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

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.