Browsing by Author "Gumaste, SD"

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    Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity
    Erzin, Y; Rao, BH; Patel, A; Gumaste, SD; Singh, DN
    The knowledge of soil electrical and thermal resistivities is essential for several engineering projects such as laying of high voltage buried power cables, nuclear waste disposal, design of fluidized thermal beds, ground modification techniques etc. This necessitates precise determination of these resistivities, and relationship between them, which mainly depend on the soil type, its origin, compaction density and saturation. Such a relationship would also be helpful for determining one of these resistivities, if the other one is known. With this in view, efforts were made to develop artificial neural network (ANN) models that can be employed for estimating the soil electrical resistivity based on its soil thermal resistivity and the degree of saturation. To achieve this, measurements of electrical and thermal resistivities were carried out on different types soils compacted at different densities and moisture contents. These models were validated by comparing the predicted results vis-A-vis those obtained from experiments. The efficiency of these ANN models in predicting the soil electrical resistivity has been demonstrated, if its thermal resistivity is known. These ANN models are found to yield better results as compared to the generalized relationships proposed by the earlier researchers. (C) 2009 Elsevier Masson SAS. All rights reserved.
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    Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils
    Erzin, Y; Gumaste, SD; Gupta, AK; Singh, DN
    This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-a-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.