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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Thursday 20 September 2018

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Artificial Neural Network Modeling of Sub grade Soil stabilized with Bagasse Ash and Geogrid


Volume 25, Issue 1, June 2016, Pages 87–100

 Artificial Neural Network Modeling of Sub grade Soil stabilized with Bagasse Ash and Geogrid

C. Rajakumr and T. Meenambal

Original language: English

Received 22 February 2016

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


For highway construction projects, sub grade soil stabilization is one of the prime and major processes. The strength of the sub grade soil is indicated by its California bearing ratio (CBR) value which is quite expensive and time consuming. In order to overcome this situation, the present study aims in predicting the soaked CBR value for the stabilized soil by Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) modeling. Experiments were done to stabilize the soil with the addition of varying percentages of bagasse ash ranging from 0% to 10%, in an increment of 2% and also with geogrid layers. Maximum dry density, optimum moisture content, plasticity index, bagasse ash fraction and number of geogrid layers were taken as input variables and soaked CBR value as output variable for the regression based models. It is observed that ANN model is accurate than the MRA model in predicting the soaked CBR value of soil stabilized with bagasse ash and geogrid, both the measured experimental values and predicted values are in good agreement.

Author Keywords: Clayey Subgrade, Bagasse ash, Geogrid, OMC, UCC, CBR, ANN, MRA.


How to Cite this Article


C. Rajakumr and T. Meenambal, “Artificial Neural Network Modeling of Sub grade Soil stabilized with Bagasse Ash and Geogrid,” International Journal of Innovation and Scientific Research, vol. 25, no. 1, pp. 87–100, June 2016.