Prediction of CBR Value of Coarse Grained Soils by Soft Computing Techniques
Keywords:
ANN, CBR, LL, MLR, Modified OMC, MDD, Coarse Fraction, Soils.Abstract
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.
References
- Sarmadian and R. Taghizadeh Mehrjardi “Modeling of Some Soil Properties Using Artificial Neural Network and Multivariate Regression in Gorgan Province, North of Iran”-Global Journal of Environmental Research (2008).
- Gunayd?n “Estimation of soil compaction parameters by using STATISTICAL analyses and artificial neural networks” Environmental Geology (2009).
- Zelalem Worku Ferede “Prediction of California Bearing Ratio (CBR) value from index properties of soil”-Addis Ababa University, April( 2010).
- Mehmet Saltan, Mesut TI Gdemir, Mustafa Karasahin “Artificial Neural Network Application for Flexible Pavement Thickness Modeling”-Turkish J. Eng. Env. Sci.,(2006).
- Taskiran “Prediction of California bearing ratio (CBR) of fine grained soils by AI methods” Advances in Engineering Software-41 (2010).
- Venkatasubramanian and Dhinakaran “ANN model for predicting CBR from index properties of soils” International journal of civil and structural engineering- Volume 2, No 2, 2011.
- Patel, Rashmi S. Desai, M.D “CBR Predicted by Index Properties for Alluvial Soils of South Gujarat”, Indian Geotechnical Conference-December(2010).
- Saklecha P.P, Katpatal Y.B “Correlation of Mechanical Properties of weathered Basaltic Terrain for strength Characterization of foundation using ANN” International Journal of Computer Applications-Nov(2011).
- Mukesh A. Patel and Dr. H. S. Patel, “Correlation Between Physical Properties and California Bearing Ratio Test on Soils of Gujarat Region in Both Soak and Unsoak Condition”, International Journal of Civil Engineering & Technology (IJCIET), Volume 3, Issue 2, 2012, pp. 50 - 59, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.
- K.V.Krishna Reddy, “Benefit Analysis of Subgrade and Surface Improvements in Flexible Pavements”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 2, 2013, pp. 385 - 392, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.
- K.V.Krishna Reddy, “Correlation Between California Bearing Ratio and Shear Strength on Artificially Prepared Soils with Varying Plasticity Index”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 6, 2013, pp. 61 - 66, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.
- Harini H.N and Sureka Naagesh, “Fine grained soils CBR prediction by Multiple Linear Regression and Artificial Neural Networks”- Volume 5, Issue 2, February (2014), pp. 119-126
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.