Prediction of CBR Value of Fine Grained Soils of Bengal Basin by Genetic Expression Programming, Artificial Neural Network and Krigging Method
Authors
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Civil Engineering Department, Aliah University, Kolkata - 700156
Sk Kamrul Alam -
Civil Engineering Department, Aliah University, Kolkata - 700156
Abhijit Mondal
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Civil Engineering Department, Jadavpur University, Kolkata - 700032
Amit Shiuly
DOI:
https://doi.org/10.1007/s12594-020-1409-0Keywords:
No Keywords.Abstract
For designing of pavements, California Bearing Ratio (CBR) value is an important parameter which is used to determine the strength of the subgrade soils. However, it is to be mentioned that, CBR test is tedious and laborious. Thus, in the present paper an attempt has been made to develop relationships between CBR and various soil index properties such as specific gravity (G), coefficient of uniformity (Cu), coefficient of curvature (Cc), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC) and maximum dry density (MDD) for alluvial soil in West Bengal, India. Empirical relationships have been proposed for both soaked and un-soaked CBR values as a function of these soil index properties by Genetic Expression Programing (GEP). Further, the same index properties have been used to predict CBR values by artificial neural network (ANN) and krigging method. The results clearly reveals that the GEP and ANN and krigging methods can be successfully used for predicting both the soaked and un-soaked CBR values by using the index properties of soil. Moreover, the developed relationships have been compared with the past available relationships. Furthermore, a multi objective optimization has been carried out for getting maximum CBR values.
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Agarwal, K.B. and Ghanekar, K.D. (1970) Prediction of CBR from plasticity characteristics of soil. South-east Asian conference on soil engineering, pp.571-576.
Chandrakar, V. and Yadav, R. K. (2016) Study of correlation of CBR value with engineering properties and index properties of coarse grained soil. Internat. Res. Jour. Engg. Tech (IRJET), v.3(11), pp.772-778.
Das, R., Wason, H.R. and Sharma, M. (2013) General Orthogonal Regression relation between body and moment magnitudes. Seismological Res. Lett., v.84(2), pp.219-224.
Das, R., Wason, H.R. and Sharma, M. (2011) Global regression relations for conversion of surface wave and body wave magnitudes to moment magnitude. Natural Hazards, v.59(2), pp.801-810.
Das, R., Wason, H.R. and Sharma, M. (2014) Unbiased Estimation of Moment from Body and Surface Wave Magnitude. Bull. Seismol. Soc. Amer., v.104(4), pp.1802-1811.
Das, R., Wason, H. R., Chowdhury, D. and Gabriel, G. (2019) A seismic moment scale. Bull. Seismol. Soc. Amer., v.109(4), pp.1542-1555.
Das, R., Wason, H., Gonzalez, G., Sharma, M., Chodhury, D., Roy, N. and Salazar, P. (2018) Earthquake Magnitude Conversion. Bull. Seismol. Soc. Amer., v.108(4), pp.1995-2007.
Farias, I., William, A. and Gaby, R. (2018) Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models. Geotech. Geol. Engg., v.36(6), pp.3485-3498.
Gunaydin, O., Gokoglu, A. and Fener, M. (2010) Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks. Adv. Engg. Software, v.41, pp.1115-1123.
Gurtug, Y. and Sridharan, A. (2002) Prediction of compaction characteristics of fine-grained soils. Géotechnique, v.52(10), pp.761-763.
Harini, H. and Nagesh, S. (2014) Predicting CBR of fine-grained soils by artificial neural network and multiplelinear regression. Internat. Jour. Civil Engg. Tech. (IJCIET), v.5(2), pp.119-126.
Javadi, A.A., Rezania, M. and Nezhad, M.M. (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Computers and Geotechnics, v.33(4), pp.222-233.
Johari, A., Habibagahi, G., & Ghahramani, A. (2006) Prediction of soil-water characteristic curve using genetic programming. Jour. Geotech. Geoenviron. Engg., v.132(5), pp.661-665.
Kalyan, K., Soumya, B., Saibal, K. and Shiuly, A. (2019) An efficient robust cost optimization procedure for rice husk ash concrete mix. Computers and Concrete, v.23(6), pp.433-444.
Katte, V., Mfoyet, S., Bertille, M., Wouatong.Armand, S. and Bezeng, L. (2019) Correlation of California Bearing Ratio (CBR) Value with Soil Properties of Road Subgrade Soil. Geotech Geol Eng, v.37(1), pp.217-234.
Kaymaz, I. (2005) Application of kriging method to structural reliability. Structural Safety, v.27(2), pp.133-151.
Kumar, K.S., Nanduri, P.M. and Kumar, P.N. (2014). Validation of Predicted California Bearing Ratio Values from Different Correlations. Amer. Jour. Engg. Res. (AJER), v.3(8), pp.344-352.
Lai, S. and Serra, M. (1997). Concrete strength prediction by means of neural network. Construction and Building Materials, v.11(2), pp.93-98.
Lakshmi, S. M., Subramanian, S., Lalithambikhai, M. P., Vela, A. M. and Ashni, M. (2016) Evaluation of soaked and unsoaked CBR values of the soil based on the compaction characteristics. Malaysian Jour. Civil Engg., v.28(2), pp.172-182.
Lee, I.M. and Lee, J.H. (1996) Prediction of pile bearing capacity using artificial neural network. Computer and Geotechnics, v.18(3), pp.189-200.
Lee, S.J., Lee, S.R. and Kim, Y.S. (2003) An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Computer, v.30, pp.489-503.
NCHRP (2001) Guide for mechanistic-empirical design of new and rehabilitated structures. National Cooperative Highway Research Program Transportation Research Board National Research Council, Illinois 61820.
Patel, R. S. and Desai, M. (2010) CBR Predicted by Index Properties for Alluvial Soils of South Gujarat. Indian Geotechnical Conference, pp.7982.
Ramasubbarao, G. V. and Siva Sankar, G. (2013) Predicting Soaked CBR Value of Fine Grained Soils Using Index and. Jordan Jour. Civil Engg., v.7(3), pp.354-360.
Reddy, C.N. and Pavani, K. (2006) Mechnically stabilsed soils-Regression equation for CBR evaluation. Indian Geotechnical Comference-, pp. 731734.
Rehman, Z. U., Khalid, U., Farooq, K. and Mujtaba, H. (2017) Prediction of CBR Value from Index Properties of different Soils. Technical Jour., University of Engineering and Technology (UET) Taxila, Pakistan, v.22(2), pp.18-26.
Sandhya Rani and Nagaraj. (2017) Prediction of CBR Value with Soil Index Properties; Case Study on Yadadri Region. International Jour. Latest Engineering and Management Res., (IJLEMR), v.2(7), pp.9-12.
Shiuly, A. and Roy, N. (2018) A generalized Vs-N correlation using various regression analysis and genetic algorithm. Acta Geodaetica et Geophysica, v.53(3), pp.479-502.
Shiuly, A., Sahu, R.B. and Mandal, S. (2017) Site specific seismic hazard analysis and determination of response spectra of Kolkata for maximum considered earthquake. Jour. Geophys. Engg., v.14(3), pp.466-477.
Shyamal, G., Atin, R. and Subrata, C. (2019) Kriging Metamodeling Based Monte Carlo Simulation for Improved Seismic Fragility Analysis of Structures. Jour. Earthquake Engg., DOI:10.1080/13632469.2019.1570395
Simpson, A.R. and Priest, S.D. (1993). The application of genetic algorithms to optimisation problems in geotechnics. Computers and Geotechnics, v.15(1), pp.1-19.
Sinha, S.K. and Wang, M.C. (2007). Artificial neural network prediction models for soil compaction and permeability. Geotech. Geol. Engg., v.26, pp.47-64.
Soumya, B. and Subrata, C. (2018) An improved robust multi-objective optimization of structure with random parameters. Adv. Struct. Engg., v.21(11), pp.1597-1607.
Talukdar, D.K. (2014) A Study of Correlation Between California Bearing Ratio (CBR) Value With Other Properties of Soil. International Jour. Emerging Tech. Adv. Engg., v.4(1), pp.559-562.
Taskiran, T. (2010) Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Engg. Software, v.41(6), pp.886-892.
Tenpe, A. and Patel, A. (2018) Application of genetic expression programming and artificial neural network for prediction of CBR. Road Materials and Pavement Design, v.19(1), pp.1-18.
Yildrin, B. and Gunaydin, O. (2011) Estimation of California bearing ratio by using soft computing systems. Expert Systems with Applications, v.38, pp.6381-6391.
