• Journal of the Korea Institute of Building Construction
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• v.17 no.1
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• pp.23-30
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• 2017

The aim of the research is to suggest an economical and sustainable method of reducing pH of recycled aggregate as an embanking and a covering materials. Because of the unhydrated cement based materials on the surface of the recycled aggregate, it causes a severe problem on environment with leaked high alkali water from embankment and covering by using recycled aggregate. In this research, to reduce the pH of recycled aggregate, regarding the recycled fine aggregate, eight different methods were tested and analyzed with three different categorized: natural treatment, artificial treatment, and chemical treatment. From the results of experiment, it was considered that the most efficient method of reducing pH of recycled aggregate was the chemical treatment using acid such as $CO_2$ acceleration or monoammonium phosphate (MAP), and diammonium phosphate (DAP). Especially, using MAP and DAP, fertilizers, is the most efficient method of reducing pH with its time duration and performance.

• Proceedings of the Korea Concrete Institute Conference
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• 1991.04a
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• pp.99-104
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• 1991

It is necessary to generalize the use for structural ALA Concrete in our country, as increasing in the need for the development of ALA and the use of ALA Concrete which is related with the diminution of the self load and foundation section of structure responding to the realistic requirement against the decrease of natural aggregate and the high-rising and large-sizing of structures. This little study, therefore intended to help in the mixing design of concrete by considering the fundamental properties of ALA Concrete used with expanded clay, which is considered by acopting the experimental factors such as unit cement content, water cement ratio and the rate of fine aggregate. By considering the results of this experiment, it has difficulty in getting expected slump with the unit water content of normal concrete because of the large absorption of lightweight aggregate, and because the weight of unit volume and specific gravity ALA Concrete are small it appears that the strength and Elastic Modulus of that are small too and that it is more ductile than normal concrete.

• Journal of the Korea Institute of Building Construction
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• v.3 no.3
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• pp.107-112
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• 2003

This study is to investigate the properties of concrete with crushed sand on site and to propose a quality guideline for its use as artificial sand and concrete. From our experimental result in laboratory and site, we found that demand water of concrete with crushed sand for target slump increased by 18kg/m3 compared to mixed sand and l8kg/m3 compared to sea sand respectively. The compressive strength increased by around 3∼6% when compared to concrete with sea sand. Accordingly, our study showed that the combined sand mixed with sea sand would be desirable to obtain workability and strength of concrete including dry shrinkage and bleeding test. Furthermore, the optimal replacement percentage of crushed sand was 50% with sea sand. As such, crushed sand would be sufficient as fine aggregate for concrete in terms of economic efficiency and quality. Crushed sand, on the other hand can only be used as fine aggregate when VFS(Very Fine Sand) is below 3.5 percentage of weight of sand and particle shape is above 55 percentage. Also, the particle shape and microsand passing NO.200 sieve should continually be improved to increase workability of concrete on site.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.7
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• pp.11-19
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• 2003

In this study, unsaturated polyester resin, artificial lightweight coarse aggregate, artificial lightweight fine aggregate, heavy calcium carbonate and steel fiber were used to produce a steel fiber-reinforced lightweight polymer concrete with which mechanical properties were examined. Results of this experimental study showed that the flexural strength of unnotched steel fiber-reinforced lightweight polymer concrete increased from 8.61 to 13.96 MPa when mixing ratio of fiber content increased from 0 to 1.5%. Stress intensity factors($K_{IC}$) increased with increasing fiber content ratio while it did not increase with increasing notch ratio. Energy release rate ($G_{IC}$) turned out to depend upon the notch size, and it increased with increasing steel fiber content.

• Computers and Concrete
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• v.5 no.6
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• pp.559-572
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• 2008

The effects of fly ash and superplasticizer (SP) on workability of concrete are quite difficult to predict because they are dependent on other concrete ingredients. Because of high complexity of the relations between workability and concrete compositions, conventional regression analysis could be not sufficient to build an accurate model. In this study, a workability model has been built using artificial neural networks (ANN). In this model, the workability is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, coarse aggregate, and fine aggregate. The effects of water/binder ratio (w/b), fly ash-binder ratio (fa/b), superplasticizer-binder ratio (SP/b), and water content on slump were explored by the trained ANN. This study led to the following conclusions: (1) ANN can build a more accurate workability model than polynomial regression. (2) Although the water content and SP/b were kept constant, a change in w/b and fa/b had a distinct effect on the workability properties. (3) An increasing content of fly ash decreased the workability, while raised the slump upper limit that can be obtained.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1294-1301
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• 2010

This study is an experimental research for the dispersion behavior and impact characteristics of debris flow according to change of slope. Large scale experimental setup for the debris flow was established to simulate the artificial rainfall and control the ground slope. Parameters such as materials of debris flow, slope, and length of slope were used for the experiments. After the experiments, it was found that the speed of ground material components was increased about 28~47%. It was found that speed can be increased by increasing the particle size. Furthermore, maximum/final loads for ground material components were increased 89% for the coarse aggregate and 68% for the fine aggregate comparing with sand.

• Computers and Concrete
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• v.13 no.5
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• pp.621-632
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• 2014

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the compressive strength of High Performance Concrete (HPC) containing binary and quaternary blends. The investigations were done on 23 HPC mixes, and specimens were cast and tested after 7, 28 and 56 days curing. The obtained experimental datas of 7, 28 and 56 days are trained using ANN which consists of eight input parameters like cement, metakaolin, blast furnace slag and fly ash, fine aggregate, coarse aggregate, superplasticizer and water binder ratio. The corresponding output parameters are 7, 28 and 56 days compressive strengths. The predicted values obtained using ANN show a good correlation between the Experimental data. The performance of the 8-9-3-3 architecture was better than other architectures. It concluded that ANN tool is convenient and time saving for predicting compressive strength at different ages.

• Computers and Concrete
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• v.28 no.6
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• pp.567-583
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• 2021

The use of waste materials as a binder or aggregate in the concrete mixture is a great step towards sustainability in the construction industry. Waste rubber (WR) can be used as coarse and fine aggregates in concrete and improves the crack resistance, impact resistance, and fatigue life of the produced concrete. However, the mechanical properties of rubberized concrete degrade significantly by replacing the natural aggregate with WR. To have accurate estimations of the mechanical properties of rubberized concrete, two machine learning methods consisting of artificial neural network (ANN) and neuro-fuzzy system (NFS) were served in this study. To do this, a comprehensive dataset was collected from reliable literature, and two scenarios were addressed for the selection of input variables. In the first scenario, the critical ratios of the rubberized concrete and the concrete age were considered as the input variables. In contrast, the mechanical properties of concrete without WR and the percentage of aggregate volume replaced by WR were assumed as the input variables in the second scenario. The results show that the first scenario models outperform the models proposed by the second scenario. Moreover, the developed ANN models are more reliable than the proposed NFS models in most cases.

• Journal of the Korean Recycled Construction Resources Institute
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• v.6 no.4
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• pp.267-274
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• 2018

Though the wet bottom ash has been used as a type of lightweight aggregate, dry bottom ash, new type bottom ash from coal combustion power plant, has scarcely researched. It is excellent lightweight aggregate in the view point of construction material. This study is performed to check the applicability of dry bottom ash as a fine aggregate in lightweight aggregate concrete, by analyzing various properties of fresh and hardened concrete. We get results that the slump of concrete is within the target range at less than 75% replacement rate of dry bottom ash, the air content is not affected by the replacement rate of dry bottom ash, the bleeding capacity is less than $0.025cm^3/cm^2$ at 75% under of the replacement rate of dry bottom ash, and the compressive strength of concrete show 90% or more comparing the base mix while initial strength development is a little low. Oven dry unit weight of concrete is reduced by 8.9% when replaced 100% dry bottom ash, and dry shrinkage tends to decrease depending on increase of replacement rate of dry bottom ash. Modulus of elasticity of concrete shows no decease at 50% over of the replacement rate of dry bottom ash, while modulus of elasticity of concrete decreases when the replacement rate increases further. The dry bottom ash, when used as a fine aggregate in lightweight concrete, can be used effectively without any deterioration in quality.