• Journal of the Korea Concrete Institute
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• v.16 no.5 s.83
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• pp.591-596
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• 2004

In order to evaluate the applicability of steel slag aggregates for tetrapod concrete, the properties of concrete as structural material were investigated. The biochemical research of marine concrete using steel slag aggregates was also carried out. The tested concrete properties are slump, ai content, compressive strength, splitting tensile strength, elastic modulus, carbonation, hydration heat, freezing and thawing, sulfate attack, drying shrinkage, etc. The biochemical experiments are carried to research the propagation and reproduction of seaweeds and survival of bottom dwelling species. According to this experiment results, the steel slag aggregate content did not have a significant effect on compressive strength, splitting tensile strength and elastic modulus. The durability of concrete was not influenced by the steel slag aggregate content. From the biochemical research, steel slag aggregate can be evaluated as the material that is ideally suited for promoting propagation and reproduction of seaweeds and sessile benthos.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.1
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• pp.109-117
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• 2011

It was calculated using empirical formulas for the weight of Tetrapod, which was a representative armor unit in the rubble mound breakwater in Korea. As the formulas were evaluated from a curve-fitting with the result of hydraulic test, the uncertainty of experimental error was included. Therefore, the neural network and M5' model tree were used to minimize the uncertainty and predicted the stability number of armor block. The index of agreement between the predicted and measured stability number was calculated to assess the degree of uncertainty for each model. While the neural network with the highest index of agreement have an excellent prediction capability, a significant disadvantage exists that general designers can not easily handle the method. However, although M5' model tree has a lower prediction capability than the neural network, the model tree is easily used by the designers because it has a good prediction capability compared with the existing empirical formula and can be used to propose the formulas like an empirical formula.