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Leakage Rate Prediction through Composite Liner due to Geomembrane Defect using Neural Network

Rakesh Kumar Dutta, Suman Kumari

Abstract


The paper presents the leakage rate prediction using artificial neural network from the liner made of soil and geomembrane. The defect in the geomembrane considered was having different shapes. Three different shapes such as square, rectangular and circular of the defect have been considered. The input variables considered for the artificial neural network (ANN) were (i) head on the top of the soil (ii) area of the defect (iii) hydraulic conductivity of the soil (iv) thickness of soil and the leakage rate or the discharge was the output. The training of the ANN model was carried out and the weights were obtained. These weights were used to describe the relationship between the input variables and output leakage rate. The study further presented the sensitivity analysis and the variables affecting the leakage rate were identified. Finally, the study concluded that the prediction accuracy using ANN in predicting the leakage rate was quite good.


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References


J.P. Giroud, K. Badu-Tweneboah and R. Bonaparte, ‘‘Rate of leakage through a composite liner due to geomembrane defects,’’Geotextiles and Geomembranes, 11(1), 1–28, 1992.

J. P. Giroud, and R. Bonaparte, ‘‘Leakage through liners constructed with geomembranes— Parts II. Composite liners,’’Geotextiles and Geomembranes, 8(2), 71-111, 1989.

J. P. Giroud, M.V. Khire, and K.L. Soderman, ‘‘Liquid migration through defects in a geomembrane overlain and underlain by permeable media,’’Geosynthetics International, 4(3-4), 293-321, 1997.

C.T. Weber, and J.G. Zornberg,”Leakage through liners under high hydraulic heads," Geosynthetics Research and Development in Progress, Eighteenth Geosynthetic Research Institute Conference (GRI-18), Austin, Texas, January 26 (CD-ROM), 2005.

R.K. Rowe, ‘‘Geosynthetics and the minimization of contaminant migration through barrier systems beneath solid waste,’’ Proc., 6th Int. Conf. on Geosynthetics, International Geosynthetics Society, Minneapolis, 27–103, 1998.

N Touze-Foltz, R.K. Rowe, and C. Duquennoi, ‘‘Liquid flow through composite liners due to geomembrane defects: Analytical solutions for axi-symmetric and two-dimensional problems,’’Geosynthetics International, 6(6), 455–479, 1999

J. Walton, M. Rahman, D. Casey, M. Picornell and F. Johnson, ‘‘Leakage through flaws in geomembrane liners,’’International Journal of Geotechnical and Geoenvironmental Engineering, 123(6), 534–539, 1997

Y.H. Faure, ‘‘Design of drain beneath geomembranes: discharge estimation and flow patterns in case of leak,’’ Proceedings of the International Conference on Geomembranes, Denver, USA, 6(2), 463-468, 1979

J. C. Walton and Budhi, Sagar, “Aspects of fluid flow through small flaws in membrane liners,”Environmental Science and Technology, 24 (6), 920–924, 1990.

M. Ismeik and O. Al-Rawi 2014 Modeling soil specific surface area with artificial neural networks. Geotechnical Testing Journal. 37(4): 1-11. DOI: 10.1520/GTJ20130146

I. Yilmaz and O. Kaynar 2011 Multiple regressions, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications. 38: 5958-5966.

C Kayadelen 2008 Estimation of effective stress parameter of unsaturated soils by using artificial neural networks. Int. J. Numer. Anal. Meth. Geomech. 32: 1087–1106. DOI: 10.1002/nag.660

S K Das and P K Basudhar 2006 Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics. 33(8): 454-459.

S E Cho 2009 Probabilistic stability analyses of slopes using the ANN-based response surface. Computers and Geotechnics. 36: 787–797. doi:10.1016/j.compgeo.2009.01.003

R K Dutta, K Dutta and S Jeevanandham 2015 Prediction of deviator stress of sand reinforced with waste plastic strips using neural network” Int. J. of Geosynth. and Ground Eng.1(11): 1-12. DOI 10.1007/s40891-015-0013-7

R Nazir, E Momeni, K Marsono and H Maizir. 2015 An artificial neural network approach for prediction of bearing capacity of spread foundations in sand. Jurnal Teknologi. 72(3): 9–14.

A Kalinli, M C Acar, and Z Gunduz 2011 New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology. 117: 29–38. doi:10.1016/j.enggeo.2010.10.002

Y L Kuo, M B Jaksa, A V Lyamin and W S Kaggwa 2009 ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Computers and Geotechnics.36:503–516.

M Ornek 2013 Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils. Neural Comput. & Applic. DOI 10.1007/s00521-013-1444-5

A Soleimanbeigi and N Hataf 2005 Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks. Geosynthetics International. 12(6): 321-332.

M Ornek, M Lamanb, A Demir and A Yildiz 2012 Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils and Foundations, 52(1): 69–80. doi:10.1016/j.sandf.2012.01.002

M A Shahin, B J Mark, R M Holger. “State of the art of artificial neural networks in geotechnical engineering”. EJGE, Bouquet, 2008.

K. Hornik, M. Stinchcombe, H. White. Multilayer feed-forward networks are universal approximators. Neural Networks , 2, 359-366, 1989.

L M Salchenberger, E M Cinar, N A Lash. “Neural networks a new tool for predicting thrift failures”. Decision Science , 23, 899-916, 1992.

L Berke, P Hajela. “Application of neural networks in structural optimization”.NATO/AGARD Advanced Study Institute, 23(I-II), 731-45, 1991.

S. E. Yasodian, R. K. Dutta, N. P. Nisha and S. Salila, “Quantification of discharge through the composite liner due to geomembrane defect,” International Journal of Geotechnics and Environment, 3(1), 17-34, 2011.

Z. Boger, H. Guterman. “Knowledge extraction from artificial neural network models”. IEEE International Conference on Computational Cybernetics and Simulation, 4, 3030 – 3035, 1997.

D. Hammerstrom. "Working with neural networks." IEEE Spectrum, 30(7),46-53, 1993.

J.D. Olden and D.A. Jackson, “Illuminating the black box a randomization approach for understanding variable contributions in artificial neural networks,” Ecological Modeling, 154 (1-2), 135-150, 2002.




DOI: https://doi.org/10.3759/joge.v6i3.3375

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