• Proceedings of the Korean Geotechical Society Conference
• /
• 2004.03b
• /
• pp.372-379
• /
• 2004

The model tests were conducted to assess the behavior characteristics of geogrid reinforced earth walls according to various surcharge loads and reinforcement spacing. The models were built in the box having dimension, 100cm tall, 140cm long, and 100cm wide. The reinforcement used was geogrid(tensile strength 2.26t/m). Decomposed granite soil(ML) was used as a backfill material. The LVDTs were installed on the model retaining walls to obtain the displacements of the facing. In the results, the maximum displacement of facing and tensile strain of geogrid was measured at 0.7H(H is wall height) from the bottom of reinforced wall.

• International Journal of Advanced Culture Technology
• /
• v.8 no.1
• /
• pp.157-164
• /
• 2020

The reinforcement of the token, which is based on the token economy currently applied in blockchain-based cryptography, plays a critical role in forming the cryptographic-related ecosystem. Therefore, in this paper, it was investigated the reinforcement principle of token supporting the Token economy for blockchain model. In order to create a healthy ecosystem based on the reinforcement system principle, it is necessary to find ways to secure scalability by seeking consensus between the participants and the market economy structure so that it can generate an influx of more participants than seeking to maximize profits of certain people. Desirable behavior is defined as an action required by ecosystem participants that have the property of making the token ecosystem sustainable, and to do so, each individual receives appropriate incentives (rewards) when taking this action, ultimately encouraging voluntary participation and action by all participants in the ecosystem to optimize the interests of both individuals and participants. The expected benefit of this study may contribute to the establishment of various business models based on the principle of the reinforcement system.

• Computers and Concrete
• /
• v.29 no.3
• /
• pp.145-159
• /
• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1995.10a
• /
• pp.198-203
• /
• 1995

The objective of this study is to provide the information on the techniques of manufacturing and experiment in small scale modeling of precast concrete(P.C.)large panel structures. The adopted scale was 1/5th 4types of experiments were performed : material tests for model concrete and model reinforcement, compressive test of horizontal joint, shear test of vertical joint and cyclic static test of 2-story subassemblage structure. Based on the experimental results, the following conclusions are drawn: (1)Model concrete may have in general larger compressive strength than expected. (2) Model reinforcement can show less ductility if the annealing processes were performed without using vaccuum tube. (3) Failure modes of horizontal and vertical joints were almost same for both prototype and model. But the strength of model appears to be higher than required by similitude law. (4)Hysteretic behavior of 1/5 scale subassemblage model can be made quite similar to prototype's if the ductility of model reinforcement and compressive strength of model concrete could be representative of those of prototype.

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

• Computers and Concrete
• /
• v.27 no.4
• /
• pp.385-393
• /
• 2021

Reinforcement corrosion is the main cause of the durability failure of reinforced concrete (RC) structure. In this paper, a three-dimensional (3D) numerical model of macro-cell corrosion is established to reveal the corrosion mechanisms of steel reinforcement in RC structure. Modified Direct Iteration Method (MDIM) is employed to solve the system of partial differential equations for reinforcement corrosion. Through the sensitivity analysis of electrochemical parameters, it is found that the average corrosion current density is more sensitive to the change of cathodic Tafel slope and anodic equilibrium potential, compared with the other electrochemical parameters. Furthermore, both the anode-to-cathode (A/C) ratio and the anodic length have significant influences on the average corrosion current density, especially when A/C ratio is less than 0.5 and anodic length is less than 35 mm. More importantly, it is demonstrated that the corrosion rate of semi-circumferential corrosion is much larger than that of circumferential corrosion for the same A/C ratio value. The simulation results can give a unique insight into understanding the detailed electrochemical corrosion processes of steel reinforcement in RC structure for application in service life prediction of RC structures in actual civil engineer.

• Structural Engineering and Mechanics
• /
• v.84 no.4
• /
• pp.547-560
• /
• 2022

By presetting various reinforcement diameters in topology optimization with the discrete model finite element analysis, an algorithm of bidirectional evolutionary structural optimization of multi-level reinforcement diameter is presented to obtain the optimal reinforcement topologies which describe the degree of stress of different parts. The results of a comparative study on different reinforcement feasible domain demonstrate that the more angle types of reinforcement are arranged in the initial domain, the higher utilization rate of reinforcement of the optimal topology becomes. According to the nonlinear finite element analysis of some deep beam examples, the ones designed with the optimization results have a certain advantage in ultimate bearing capacity, although their failure modes are greatly affected by the reinforcement feasible domain. Furthermore, the bearing capacity can be improved when constructional reinforcements are added in the subsequent design. However the adding would change the relative magnitude of the bearing capacity between the normal and inclined section, or the relative magnitude between the flexural and shear capacity within the inclined section, which affects the failure modes of components. Meanwhile, the adding would reduce the deformation capacity of the components as well. It is suggested that the inclined reinforcement and the constructional reinforcement should be added properly to ensure a desired ductile failure mode for components.

• Computers and Concrete
• /
• v.20 no.6
• /
• pp.731-738
• /
• 2017

Reinforced concrete flat plates consist of slabs supported directly on columns. The absence of beams makes these systems attractive due to advantages such as economical formwork, shorter construction time, less total building height with more clear space and architectural flexibility. Punching shear failure is usually the governing failure mode of flat plate structures. Punching failure is brittle in nature which induces more vulnerability to this type of structure. To analyze the flat plate behavior under punching shear, twelve finite element models of flat plate on a column with different parameters have been developed and verified with experimental results. The maximum range of variation of punching stress, obtained numerically, is within 10% of the experimental results. Additional finite element models have been developed to analyze the influence of integrity reinforcement, clear cover and column reinforcement. Variation of clear cover influences the punching capacity of flat plate. Proposed finite element model can be a substitute to mechanical model to understand the influence of clear cover. Variation of slab thickness along with column reinforcement has noteworthy impact on punching capacity. From the study it has been noted that integrity reinforcement can increase the punching capacity as much as 19 percent in terms of force and 101 percent in terms of deformation.

• Steel and Composite Structures
• /
• v.47 no.6
• /
• pp.709-728
• /
• 2023

Experimental and analytical studies were conducted to clarify the influencing mechanisms of the longitudinal reinforcement on performance of axially loaded Reinforced Concrete-Filled Steel Tube (R-CFST) short columns. The longitudinal reinforcement ratio was set as parameter, and 10 R-CFST specimens with five different ratios and three Concrete-Filled Steel Tube (CFST) specimens for comparison were prepared and tested. Based on the test results, the failure modes, load transfer responses, peak load, stiffness, yield to strength ratio, ductility, fracture toughness, composite efficiency and stress state of steel tube were theoretically analyzed. To further examine, analytical investigations were then performed, material model for concrete core was proposed and verified against the test, and thereafter 36 model specimens with four different wall-thickness of steel tube, coupling with nine reinforcement ratios, were simulated. Finally, considering the experimental and analytical results, the prediction equations for ultimate load bearing capacity of R-CFSTs were modified from the equations of CFSTs given in codes, and a new equation which embeds the effect of reinforcement was proposed, and equations were validated against experimental data. The results indicate that longitudinal reinforcement significantly impacts the behavior of R-CFST as steel tube does; the proposed analytical model is effective and reasonable; proper ratios of longitudinal reinforcement enable the R-CFSTs obtain better balance between the performance and the construction cost, and the range for the proper ratios is recommended between 1.0% and 3.0%, regardless of wall-thickness of steel tube; the proposed equation is recommended for more accurate and stable prediction of the strength of R-CFSTs.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.145-156
• /
• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.