• 월간낙농육우
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• v.33 no.9
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• pp.112-115
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• 2013

• Journal of Korea Water Resources Association
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• v.41 no.2
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• pp.127-135
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• 2008

The objective of sewer rehabilitation is to improve its function while eliminating inflow/infiltration (I/I) and insufficient carrying capacity (ICC). Such rehabilitation efforts, however, have not been particularly successful due to a lack of sewer data and unsystematic field practices. The present study aimed to solve these problems by developing a decision making model consisting of two models: the rehabilitation weighting model (RWM) and the rehabilitation priority model (RPM). In RWM, the I/I of each pipe in a drainage district is estimated according to various defects, with each defect given an individual weighting factor using an analytic hierarchy process (AHP). RPM determines the optimal rehabilitation priority (ORP) using a genetic algorithm (GA). The developed models can be used to overcome the problems associated with unsystematic practices and, in practice, as a decision making tool for urban sewer system rehabilitation.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.143-152
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• 2003

This paper suggests the improvement of the Sumjinkang for the estimation of areal averages of heavy rainfall events based on the optimal network and three existing networks. The problem consists of minimizing an objective function which includes both the accuracy of the areal mean estimation as expressed by the Kriging variance and the economic cost of the data collection. The wellknown geostatistical variance-reduction method is used in combination with SATS which is an algorithm of minimization. At the first stage, two kinds of optimal solutions are obtained by two trade-off coefficients. One of them is a optimal solution, the other is an alternative. At the second stage, a quasi optimal network and a quasi alternative are suggested so that the existing raingages near to the selected optimal raingages are included in the two solutions instead of gages of new gages.

• Journal of Korea Water Resources Association
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• v.38 no.3 s.152
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• pp.199-212
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• 2005

The study on the plan for rehabilitation project of domestic water distribution system - especially using Heuristic Algorithm as Genetic Algorithm which is expected to provide a more optimal solution effectively - has not been done sufficiently. The purpose of this study is the development of the optimal decision making system for the rehabilitation of the water distribution system considering economic and hydraulic influences using ReHS which is recent study of OR technique. Five different models with different objective functions are developed and tested to virtual pipe network according to various conditions considered in this study. These models provide more options for the rehabilitation of pipe network systems compared to previously suggested models in the literature.

• Journal of the Korean Geotechnical Society
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• v.39 no.2
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• pp.5-17
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• 2023

In this study, a classification and uniaxial compression test of soil was conducted on 15 collapsed sites to use ground improvement with excellent protection effect owing to the increase of localized heavy rain in Korea. The Casagrande method and fall cone test were performed on the field soil to derive an expression for comparing liquid limit and plastic limit values, soil classification, and correlation between each other. By deriving the optimal mixing ratio of the ground improvement agent using uniaxial compressive strength for each soil classification, the classification of the fine-grained soil was not clear owing to the proficiency difference and test error. However, after classifying using the fall cone test, it was possible to suggest a clear optimal mixing ratio.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.425-430
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• 2002

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05a
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• pp.477-480
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• 2003

• Journal of the Korean GEO-environmental Society
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• v.5 no.2
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• pp.15-21
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• 2004

This study is to develop simple solidifying agent to improve loose sand ground by admixing or injecting. This paper studied the optimum mixing ratio of micro cement, bentonite, chemistry admixture, plasticizer, accelerator for the optimum fluidity and strength. The optimum mixing ratio of micro cement and bentonite is 70% : 20%, the optimum ratio of the weight of rapid solidifying agent to the weight of total improved soil is about 8%, the optimum curing period is five days.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.199-204
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• 2009

In this paper, a method for determining the boundary conditions which derived from the random obstacles on the curves was presented. A simple computer program using this method is also developed. This determination method and program can be used for a good engineering tool in optimal curve design and an Radius-transition curve length $(R-L_t)$ combination within the permissible zone can be improved without any increased costs.

• Weed & Turfgrass Science
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• v.7 no.2
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• pp.148-157
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• 2018

Research was initiated to evaluate four domestic and overseas organic soil amendments (SAs) on turfgrass groundcover and density and to provide basic information on practical sports turf establishment. This study was conducted in Agrostis palustris Huds. (CB) grown in sand-based root zone. A total of 20 treatments of SA+sand were prepared by mixing 10 to 50% (v/v). These amendments were SABP (Berger Peat), SAEP (Eco-Peat), SAGS (G1-Soil), and SAPP (Premier Peat). Turfgrass groundcover and density significantly varied with SAs, its mixing rate to sand and week after seeding (WAS). Cumulative turfgrass density was variable, but a great change occurred between 2 and 4 WAS. Turfgrass density at 2 WAS ranged from 36.7 (SABP 30) to 89.7% (SAGS 20), being 53.0% in differences among treatments. However, CB reached to carrying capacity around 6 WAS. Thus, most treatments were similar to 90% or so in density. At the end of study, overall groundcover ranged between 60.7 (SAEP 10) and 96.7% (SAPP 50). Proper mixing rate was variable with SAs, being 10 and 20% for SABP and SAGS, respectively. But the optimum rate was 50% for both SAEP and SAPP.