts walking on a surface with no priori knowledge of the inclination of the surface. By accumulating experience during walking, the robot improves its walking gait and finally forms a gait that is adapted to the surface inclination. A neural controller is proposed to control the gait which has 72 reciprocally inhibited and excited neurons. PI control is used for position control, and the neurons are trained by a reinforcement learning mechanism. Experiments of static gait learning and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism.

• Corrosion Science and Technology
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• v.16 no.2
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• pp.69-75
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• 2017

Zinc coating on carbon steels give the higher corrosion resistance in chloride containing environments and in carbonated concrete. However, hydrogen evolution accompanies the corrosion of zinc in the initial activity in fresh concrete, which can lead to the formation of a porous structure at the reinforcement -concrete interface, which can potentially reduce the bond-strength of the reinforcement with concrete. The present study examines the mechanism of the corrosion of hot-dip galvanized steel in detail, as in the model pore solutions and real concrete. Calcium ion plays an important role in the corrosion mechanism, as it prevents the formation of passive layers on zinc at an elevated alkalinity. The corrosion rate of galvanized steel decreases in accordance with the exposure time; however, the reason for this is not the zinc transition into passivity, but the consumption of the less corrosion-resistant phases of hot-dip galvanizing in the concrete environment. The results on the electrochemical tests have been confirmed by the bond-strength test for the reinforcement of concrete and by evaluating the porosity of the cement adjacent to the reinforcement.

• Magazine of the Korea Concrete Institute
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• v.6 no.5
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• pp.193-202
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• 1994

In this study, an equation for modelling the shear strength of reinforced concrete member with web reinforcement is proposed. Although the general formulas for shear strength of reinforced concrete member with small a /d are obtained based on the experimental results, the proposed equation herein is derived from lower bound theorem of limit analysis. The proposed model takes into account arch mechanism and truss mechanism. And ir provides the values of divided shear strength ratio of each mechanism as well as visual understanding of the mechanism on how the given load is transfered to the support. Also, the model takes into account the effect of a /d. longitudinal reinforcement ratio, and web reiriforcement ratio quantitively. Based on the comparisons of the result of this model with previous, test results, it shows good agreements.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.420-426
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• 1998

Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.

• Geomechanics and Engineering
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• v.9 no.5
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• pp.601-618
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• 2015

Soil-reinforcement interaction mechanism is an important issue in the design of geosynthetic reinforced soil structures. This mechanism depends on the soil properties, reinforcement characteristics and interaction between these two elements (soil and reinforcement). In this work the shear strength of sand/geotextile interfaces were characterized through direct and simple shear tests. The direct shear tests were performed on a conventional direct shear device and on a large scale direct shear apparatus. Unreinforced sand and one layer reinforced sand specimens were characterized trough simple shear tests. The interfaces shear strength achieved with the large scale direct shear device were slightly larger than those obtained with the conventional direct shear apparatus. Notwithstanding the differences between the shear strength characterization through simple shear and direct shear tests, it was concluded that the shear strength of one layer reinforced sand is similar to the sand/geotextile interface direct shear strength.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.904-914
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• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Geotechnical Engineering
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• v.12 no.6
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• pp.21-36
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• 1996

The stress transfer mechanism between soil and grid reinforcements involves two basic mechanism : frictional soil resistance and passive soil resistance. However the mechanism of the passive soil resistance is very complex to understand. To study the failure mechanism of ribbed reinforcement, the direct shear tests which are dominated by passive soil resistance are analyzed by using the finite element method. The finite element method is used to examine the effects of ribs on this passive soil resistance development and the met hanism of failure. The calculated behavior of the ribbed reinforcement is compared with the measured behavi or. Comparisons between the measured and the simulated strain pat terns, failure modes and load displacement relationship are presented. The behavior of the ribbed reinforcements in a cohesive soil is predicted on the basis of a good agreement between the measured and the Predicted behavior of the Ottawa sand.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.233-238
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• 2002

This study presents failure mechanisms for the pull-out strength of headed reinforcement for upper bound solution based on the limit theorem. The failure mechanisms to be presented follow the failure surface pattern of punching shear failure found in the joints of slab with a column. Several failure surfaces of the mechanisms have different characteristics for dissipation works and these mechanisms are able to interpret the role of bar details surrounding headed reinforcement.