Browsing by Author "Singh, DN"

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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.
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    Factors influencing the crushing strength of some Aegean sands
    Erzin, Y; Patel, A; Singh, DN; Tiga, MG; Yilmaz, I; Srinivas, K
    Engineering properties of sands mainly depend on the integrity of the particles, which in turn has a strong bearing on their crushing strength. Seven different Aegean sands were tested for mineralogy, particle shape, size and specific gravity and the influence of aspect ratio, particle composition, particle shape and size on the crushing strength was examined. As the Aegean sands have a small range of sphericity and roundness, crushing strength tests were also performed on five Anatolian sands. A multiple regression analysis was carried out and an equation proposed to determine the crushing strength value of the Aegean sands. The computed values were found to be in good agreement with those obtained from the experimental investigations. It is concluded that the equation is sufficiently accurate to be a useful, time- and cost-effective way of obtaining crushing strength estimations at the preliminary stage of site investigations.
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    Artificial neural network models for predicting soil thermal resistivity
    Erzin, Y; Rao, BH; Singh, DN
    Thermal properties of soils are of great importance in view of the modern trends of utilizing the subsurface for transmission of either heated fluids or high power currents. For these situations, it is essential to estimate the resistance offered by the soil mass in dissipating the heat generated through it. Several investigators have tried to develop mathematical and theoretical models to estimate soil thermal resistivity. However, it is evident that these models are not efficient enough to predict accurate thermal resistivity of soils. This is mainly due to the fact that thermal resistivity of soils is a complex phenomenon that depends upon various parameters viz., type of the soil, particle size distribution and its compaction characteristics (i.e., dry density and moisture content). To overcome this, Artificial Neural Network (ANN) models, which are based on experimentally obtained thermal resistivity values for clay, silt, silty-sand, fine- and coarse-sands, have been developed. Incidentally, these soils are the most commonly encountered soils in nature and exhibit entirely different characteristics. The thermal resistivity of these soils, corresponding to their different compaction states, was obtained with the help of a laboratory thermal probe and compared vis-a-vis those obtained from the ANN model. The thermal resistivity of these soils obtained from ANN models and experimental investigations are found to snatch extremely well. The performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance account for were used to control the performance of the prediction capacity of the models developed in this study. In addition to this, thermal resistivity of these soils obtained from ANN models were compared with those computed from the empirical relationships reported in the literature and were found to be superior. The study demonstrates the utility and efficiency of the ANN model for estimating thermal resistivity of soils. (c) 2007 Elsevier Masson SAS. All rights reserved.
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    Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods
    Bayat, H; Ebrahimi, E; Ersahin, S; Hepper, EN; Singh, DN; Amer, AMM; Yukselen-Aksoy, Y
    Depending on the method used, measuring the specific surface area (SSA) can be expensive and time consuming and limited numbers of studies have been conducted to predict SSA from soil properties. In this study, 127 soil sample data were gathered from the available literature. The data set included SSA values and some of the soil physical and chemical index properties. At the first step, linear regression, non-linear regression, regression trees, artificial neural networks, and a multi-objective group method of data handling were used to develop seven pedotransfer functions (PTFs) for the purpose of finding the best method in predicting SSA. Results showed that the artificial neural networks performed better than the other methods used in the development and validation of PTFs. At the second step, to find the best set of SSA for predicting input variables and to investigate the importance of the input parameters, the artificial neural networks were further used and 25 models were developed. The results showed that the PTF, containing the input variables of sand%, clay%, plastic limit, liquid limit, and free swelling index performed better than the other PTEs. This can be attributed to the close relation between the free swelling index and Atterberg limits with the soil clay mineralogy, which is one of the most important factors controlling SSA. The sensitivity analysis showed that the greatest sensitivity coefficients were found for the cation exchange capacity, clay content, liquid limit, and plasticity index in different models. Overall, the artificial neural networks method was proper to predict SSA from soil variables. (C) 2015 Elsevier B.V. All rights reserved.