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International Journal of Scientific Research in Science, Engineering and Technology![]() ![]() ![]() |
Authors(2):
This paper presents the application soft computing techniques like Artificial Neural Network (ANN) tool of MATLAB and Multiple Regression Analysis (MLR) tool of STATISTICA to build models to help predict California Bearing Ratio value of Coarse grained soils from the basic properties of soil viz. optimum moisture content and maximum dry density, liquid limit and Coarse fraction. Out of total Fifty four soil data sets, 38 were used for training and 16 were used for testing. It was observed that prediction of CBR from the properties of soil was better through ANN than MLR. The performance of the developed ANN model has been validated by actual laboratory tests and a good correlation of 0.9 was obtained.
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