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P. K. Basudhar
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Civil Engineering, Building, Construction & Architecture Engineering Transportation

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ANALYSIS OF CULVERT APPROACHES WITH PILES OF VARYING LENGTH M. R. Madhav; P. K. Basudhar; N. Miura
Lowland Technology International Vol 1 No 1, June (1999)
Publisher : International Association of Lowland Technology

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Abstract

For the construction of culverts and their approaches on soft and highly compressible soils, an innovative approach is to provide piles with length decreasing with distance from the culvert. The equivalent stiffness of the piled strip as a function of the relative length of the piles estimated from Brown and Wiesner (1976), is bounded by Linear and exponential variations with distance. An extended Pasternak type model is proposed for the culvert approaches with piles of varying length. The response of the system is shown to be governed by the relative stiffnesses of the granular bed, the culvert foundation, the approaches at the near and far ends and the relative pile length to diameter ratio. The settlement profiles are presented for the typical values of the above parameters. The relative stiffness of the granular pad has a significant effect on settlements and on the loads transferred to the culvert foundations.
PREDICTION OF HYDRAULIC CONDUCTIVITY OF CLAY LINERS USING ARTIFICIAL NEURAL NETWORK S. K. Das; P. K. Basudhar
Lowland Technology International Vol 9 No 1, June (2007)
Publisher : International Association of Lowland Technology

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This paper pertains to prediction of hydraulic conductivity of soil used as clay liners using artificial neural networks based on soil classification test results like Atterberg’s limit, grain size and compaction characteristics. Feed forward back propagation neural network has been used and trained with different combination of input parameters of laboratory test results available in literature. Statistical performances criteria like root mean square error, correlation coefficient, coefficient of determination and overfitting ratio are used to compare different neural network models, the available statistical model and the results obtained using group method of data handling (GMDH) neural network. The neural network models are found to be more efficient and reliable compared to statistical model. Identification of important soil parameters affecting the hydraulic conductivity of soils is discussed. A model equation is presented with weights of the trained neural network as model parameter.
Co-Authors M. R. Madhav N. Miura S. K. Das