• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.2
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• pp.247-254
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• 2008

Resilient materials are generally used for the floating floors to reduce the floor impact sound. Dynamic stiffness of resilient material, which has the most to do with the floor impact sound reduction. The resilient materials available in Korea include EPS(styrofoam), recycled urethane types, EVA(ethylene vinylacetate) foam rubber, foam PE(polyethylene). glass fiber & rock wool, recycled tire, foam polypropylene. compressed polyester, and other synthetic materials. In this study, we tested dynamic stiffness of resilient material and floor impact sound reduction characteristic to a lot of kinds of resilient materials. It was found that dynamic stiffness of multi-layered damping material could be estimated if know value of each layer that compose whole structure. And the test showed that the amount of the heavy-weight impact sound reduction appeared by being influenced from this dynamic stiffness of resilient material. The dynamic stiffness looked like between other resilient materials, a similar to the amount of the heavy-weight impact sound reduction was shown.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1747-1755
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• 2008

Design of the reinforced roadbed thickness is concerned with safe operation of trains at specified levels of speed, axle load and tonnage. There are two methods for evaluating it. One is using an experimental equation and the other is using elastic theory with considering axle load, material properties of subsoils and allowable elastic settlement. Multi-layered theory is used to determine reinforced roadbed thickness by RTRI. Although their reinforced roadbed thickness is designed with an objective of achieving a minimum standard 2.5mm of settlement on the subgrade surface, it is hardly applied to real design. Li(1994) has suggested the experimental model which design approach is to limit plastic strain and deformations for the design period. It is worth due to adopting soil equivalent number of repeated load application. Moreover, it has been a more advanced method than existing design methods because including resilient modulus of subsoil beneath track, soil deviator stress caused by train axle loads and MGT. In this paper, it is analyzed under domestic track conditions to estimate the reinforced roadbed thickness with different soil types.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.