• Journal of the Korea Concrete Institute
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• v.14 no.1
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• pp.41-48
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• 2002

In this paper, permeable polymer concretes with unsaturated polyester or vinylester resin content from 5 to 8 weight ％, resin-filler ratio of 1 : 1, sand content from 0 to 15 weight ％ and crushed stone of size 2.5∼10 mm were prepared, and tested for compressive strength, flexural strength and water permeability. The effects of the resin and sand contents on the properties of permeable polymer concrete were discussed. It is concluded from the test results that increase in the strength and decrease in the coefficient of permeability of the permeable polymer concrete arc clearly observed with increasing the resin and sand contents. The permeable polymer concrete showed compressive strength in the range of 170 to 350 kgf/$\textrm{cm}^2$ and flexural strength in the range of 40 to 90 kgf/$\textrm{cm}^2$ at coefficient of permeability from 0.1 to 1.0 cm/sec in this experiment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.5
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• pp.75-83
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• 2019

In order to fulfill the obligation to voluntarily reduce greenhouse gas emissions under the Paris Climate Agreement, the proportion of coal and nuclear power generation is reduced worldwide and national efforts are being made to spread renewable energy including solar power generation. Korea also intends to increase the proportion of renewable energy generation to 30~35% by 2040 by introducing laws and regulations. In addition, while the country is trying to apply solar power generation to sidewalks and roads, there is no research related to it in Korea. Therefore, as a precedent study to develop solar power generation roads, solar power generation concrete blocks applicable to sidewalks and plazas were developed and the applicability was evaluated by constructing them on the site. As a result of indoor experiment, compressive strength was measured by 25.5~35.7MPa and flexural strength was measured by 5.1~10.5MPa, which showed that all domestic standards were satisfied. However, the higher the unit cement amount, the lower the strength was measured according to the mixing of the broken fine aggregate. The absorption rate was 5.7%, which satisfied the domestic standard of 7% or less. As a result of the freeze-thawing test, the reduction rate of the compressive strength after 100 cycles was up to 6.3%. As a result of measuring the settlement amount after construction, the maximum of 2.498mm was measured and irregular settlement occurred in the overall area, which is because the resolution of the sand layer was poor during construction. Maintenance techniques of sidewalk concrete block and solar panel need to be established more efficiently through long-term operation in the further.

• Journal of the Korea Concrete Institute
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• v.14 no.4
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• pp.457-466
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• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.