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- W2015071534 abstract "The permeability coefficient is a one of the important soil parameters that is essential to geotechnical designs. As the determination of the permeability coefficient from field tests is relatively time-consuming, empirical formulas based on soil parameters can be useful. Over the decades, a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, grain size, specific gravity, and others. In this study, an artificial neural network (ANN) model is proposed to estimate the permeability coefficient with various permeability test sets. The permeability coefficient was modeled as a function of six variables including the natural water content, specific gravity, and grain sizes. 379 in-situ permeability tests performed at 21 construction sites in Korea were used for the training and testing of the ANN model. It was found that the permeability coefficient could be reasonably estimated using developed neural network model." @default.
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- W2015071534 date "2011-10-01" @default.
- W2015071534 modified "2023-09-23" @default.
- W2015071534 title "Development of Neural Network Model to Estimate the Permeability Coefficient of Soils" @default.
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- W2015071534 doi "https://doi.org/10.1080/1064119x.2011.554963" @default.
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