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Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

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Abstract

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.

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References

  1. Hazen A (1892) Some physical properties of sands and gravels. Massachusetts State Board of Health Annual Report, 539–556

  2. Kenny TC, Lau D, Ofoegbu GI (1984) Permeability of compacted granular materials. Can Geotech J 21:726–729

    Article  Google Scholar 

  3. Cedergren HR (1988) Seepage drainage and flow nets, 3rd edn. Wiley, Hoboken

    Google Scholar 

  4. D’Appolonia DJ (1980) Soil-bentonite slurry trench cutoffs. J Geotech Geoenviron Eng ASCE 106(4):399–417

    Google Scholar 

  5. Daniel D (1987) Earthen liners for land disposal facilities. Geotechnical practice for waste disposal ‘87, vol 13. ASCE, New York, pp 21–39

    Google Scholar 

  6. Kenney TC, Van Veen WA, Swallow MA, Sungaila MA (1992) Hydraulic conductivity of compacted bentonite-sand mixtures. Can Geotech J 29(3):364–374

    Article  Google Scholar 

  7. Ryan CR (1987) Vertical barriers in soil for pollution containment. Geotechnical practice for waste disposal, vol 13. ASCE, New York, pp 182–204

    Google Scholar 

  8. Shelley TL, Daniel DE (1993) Effect of gravel on hydraulic conductivity of compacted soil liners. J Geotech Eng 119(1):54–68

    Article  Google Scholar 

  9. Shakoor A, Cook BD (1990) The effects of stone content, size, and shape on the engineering properties of a compacted silty clay. Bull Assoc Eng Geol 27:245–253

    Google Scholar 

  10. Shafiee A (2008) Permeability of compacted granule-clay mixtures. Eng Geol 97(11):199–208

    Article  Google Scholar 

  11. Lambe TW, Whitman RV (2008) Soil mechanics. SI version. Wiley India Pvt. Limited, Hoboken

    Google Scholar 

  12. Mesri G, Olson RE (1971) Mechanism controlling the permeability of clays. Clay Clay Miner 19:151–158

    Article  Google Scholar 

  13. Mitchell JK, Soga K (2005) Fundamentals of soil behavior. Wiley, Hoboken

    Google Scholar 

  14. Cary AS, Walter BH, Harstad HT (1943) Permeability of mud mountain dam core material. Trans Am Soc Civ Eng 108:719–728

    Google Scholar 

  15. Acar YB, Olivieri I (1989) Pore fluid effects on the fabric and hydraulic conductivity of laboratory-compacted clay. Transport Res Record 1219:144–159

    Google Scholar 

  16. Cronican AE, Gribb MM (2004) Hydraulic conductivity prediction for sandy soils. Supplement entitled: equations for predicting hydraulic conductivity based on grain-size data. Ground Water 42:459–464

    Article  Google Scholar 

  17. Amer AM, Awad AA (1974) Permeability of cohesionless soils. J Geotech Eng Div ASCE 100(GT12):1309–1316

    Google Scholar 

  18. Chapuis RP, Gill DE, Baass K (1989) Laboratory permeability tests on sand: influence of the compaction method on anisotropy. Can Geotech J 26:614–622

    Article  Google Scholar 

  19. Odong J (2007) Evaluation of empirical formulae for determination of hydraulic conductivity based on grain-size analysis. J Am Sci 3(3):54–60

    Google Scholar 

  20. Alyamani MS, Sen Z (1993) Determination of hydraulic conductivity from complete grain-size distribution curves. Ground Water 31(4):551–555

    Article  Google Scholar 

  21. Sperry JM, Peirce JJ (1995) A model for estimating the hydraulic conductivity of granular material based on grain shape, grain size, and porosity. Ground Water 33(6):892–898

    Article  Google Scholar 

  22. Carrier DW (2003) Goodbye, hazen; hello, kozaney-carman. J Geotech Geoenviron Eng ASCE 129(11):1054–1056

    Article  Google Scholar 

  23. Chapuis RP (2004) Predicting the saturated hydraulic conductivity of sand and gravel using effective diameter and void ratio. Can Geotech J 41(11):787–795

    Article  Google Scholar 

  24. Kresic N (2007) Quantitative solutions in hydrogeology and groundwater modeling, 2nd edn. CRC Press, Taylor & Francis Group, Boca Raton

    Google Scholar 

  25. Raju PSRN, Pandian NS, Nagaraj TS (1995) Analysis and estimation of coefficient of consolidation. Geotech Test J 18(2):252–258

    Article  Google Scholar 

  26. Chapuis RP (2012) Predicting the saturated hydraulic conductivity of soils: a review. Bull Eng Geol Environ 71(3):401–434

    Article  Google Scholar 

  27. Taylor DW (2013) Fundamentals of soil mechanics. Literary Licensing LLC, Whitefish

    Google Scholar 

  28. Jang JR (1993) anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 33(3):2889–2892

    Google Scholar 

  29. Najjar YM, Basheer IA (1996) Utilizing computational neural networks for evaluating the permeability of compacted clay liners. Geotech Geol Eng 14(3):193–212

    Google Scholar 

  30. Boroumand A, Baziar MH (2005) Determinations of compacted clay permeability by artificial neural networks. Proceedings of the ninth international water technology conference, Sharm El-Sheikh, March 17–20, pp 515–526

  31. Sinha SK, Wang MC (2008) Artificial neural network prediction models for soil compaction and permeability. Geotech Geol Eng 26:47–64

    Article  Google Scholar 

  32. Yilmaz I, Marschalko M, Bednarik M, Kaynar O, Fojtova L (2012) Neural computing models for prediction of permeability coefficient of coarse-grained soils. Neural Comput Appl 21:957–968

    Article  Google Scholar 

  33. Arshad RR, Sayyad G, Mosaddeghi M, Gharabaghi B (2013) Predicting saturated hydraulic conductivity by artificial intelligence and regression models. ISRN Soil Sci, Article ID 308159, 8

  34. Zanganeh M, Mousavi SJ, Etemad Shahidi AF (2009) A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng Appl Artif Intell 22(8):1194–1202

    Article  Google Scholar 

  35. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  36. Mousavi SJ, Ponnambalam K, Karray F (2007) Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets Syst 158(10):1064–1082

    Article  MathSciNet  MATH  Google Scholar 

  37. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Berlin

    Book  MATH  Google Scholar 

  38. Demuth H, Beale M, Hagan M (2008) Neural network \({\rm toolbox}^{{\rm TM}}\) 6, user’s guide. The MathWorks, Inc, Natick

    Google Scholar 

  39. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314

    Article  MathSciNet  MATH  Google Scholar 

  40. Hornik KM, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximations. Neural Netw 2(5):359–366

    Article  Google Scholar 

  41. Hecht-Nielson R (1987) Kolmogorov’s mapping neural network existence theorem. 1st IEEE ICNN, 3, San Diago

  42. Kůrková V (1992) Kolmogorov’s theorem and multilayer neural networks. Neural Netw 5(3):501–506

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Hadi Ganjidoost, S. Jamshid Mousavi & Abbas Soroush

  2. Department of Civil and Environmental Engineering, University of Waterloo, Ontario, Canada

    Hadi Ganjidoost

Authors
  1. Hadi Ganjidoost
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  2. S. Jamshid Mousavi
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  3. Abbas Soroush
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Correspondence to S. Jamshid Mousavi.

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Ganjidoost, H., Mousavi, S.J. & Soroush, A. Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient. Neural Process Lett 44, 53–79 (2016). https://doi.org/10.1007/s11063-015-9479-5

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  • DOI: https://doi.org/10.1007/s11063-015-9479-5

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