• Geomechanics and Engineering
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• v.8 no.4
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• pp.595-613
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• 2015

An experimental investigation into the uplift capacity of horizontal square plate anchors in sand with and without geogrid reinforcement is reported. The parameters investigated are the effect of the depth of the single layer of geogrid, vertical spacing of geogrid layers, number of geogrid layers, length of geogrid layers, the effects of embedment depth, and relative density of sand. A series of three dimensional finite element analyses model was established and confirmed to be effective in capturing the behaviour of plate anchor-reinforced sand by comparing its predictions with experimental results. The results showed that the geogrid reinforcement had a considerable effect on the uplift capacity of horizontal square plate anchors in sand. The improvement in uplift capacity was found to be strongly dependent on the embedment depth and relative density of sand. A satisfactory agreement between the experimental and numerical results on general trend of behaviour and optimum geometry of reinforcement placement is observed. Based on the model test results and the finite element analyses, optimum values of the geogrid parameters for maximum reinforcing effect are discussed and suggested.

• 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 Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.298-299
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• 2014

As a governmental plan for real estate revitalization, remodeling vertical extension has been permitted. Thus, the Ministry of Land, Infrastructure and Transport preannounced proclaiming the revised Housing Act and establishing the remodeling basic plan, and it is anticipated that the remodeling market will be revitalized in earnest after the enforcement of remodeling vertical extension(April 25th. 2014). As vertical extension is applicable up to 3 stories, the safety of building for remodeling is becoming important, so most remodeling construction works use various methods for structural reinforcement. In this process, the selection of structural reinforcement method has depended on structural engineer's experience and knowledge and there has been a limitation in selecting the optimum structural reinforcement method which considers remodeling project characteristics. Therefore, this study analyzed the factors to determine the kinds of structural reinforcement method in a remodeling project and suggested a process to select the best structural reinforcement method of remodeling construction.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.479-484
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• 2000

In this study, optimal design of reinforced concrete piers under seismic load is numerically investigated. Object function is the area of the concreate-section. Design variables are the total area of reinforcement and concrete-section dimension(Circular section diameter). Constraints of the design strength of the column, longitudinal reinforcement ratio and lower and upper bounds on the design variables are imposed. The reinforcement concrete column is analysed and designed by the Ultimated Strength Design method and load combination involving dead, live, wind and seismic load is used. For numerical optimization, ADS(Garret N, Vanderplaats_ routine is used. From the result of numerical examples, the concrete-section dimension was reduced, but longitudinal reinforcement was not changed. The results show that confinement reinforcement was reduced and confinement reinforcement spacing is increased. The higher strength of reinforcement used, the more concrete-section area was reduced.

• Structural Engineering and Mechanics
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• v.88 no.2
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• pp.179-188
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• 2023

The mechanical behaviours of biopolymer-treated soil depend on the formation of soil-biopolymer matrices. In this study, various biopolymers(e.g., xanthan gum (XG), locust bean gum (LBG), sodium alginate (SA), agar gum (AG), gellan gum (GE) and carrageenan kappa gum (KG) are selected to treat three types of natural soil at different concentrations (e.g., 1%, 2% and 3%) and curing time (e.g., 4-365 days), and reveal the reinforcement effect on natural soil by using unconfined compression tests. The results show that biopolymer-treated soil obtains the maximum unconfined compressive strength (UCS) at curing 14-28 days. Although the UCS of biopolymer-treated soil has a 20-30% reduction after curing 1-year compared to the maximum value, it is still significantly larger than untreated soil. In addition, the UCS increment ratio of biopolymer-treated soil decreases with the increase of biopolymer concentration, and there exists the optimum concentration of 1%, 2-3%, 2%, 1% and 2% for XG, SA, LBG, KG and AG, respectively. Meanwhile, the optimum initial moisture content can form uniformly biopolymer-soil matrices to obtain better reinforcement efficiency. Furthermore, the best performance in increasing soil strength is XG following SAand LBG, which are significantly better than AG, KG and GE.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.167.1-167.1
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• 2005

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.763-783
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• 2016

A methodology based on Teaching Learning-Based Optimization (TLBO) algorithm is proposed for optimum design of reinforced concrete retaining walls. The objective function is to minimize total material cost including concrete and steel per unit length of the retaining walls. The requirements of the American Concrete Institute (ACI 318-05-Building code requirements for structural concrete) are considered for reinforced concrete (RC) design. During the optimization process, totally twenty-nine design constraints composed from stability, flexural moment capacity, shear strength capacity and RC design requirements such as minimum and maximum reinforcement ratio, development length of reinforcement are checked. Comparing to other nature-inspired algorithm, TLBO is a simple algorithm without parameters entered by users and self-adjusting ranges without intervention of users. In numerical examples, a retaining wall taken from the documented researches is optimized and the several effects (backfill slope angle, internal friction angle of retaining soil and surcharge load) on the optimum results are also investigated in the study. As a conclusion, TLBO based methods are feasible.

• Computers and Concrete
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• v.24 no.4
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.863-874
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• 2009

The engineering characteristics and the reinforcement effect of the elephant foot method were discussed with parametric study. The elephant foot method is adopted to support the loads transferred from tunnel crown and improve bearing capacity of elephant foot in poor ground condition. The evaluation of reinforcement effect, which has the mechanical relationship between ground condition, footing size and reinforcement system, was carried out through the previous research and numerical analysis. In addition, the simple design chart was proposed to estimate the applicability of the elephant foot reinforcement method. It will be practical for the engineer to determine the optimum reinforcement method for safe tunnelling in soft ground condition.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.217-225
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• 2003

The ground reinforcement effect of pressure grouting depends on grout penetration into ground. It is not, however, easy to predict the grout penetration in the design process because of the heterogeneity of ground conditions. This study investigates the proper grouting pressure and grouting method through laboratory model tests for pressure grouting using loose to medium dense crushed rock and sandy ground using specially designed and fabricated device. The optimum injection pressure, grout quantity and injection time are investigated through performing pressure grouting under changing conditions of injection in this test. From the test results, it was found that optimum injection pressure covers the range of 3 to 4kg/cm$^2$.