California Bearing Ratio (CBR) value is an indicator of subgrade soil strength and is used often for design of flexible pavements. The conventional soaked CBR testing method is expensive and time consuming. To overcome this situation, it is appreciable to predict CBR value of subgrade soil with simple properties of soils such as index properties which include grain size analysis (% Gravel, % Sand, % Fines), Liquid Limit (LL), and Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) from Modified Compaction test.

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.

Anupama. U, Harini H. N.

ANN, CBR, LL, MLR, Modified OMC, MDD, Coarse Fraction, Soils.

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Publication Details
Published in : Volume 2 | Issue 4 | July-August - 2016 Date of Publication Print ISSN Online ISSN 2016-08-30 2395-1990 2394-4099
Page(s)	Manuscript Number		Publisher
545-550	IJSRSET1624112		Technoscience Academy
Cite This Article
Anupama. U, Harini H. N. , "Prediction of CBR Value of Coarse Grained Soils by Soft Computing Techniques", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.545-550 , July-August-2016. URL : http://ijsrset.com/IJSRSET1624112.php