Open Access Open Access  Restricted Access Subscription or Fee Access

Soft Computing Based Prediction of Deviator Stress of Waste Plastic Reinforced Sand

Rakesh Kumar Dutta, Viswas Nandakishor Khatri, Tammineni Gnananandarao

Abstract


In the recent past, the soft computing techniques have received a significant attention for solution of the geotechnical stability problems. Following the trend, the present study tries to explore the use of different soft computing techniques such as  random forest regression, support vector machines (SVM) RBF kernel, SVM poly kernel and M5P model tree for the prediction of deviator stress of sand reinforced with waste plastic strips. The deviator stress (s) was assumed to be dependent on strip content (SC), strip elongation at failure (ets), aspect ratio (AR) of strip, confining pressure (CP), and strain at failure of the composite specimen (e). The performance of each soft computing technique was assessed with the help of various standard statistical parameters. The findings of the present study revealed that the SVM RBF kernel approach was quick and accurate tool for predicting the deviator stress of sand reinforced with waste plastic strips. Further the sensitivity analysis conducted for the SVM RBF kernel indicated that the strip content (SC) affected the prediction of deviator stress of sand quite significantly.


Full Text:

PDF

References


C. H. Benson and M. U. Khire,“Reinforcing sand with strips of reclaimed high-density polyethylene.” J Geotech Eng, 121(4), 838–855, 1994.

R.K. Dutta and G.V. Rao, “Engineering properties of sand reinforced with strips from waste plastic.” Proceedings international conference on geotechnical engineering, Sharjah, 3-6 Oct, 186-193, 2004.

R.K. Dutta, K. Dutta and S. Jeevanandham, “Prediction of deviator stress of sand reinforced with waste plastic strips using neural network.” Int. J. of Geosynth. and Ground Eng., 1(2), 1-12, 2015, DOI 10.1007/s40891-015-0013-7

T. Gnananandarao, R.K. Dutta, and V.N. Khatri, “Artificial neural networks based bearing capacity prediction for square footing resting on confined sand.” Indian Geotechnical Conference, 14-16 December, IIT Guwahati, Assam, India, 2017.

T. Gnananandarao, R.K. Dutta and V.N. Khatri, “Application of artificial neural network to predict the settlement of shallow foundations on cohesionless soils.” Indian Geotechnical Conference, 15-17 December, IIT Madras, Chennai, India, 2016.

M. Pal and S. Deswal, “Modeling pile capacity using support vector machines and generalized regression neural network.” Journal of Geotechnical and Geoenvironmental Engineering, 134(7), 1021-1024, 2008, doi: 10.1061/(ASCE)1090-0241(2008)134:7(1021)

P. Samui, “Support vector machine applied to settlement of shallow foundations on cohesionless soils.” Computers and Geotechnics, 35(3), 419–427, 2008, 10.1016/j.compgeo.2007.06.014

doi: 10.1016/j.compgeo.2007.06.014

N. Puri, H.D. Prasad, and A. Jain, “Prediction of geotechnical properties using machine learning techniques.” Procedia Computer Science, 125, 509–517, 2018, 10.1016/j.procs.2017.12.066

V.N. Vapnik, “The Nature of Statistical Learning Theory.” Springer-Verlag, New York, 1995.

A.J. Smola, “Regression estimation with support vector learning machines.” Master’s Thesis, Technische Universitat M ¨ unchen, Germany 1996.

L. Breiman, “Random forests - Random Features.” Technical Report 567, Statistics Department, University of California, Berkeley, 1999.

L. Breiman, “Bagging predictors.” Machine Learning, 24(2), 123–140, 1996.

J.R. Quinlan, “Learning with continuous classes.” In: Adams A, Sterling L (eds) Proceedings of the 5th Australian joint conference on artificial intelligence. World Scientific, Singapore, 343–348, 1992.

M. Pal, and P.M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sensing of Environment, 86(4), 554–565, 2003.

W. Feller, “An introduction to probability theory and its application.” 1(3), Wiley, New York, 1968.

S. Bochkanov and V. Bystritsky, “ALGLIB, www.alglib.net”. (July 5, 2012).

D.P. Palmer, N.M. O’Boyle, R.C. Glen, and J.B.O. Mitchell, “Random forest models to predict aqueous solubility.” Journal of Chemical Information and Modeling, 47(1), 150–158, 2007.

B. Singh, P., Sihag and K. Singh, “Modelling of impact of water quality on infiltration rate of soil by random forest regression.” Model. Earth Syst. Environ, 3(3), 999-1004, 2017. doi: 10.1007/s40808-017-0347-3




DOI: https://doi.org/10.3759/joge.v6i3.2751

Refbacks

  • There are currently no refbacks.