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Prediction of Compaction Characteristics of Soils from Index Test’s Results

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Abstract

This paper presents some attempts at prediction of compaction characteristics of soils using the results of the index tests. A data bank, including 728 compaction tests, was compiled. Each case includes the results of soil type, grain size distribution, Atterberg limits (WL and WP) and specific gravity of soil particles, as well as the compaction characteristics, maximum dry density and optimum moisture content were calculated under different levels of compaction energy. Using artificial neural networks (ANNs) and multi-linear regression (MLR), the applicability of basic information about soils to estimate the compaction characteristics was evaluated. A sensitivity analysis accomplished on the results of ANN method, demonstrated that fine content has the most pronounced effect on the accuracy of compaction characteristics prediction. Using a trial and error approach and combining the different individual variables, the efficiency of multi-linear regression models were improved. However, the comparisons showed that ANN models are more effective in capturing the correlation among compaction characteristics of soils and their index properties, while the ANN shortcomings, due to their black box nature, make MLR models more useful in prompt estimations.

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References

  • Agus SS (2005) An experimental study on hydro-mechanical characteristics of compacted bentonite-sand mixtures. Ph.D. thesis, Bauhaus-University Weimar, Germany

  • Ahangar-Asr A, Faramarzi A, Mottaghifard N, Javadi AA (2011) Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Comput Geosci 37(11):1860–1869

    Article  Google Scholar 

  • Arifin YF (2008) Thermo-hydro-mechanical behavior of compacted bentonite-sand mixtures: an experimental study. Ph.D. thesis, Bauhaus-University Weimar, Germany

  • Baxter CW, Zhang Q, Stanley SJ, Shariff R, Tupas R-RT, Shark HL (2001) Drinking water quality and treatment: the use of artificial neural networks. Can J Civ Eng 28(Suppl. 1):26–35

    Article  Google Scholar 

  • Benson CH, Trast JM (1995) Hydraulic conductivity of thirteen compacted clays. Clays Clay Miner 43(6):669–681

    Article  Google Scholar 

  • Benson C, Daniel D, Boutwell G (1999) Field performance of compacted clay liners. J Geotech Geoenviron Eng ASCE 25(5):390–403

    Article  Google Scholar 

  • Carrier D (2003) Goodbye, hazen; hello, kozeny-carman. J Geotech Geoenviron Eng 129(11):1054–1056

    Article  Google Scholar 

  • Daniel DE, Benson CH (1990) Water content-density criteria for compacted soil liners. J Geotech Eng 116(12):1811–1830

    Article  Google Scholar 

  • Daniel DE, Wu YK (1993) Compacted clay liners and covers for arid sites. J Geotech Eng 119(2):223–237

    Article  Google Scholar 

  • Davidson DT, Gardiner WF (1949) Calculation of standard proctor density and optimum moisture content from mechanical analysis, shrinkage factors, and plasticity index. In: Proceedings of the HRB29, pp 447–481

  • Du YJ, Shen SL, Liu SY, Hayashi S (2009) Contaminant mitigating performance of Chinese standard municipal solid waste landfill liner systems. J Geotext Geomembr 27(3):232–239

    Article  Google Scholar 

  • Falamaki A (2013) Artificial neural network application for predicting soil distribution coefficient of nickel. J Environ Radioact 115:6–12

    Article  Google Scholar 

  • Falamaki A, Shahin S (2018) Determination of shear strength parameters of municipal solid waste from its physical properties. Iran J Sci Technol Trans Civ Eng. https://doi.org/10.1007/s40996-018-0158-4

    Google Scholar 

  • Fausett LV (1994) Fundamentals neural networks: architecture, algorithms, and applications. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Gao LX, Luan MT, Yang Q (2008) Experimental study on permeability of unsaturated remolded clay. Electron J Geotech Eng 13(Bund. D):1–15

    Google Scholar 

  • Giustolisi O, Savic DA (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinf 8(3):207–222

    Article  Google Scholar 

  • Günaydın O (2009) Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. J Environ Geol 57(1):203–215

    Article  Google Scholar 

  • Gutrug Y, Sridharan A (2004) Compaction behavior and prediction of its characteristics of fine grained soils with with particular references to compaction energy. J Soils Found 44(5):27–36

    Article  Google Scholar 

  • HMSO (1957) Soil mechanics for engineers. Her Majesty’s Stationary Office, London

    Google Scholar 

  • Holtz RD, Kovacs WD (1981) An introduction to geotechnical engineering. Prentice-Hall Inc, Eaglewood Cliffs

    Google Scholar 

  • Horpibulsuk S, Katkan W, Appichatvulop A (2008) An approach for assessment of compaction curves of fine grained soils at various energies using a point test. J Soils Found 48(1):115–125

    Article  Google Scholar 

  • Hossein Alavi A, Hossein Gandomi A, Mollahassani A, Akbar Heshmati A, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379

    Article  Google Scholar 

  • Inci G, Yesiller N, Kagawa T (2003) Experimental investigation of dynamic response of compacted clayey soils. Geotech Test J 26(2):1–17

    Google Scholar 

  • Jamshidi Chenari R, Tizpa P, Ghorbani Rad MR, Machado SL, Karimpour Fard M (2015) The use of index parameters to predict soil geotechnical properties. Arab J Geosci 8(7):4907–4919

    Article  Google Scholar 

  • Jeng YS, Strohm WE (1976) Prediction of the shear strength and compaction characteristics of compacted fine-grained cohesive soils. Final report, U.S. Army Engineer Water Ways Experiment Station. Soils and Pavement Laboratory, Vicksburg, MO, USA

  • Jesmani M, Manesh AN, Hoseini SMR (2008) Optimum water content and maximum dry unit weight of clayey gravels at different compactive efforts. Electron J Geotech Eng 13:1–14

    Google Scholar 

  • Jumikis AR (1946) Geology and soils of the Newark metropolitan area. J Soil Mech Found Div ASCE 93(SM2):71–95

    Google Scholar 

  • Kofiatis GP, Manikopoulos CN (1982) Correlation of maximum dry density and grain size. J Geotech Eng Div ASCE 108(GT9):1171–1176

    Google Scholar 

  • Lambe TW, Whitman RV (1969) Soil mechanics. Wiley, New York

    Google Scholar 

  • Linveh M, Ishai I (1978) Using indicative properties to predict the density–moisture relationship of soil. Transp Res Rec 60P:22–28

    Google Scholar 

  • Loehle C (1997) A hypothesis testing frame work for evaluating eco system model performance. Ecol Model 97:153–165

    Article  Google Scholar 

  • McRae JL (1958) Index of compaction characteristics. In: Symposium on application of soil testing in highway design and construction. ASTM Special Technical Publication, No. 239, pp 119–127

  • Miller CJ, Yesiller N, Yaldo N, Merayyan S (2002) Impact of soil type and compaction conditions on soil water characteristic. J Geotech Geoenviron Eng 128(9):733–742

    Article  Google Scholar 

  • Nagaraj TS (1994) Analysis and prediction of compaction characteristics of soils. (Unpublished)

  • Najjar YM, Ali HE (1998) CPT-based liquefaction potential assessment: a neuronet approach, vol 1. ASCE Geotechnical Special Publication, Reston, pp 542–553

    Google Scholar 

  • Najjar YM, Basheer IA, Naouss WA (1996) On the identification of compaction characteristics by neuronets. Comput Geotech 18(3):167–187

    Article  Google Scholar 

  • Olsen HW (1962) Hydraulic flow through saturated clays. Clays Clay Miner 9:131–161

    Article  Google Scholar 

  • Omar E, Shanbleh A, Basma A, Barkat S (2003) Compaction characteristics of granular soils in United Arab Emirates. J Geotech Geol Eng 21:283–295

    Article  Google Scholar 

  • Osinubi KJ, Bello AA (2011) Soil-water characteristics curves for reddish brown tropical soil. Electron J Geotech Eng 16(Bund. A):1–25

    Google Scholar 

  • Osinubi KJ, Nwaiwu CMO (2005) Hydraulic conductivity of compacted lateritic soil. J Geotech Geoenviron Eng 131(8):1034–1041

    Article  Google Scholar 

  • Ramiah BK, Viswanath V, Krishnamurthy HV (1970) Interrelationship of compaction and index properties. In: Proceedings of the second south east Asian conference on soil engineering, Singapore, pp 577–587

  • Rezania M, Javadi AA, Giustolisi O (2008) An evolutionary-based data mining technique for assessment of civil engineering systems. J Eng Comput 25(6):500–517

    Article  MATH  Google Scholar 

  • Ring GW, Sallgerb JR, Collins WH (1962) Correlation of compaction and classification test data. HRB Bull 325:55–75

    Google Scholar 

  • Rowan HW, Graham WW (1948) Proper compaction eliminates curing period in construction fills. Civ Eng 18:450–451

    Google Scholar 

  • Shahin MA (2013) Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. In: Metaheuristics in water, geotechnical and transport engineering, pp 169–204

  • Shahin M, Jaksa M, Maier H (2008) State of the art of artificial neural networks in geotechnical engineering. Electron J Geotech Eng Bouquest08:1–26

    Google Scholar 

  • Shahnazari H, Tutunchian MA, Mashayekhi M, Amini AA (2012) Application of soft computing for prediction of pavement condition index. J Transp Eng 138(12):1495–1506

    Article  Google Scholar 

  • Shahnazari H, Tutunchian MA, Rezvani R, Valizadeh F (2013) Evolutionary-based approaches for determining the deviatoric stress of calcareous sands. Comput Geosci 50:84–94

    Article  Google Scholar 

  • Shahnazari H, Shahin MA, Tutunchian MA (2014) Evolutionary based approaches for settlement prediction of shallow foundations on cohesionless soils. Int J Civ Eng 12(1):55–64

    Google Scholar 

  • Shariatmadari N, Eslami A, Karimpour-Fard M (2007) Bearing capacity of driven piles in sands from SPT-applied to 60 case histories. Iran J Sci Technol 17:35–58

    Google Scholar 

  • Shariatmadari N, Machado SL, Noorzad A, Karimpour-Fard M (2009) Municipal solid waste effective stress analysis. J Waste Manag 29(12):2918–2930

    Article  Google Scholar 

  • Shirazi SM, Kazama H, Salman FA, Othman F, Akib S (2010) Permeability and swelling characteristics of bentonite. Int J Phys Sci 5(11):1647–1659

    Google Scholar 

  • Sinha SK, Wang MC (2008) Artificial neural network prediction models for soil compaction and permeability. Geotech Geol Eng J 26(1):47–64

    Article  Google Scholar 

  • Sivakugan N, Eckersley JD, Li H (1998) Settlement predictions using neural networks. Aust Civ Eng Trans CE 40:49–52

    Google Scholar 

  • Sivrikaya O (2008) Models of compacted fine-grained soils used as mineral liner for solid waste. J Environ Geol 53:1585–1595

    Article  Google Scholar 

  • Sivrikaya O, Ölmez A (2007) Correlations between compaction parameters and index properties of soils. In: Second Turkish geotechnics symposium, 22–23 Nov, Adana, pp 47–60. (in Turkish)

  • Sivrikaya O, Soycan TY (2011) Estimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks. Int J Numer Anal Meth Geomech 35(17):1830–1841

    Article  Google Scholar 

  • Sridharan A, Nagaraj HB (2005) Plastic limit and compaction characteristics of fine grained soils. Gr Improv 9(1):17–22

    Article  Google Scholar 

  • Taha MR, Kabir MH (2005) Tropical residual soil as compacted soil liners. Environ Geol 2005(47):375–381

    Article  Google Scholar 

  • Tenpe A, Kaur S (2015) Artificial neural network modeling for predicting compaction parameters based on index properties of soil. Int J Sci Res (IJSR) 4(7):1198–1202

    Google Scholar 

  • Tizpa P, Jamshidi Chenari R, Karimpour Fard M, Lemos Machado S (2015) ANN prediction of some geotechnical properties of soil from their index parameters. Arab J Geosci 8(5):2911–2920

    Article  Google Scholar 

  • Turnbull JM (1948) Computation of the optimum moisture content in the moisture density relationship of soils. In: Proceedings of the 2nd international conference on soil mechanics and foundation engineering, Rotterdam, Holland, pp 256–262

  • Ural DN, Saka H (1998) Liquefaction assessment by neural networks. Electron J Geotech Eng. http://www.ejge.com/Ppr9803/Ppr9803.htm

  • Waller LA, Smith D, Childs JE, Real LA (2003) Monte Carlo assessments of goodness-of-fit for ecological simulation models. Ecol Model 164(2003):49–63

    Article  Google Scholar 

  • Wang MC, Huang CC (1984) Soil compaction and permeability prediction models. J Environ Eng ASCE 110(6):1063–1083

    Article  Google Scholar 

  • Zarei Gh, Homaee M, Liaghat A (2008) Modeling transient evaporation from descending shallow ground water table based on Brooks–Corey retention function. Water Resour Manag 23:2867–2876

    Article  Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company, St. Paul

    Google Scholar 

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Karimpour-Fard, M., Machado, S.L., Falamaki, A. et al. Prediction of Compaction Characteristics of Soils from Index Test’s Results. Iran J Sci Technol Trans Civ Eng 43 (Suppl 1), 231–248 (2019). https://doi.org/10.1007/s40996-018-0161-9

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  • DOI: https://doi.org/10.1007/s40996-018-0161-9

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