• Journal of the Korea Concrete Institute
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• v.21 no.1
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• pp.55-64
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• 2009

This study is aimed to derive the optimum mix proportion of the combined self compacting concrete according to cement types (blast-furnace slag cement and belite cement) and to propose the basic data to field construction work after evaluating the quality properties. Specially, lime stone powder (LSP) as binder and viscosity agent are used in the combined self compacting concrete because slurry wall of an underground LNG storage tank should be kept stability of quality during concrete working. Replacement ratio of LSP is determined by confined water ratio test and main design factors including fine aggregate ratio ($S_r$), coarse aggregate ratio ($G_v$) and water-cement ratio (W/C) are selected. Also, quality properties including setting time, bleeding content, shortening depth and hydration heat on the optimum mix proportion of the combined self compacting concrete according to cement type are compared and analyzed. As test results, the optimum mix proportion of the combined self compacting concrete according to cement type is as followings. 1) Slag cement type-replacement ratio of LSP 13.5%, $S_r$ 47% and W/C 41%. 2) Belite cement type-replacement ratio of LSP 42.7%, Sr 43% and W/C 51%. But optimum coarse aggregate ratio is 53% regardless of cement types. Also, as test results regarding setting time, bleeding content, shortening depth and hydration heat of the combined self compacting concrete by cement type, belite cement type is most stable in the quality properties and is to apply the actual construction work.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.277-285
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• 2014

The purpose of this study is to investigate experimentally the variation factors range having influence on the rheological properties of the combined self-compacting concrete according to materials quality, weighting error and site conditions. Two types cement (blast-furnace slag cement and belite cement), lime stone powder as binder and the optimum mix proportions in the preceded study are selected for this study. Also, variations for sensitivity test are as followings; (1) Concrete temperature 3 cases (2) Surface moisture of sand 5cases (3) Fineness modulus of sand 5cases (4) Specific surface of lime stone powder 3cases (5) Dosage of chemical admixture 5cases. Slump flow ($650{\pm}50mm$), 500 mm reaching time (($7{\pm}3sec$), V-type flowing time ($15{\pm}5sec$) and U-box height (min. 300 mm) are tested for sensitivity. As test results, the variations range for quality control are as followings. (1) Concrete temperature; $10{\sim}20^{\circ}C$(below $30^{\circ}C$) (2) Surface moisture of sand; $base{\pm}0.6%$ (3) Fineness modulus of sand; $2.6{\pm}0.2$ (4) Dosage of chemical admixture; $base{\pm}0.2%$ (5) Specific surface of lime stone powder $6000cm^2/g$. Compared with two types cement including based belite cement (binary type) and based slag cement (ternary type), the combined self-compacting concrete used belite cement type is most stable in the quality control because of high contents for lime stone powder and $C_2S$. It is to propose a control scheme of the combined self-compacting concrete in the actual construction work.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.461-464
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• 2008

This study is compactability and Passing ability to get to know the flowing characteristic of high flowing self-compacting concrete with mixing steel fiber of various size and diameter. After flowing test, size and diameter are getting longer, flowing performance is getting lower. It meets the standard of combined high flowing self-compacting concrete of JSCE 2 grade and passing performance from ASTM C 1621. Through this study, it can be possible to be applied in site of HSCC with mixing steel fiber.

• Computers and Concrete
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• v.19 no.5
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• pp.555-566
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• 2017

Recently some efforts have been performed to combine the advantages of light-weight and self-compacting concrete in one package called Light-Weight Self-Compacting Concrete (LWSCC). Accurate prediction of hardened properties from fresh state characteristics is vital in design of concrete structures. Considering the lack of references in mixture design of LWSCC, investigating the proper mixture components and their effects on mechanical properties of LWSCC can lead to a reliable basis for its application in construction industry. This study utilizes wide range of existing data of LWSCC mixtures to study the individual and combined effects of the components on the compressive strength. From sensitivity of compressive strength to the proportions and interaction of the components, two equations are proposed to estimate the LWSCC compressive strength. Predicted values of the equations are in good agreement with the experimental data. Application of lightweight aggregate to reduce the density of LWSCC may bring some mixing problems like segregation. Reaching a higher strength by lowered density is a challenging problem that is investigated as well. The results show that, the compressive strength can be improved by increasing the of mixture density of LWSCC, especially in the range of density under $2000Kg/m^3$.

• Computers and Concrete
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• v.25 no.5
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• pp.401-409
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• 2020

Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.449-452
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• 2008

This study is to manufacture HSCC (High flowing Self-Compacting Concrete) be able to construction without vibration & hardening, and it is stable according to the change of the surface number of aggregate and to examine the factor of reduction occurred before after hardening through the indoor experiment. It is essential to use of the thickener to increase the viscosity in the combined HSCC. In this result, it make more bubbles than HSCC of pulverulent body. The result of study has shown, through the surface air bubble by not passed air bubbles within concrete after hardening, It has bad effect in not only appearance of structures but strength & duration. It is the experiment for air bubble of concrete according to the types of aggregate (fine aggregate), mixing time of concrete, exfoliation, material of model form and so that reduce the air bubble of combined HSCC. Experiments have shown, the effect of exfoliation was bigger than the effect of form for the performance of surface finishing of combined HSCC after hardening according to the exfoliation or material of model form and the opaque guris has good condition of finishing.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.369-372
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• 2008

In this research experimentally analyzes the flow characteristics of a combined High flowing Self-Compacting Concrete of which the viscosity agent and defoaming agent addition amount are changed, to make the combined High flowing Self-Compacting Concrete that can secure the required flow performance and air amount. As a result of the experiment, the slump flow of the combined High flowing Self-Compacting Concrete added with viscosity agent increases when the viscosity agent addition amount is 0.2%(${\times}$W %). When viscosity agent addition amount increases, viscosity agent shows that it largely deviates from the regulation value in the flow time of V-funnel, which is presented in the JSCE standards (grade 2). Also, all mixtures, except for mixtures added with viscosity agent, defoaming agent, and AE agent, do not meet a target air amount $4.5{\pm}1.5%$. High flowing Self-Compacting Concrete mixtureadded with defoaming agent shows that although time passes after its first mixture, its air amount reduces a little. Based on the experiment, we can know an optimal polymer amount to obtain the required flow performance

• Computers and Concrete
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• v.5 no.5
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• pp.475-490
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• 2008

The paper explores the potential of applicability of Genetic programming approach (GP), adopted in this investigation, to model the combined effects of five independent variables to predict the mini-slump, the plate cohesion meter, the induced bleeding test, the J-fiber penetration value, and the compressive strength at 7 and 28 days of self-compacting slurry infiltrated fiber concrete (SIFCON). The variables investigated were the proportions of limestone powder (LSP) and sand, the dosage rates of superplasticiser (SP) and viscosity modifying agent (VMA), and water-to-binder ratio (W/B). Twenty eight mixtures were made with 10-50% LSP as replacement of cement, 0.02-0.06% VMA by mass of cement, 0.6-1.2% SP and 50-150% sand (% mass of binder) and 0.42-0.48 W/B. The proposed genetic models of the self-compacting SIFCON offer useful modelling approach regarding the mix optimisation in predicting the fluidity, the cohesion, the bleeding, the penetration, and the compressive strength.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.977-980
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• 2008

Recently, the study on the application of SCC(Self-Compacting Concrete) is actively underway, in order to solve the lack of flowability and the poor compacting which is one of the chronic problems of tunnel lining concrete. The aim of this study is that to verify the validity of the application of SCLC(Self-Compacting Lining Concrete) for tunnel lining concrete and to examine the characteristic of flowing and mechanics of SCLC in term of comparing before and after casting SCLC was developed by Packing Factor mix method and casted in field mix-design according to the condition of site and the characteristic of aggregate. Before casting, the tests of the capability of flowability and durability was performed by slump flow, air void and so on. Additionally, the slump flow loss is measured to evaluated the possibility of cast-in-place. Furthermore, considering on the first time SCLC casting applied to the tunnel lining in Korea, it is provided that the careful items and the correct way for construction when applied the SCLC on site.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.181-195
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• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.