Application of Soft Computing Techniques in Modelling of Soaked and Unsoaked California Bearing Ratio
Asian Soil Research Journal,
In this study, author attempted to establish a correlation between soil physical parameters and California Bearing Ratio of lateritic soils using advanced mathematical techniques such as the Support Vector Machine (SVM), Random Forest (RF), M5 tree, multiple linear regression, and Artificial Neural Network. A total of 480 soil samples were collected and separated into a data set using training and validation of the generated models based on the main soil parameters of Liquid Limit (LL), Plastic Limit (PL), Natural moisture content (NMC), Specific gravity (GS), Fines (F), Gravel, and Sand. The Principal Component Analysis (PCA) was used to minimize the dataset's huge dimension, and the approximate sum of the first four principal components (PC) captured 88 percent of the variability in the response variable with just 12% information loss. The RMSE values of 21.6, 21.23, 295.67, 7.03, 14.54 and 24.43,24.59,326.49,8.63,17.71 are from the MLR, ANN, MS Tree, RF, and SVM models for SCBR and USCBR values, respectively. For SCBR and USCBR, random forest (RF) yielded the lowest values of 7.03 and 8.63, respectively. Similarly, the R values range from 0.1 to 0.94 and 0.01 to 0.92, indicating that the anticipated and real SCBR and USCBR are related. The Random Forest Model for SCBR and USCBR was shown to be the best by the correlation coefficient values, while the MS tree model for SCBR and USCBR was determined to have the lowest coefficient of determination R2. As a result, it can be concluded that Random Forest provided the best Soaked and Unsoaked CBR model based on the dataset, while MS tree provided the poorest model. The model is a valuable tool for evaluating the subsurface indices of a civil engineering site at the preliminary planning stage before final structural design for the substructures, as the anticipated soil parameter values are within permitted accuracy.
- Compaction characteristics
- california bearing ratio
How to Cite
Srinivasa R, Ruchita Pankaj S, Sathiraju VS. Prediction of California Bearing Ratio through Empirical Correlations of Index Properties for Tropical Indian Soils. International Journal of Innovations in Engineering and Technology. 2019;15(1).
Datta T, Chottopadhyay BC. Correlation Between CBR And Index Properties Of Soil, Proceedings of Indian Geotechnical Conference, Kochi (Paper No. A-350); 2011.
Ayodele AL, Falade FA, Ogedengbe MO. Effect of fines content on some engineering properties of lateritic soil in Ile- Ife, Department of Civil Engineering, Obafemi Awolowo University, Ile-Ife; 2009.
Dharamveer S, Reddy KS, Laxmikant Y. Moisture and Compaction Based Statistical Model for Estimating CBR of Fine-Grained Subgrade Soils, International Journal of Earth Sciences and Engineering. 2011;04(06):100-103.
Rahim, AM. Subgrade soil index properties to estimate resilient modulus for pavement design International Journal of Pavement Engineering. 2005;6(3):163-169.
Vinod P, Reena C. Prediction of CBR value of lateritic soils using liquid limit and gradation characteristics data. Highw Res J IRC. 2008;1(1):89–98.
Patel RS, Desai MD. CBR predicted by index properties for alluvial soils of South Gujarat. In: Proceedings of the Indian geotechnical conference, Mumbai. 2010;79–82.
Magdi ME. Zumrawi. Prediction of CBR Value from Index Properties of Cohesive Soils, Annual Conference of Postgraduate Studies and Scientific Research, Hall, Khartoum, Proceeding. 2012;1:111- 117.
Valentine YK. Souleyman MoupeMfoyet. Bertille Manefouet. Armand Sylvain Ludovic Wouatong . Lawrence Aleh; 2017.
AASHTO. “Standard Specifications for Transportation, Material and Method of Abu-Kiefa M. A. 1998; 1986.
Osuji OS, Akinwamide JT. Physico-Chemical Properties of Lateritic Soils in Ado-Ekiti, South Western Nigeria. Universal Journal of Environmental Research and Technology. 2018;7(1):10-18
Winterkorn HF, Chandrashekharan EC. Laterite soils and their stabilization. Highway Research Board Bull. 1951;44:10-29.
Terzaghi K. Design and performance of Sasumua Dam. Inst. Civil Engineers Proc. 9:369-395.67. Topping, J. 1957. Errors of observation and their treatment; 1958.
Gidigasu MD. The importance of soil genesis in the engineering classification of Ghana soils. Eng. Geol. 1971;5:117- 161.
Akinola OO, Obasi RA. Compositional Characteristics and Industrial Potential of The Lateritic Clay Deposit In Ara-IjeroEkiti Areas, Southwestern Nigeria. International Journal of Scientific & Technology Research. 2014;3(8):305 -31.
ASTM D. Standard Practice for Description and Identification of soils (Visual -Manual Procedure),’ American Society for Testing and Materials; 2000.
Shahin MA, Jaksa MB, Maier HR. State of the art of artificial neural networks in geotechnical engineering, Electronic Journal of Geotechnical Engineering. 2008;8:1-26.
Smith GN. Probability and statistics in civil engineering: an introduction. London: Collins; 1986.
Abstract View: 123 times
PDF Download: 26 times