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Frontiers of Structural and Civil Engineering

, Volume 12, Issue 3, pp 361–371 | Cite as

A multi-attribute decision making approach of mix design based on experimental soil characterization

  • Amit K. Bera
  • Tanmoy MukhopadhyayEmail author
  • Ponnada J. Mohan
  • Tushar K. Dey
Research Article
  • 76 Downloads

Abstract

The clay mineral composition is one of the major factors that governs the physical properties of silty sand subgrade. Therefore, a thorough knowledge of mineral composition is essential to predict the optimum engineering properties of the soil, which is generally characterized by different indices like maximum dry density (MDD), California bearing ratio (CBR), unconfined compressive strength (UCS) and free swelling index (FSI). In this article, a novel multiattribute decision making (MADM) based approach of mix design has been proposed for silty sand–artificial clay mix to improve the characteristic strength of a soil subgrade. Experimental investigation has been carried out in this study to illustrate the proposed approach of selecting appropriate proportion for the soil mix to optimize all the above mentioned engineering properties simultaneously. The results show that a mix proportion containing approximately 90% silty sand plus 10% bentonite soil is the optimal combination in context to the present study. The proposed methodology for optimal decision making to choose appropriate combination of bentonite and silty sand is general in nature and therefore, it can be extended to other problems of selecting mineral compositions.

Keywords

silty sand bentonite soil soil mix design multi-attribute decision making 

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References

  1. 1.
    Mir B A. Some studies on the effect of fly ash and lime on physical and mechanical properties of expansive clay. International Journal of Civil Engineering, 2015, 13: 203–212Google Scholar
  2. 2.
    Wong L S. Formulation of an optimal mix design of stabilized peat columns with fly ash as a pozzolan. Arabian Journal for Science and Engineering, 2015, 40(4): 1015–1025CrossRefGoogle Scholar
  3. 3.
    Ogata N, Kosaki A, Ueda H, Asano H, Takao H. Execution techniques for high level radioactive waste disposal: IV design and manufacturing procedure of engineered barriers. Journal of Nuclear Fuel Cycle and Environment, 1999, 5(2): 103–121CrossRefGoogle Scholar
  4. 4.
    Komine H, Ogata N. Experimental study on swelling characteristics of sand-bentonite mixture for nuclear waste disposal. Soil and Foundation, 1999, 39(2): 83–97CrossRefGoogle Scholar
  5. 5.
    Komine H, Ogata N. Predicting swelling characteristics of bentonites. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(8): 818–829CrossRefGoogle Scholar
  6. 6.
    Daniel D E. Predicting hydraulic conductivity of clay liners. Journal of Geotechnical Engineering, 1984, 110(2): 285–300CrossRefGoogle Scholar
  7. 7.
    Chapuis R P. Sand-bentonite liners: Predicting permeability from laboratory tests. Canadian Geotechnical Journal, 1990, 27(1): 47–57CrossRefGoogle Scholar
  8. 8.
    Chapuis R P. The 2000 R.M. Hardy lecture: Full-scale hydraulic performance of soil-bentonite and compacted clay liners. Canadian Geotechnical Journal, 2002, 39(2): 417–439Google Scholar
  9. 9.
    Haug M D, Wong L C. Impact of molding water content on hydraulic conductivity of compacted sand-bentonite. Canadian Geotechnical Journal, 1992, 29(2): 253–262CrossRefGoogle Scholar
  10. 10.
    Kenney T C, Van Veen W A, Swallow M A, Sungaila M A. Hydraulic conductivity of compacted bentonite-sand mixtures. Canadian Geotechnical Journal, 1992, 29(3): 364–374CrossRefGoogle Scholar
  11. 11.
    Santucci de Magistris F, Silvestri F, Vinale F. Physical and mechanical properties of compacted silty sand with low bentonite fraction. Canadian Geotechnical Journal, 1998, 35(6): 909–925CrossRefGoogle Scholar
  12. 12.
    Abichou T, Benson C, Edil T. Network model for hydraulic conductivity of sand-bentonite mixtures. Canadian Geotechnical Journal, 2004, 4(4): 698–712CrossRefGoogle Scholar
  13. 13.
    Sivapullaiah P V, Sridharan A, Stalin V K. Hydraulic conductivity of bentonite-sand mixtures. Canadian Geotechnical Journal, 2000, 37(2): 406–413CrossRefGoogle Scholar
  14. 14.
    Kashir M, Yanful E K. Hydraulic conductivity of bentonite permeated with acid mine drainage. Canadian Geotechnical Journal, 2001, 38(5): 1034–1048CrossRefGoogle Scholar
  15. 15.
    Yoon K P, Hwang C L. Multiple Attribute Decision Making, An Introduction. London: Sage Publications, 1995CrossRefGoogle Scholar
  16. 16.
    Kuo M S, Liang G S, Huang W C. Extensions of the multicriteria analysis with pairwise comparison under a fuzzy environment. International Journal of Approximate Reasoning, 2006, 43(3): 268–285MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Triantaphyllou E, Shu B, Sanchez S N, Ray T. Multi-criteria decision making: An operations research approach. Encyclopedia of electrical and electronics engineering, 1998, 15: 175–186Google Scholar
  18. 18.
    Savitha P K, Chandrasekar C. Trusted network selection using SAW and TOPSIS algorithms for heterogeneous wireless networks. International Journal of Computers and Applications, 2011, 26(8): 22–29CrossRefGoogle Scholar
  19. 19.
    Asgharpour M J. Multiple Criteria Decision Making. 6th ed. Tehran: University of Tehran Press, 2008Google Scholar
  20. 20.
    Saaty T L. Decision making with the analytic hierarchy process. International Journal of Services Sciences, 2008, 1(1): 83–98CrossRefGoogle Scholar
  21. 21.
    Saaty T L. The Analytic Hierarchy Process. New York: McGraw-Hill, 1980zbMATHGoogle Scholar
  22. 22.
    Grain size analysis. Bureau of Indian Standards, IS: 2720 (Part 4), 1985Google Scholar
  23. 23.
    Alther G R. The role of bentonite in soil sealing applications. Bulletin of the Association of Engineering Geologists, 1982, 19: 401–409Google Scholar
  24. 24.
    Determination of specific gravity fine grained soils. Bureau of Indian Standards, IS: 2720 (Part 3), 1980Google Scholar
  25. 25.
    Classification and identification of soils for general engineering purposes. Bureau of Indian Standards, IS: 1498, 1970Google Scholar
  26. 26.
    Determination of water content—dry density relation using light compaction. Bureau of Indian Standards, IS: 2720 (Part 7), 1983Google Scholar
  27. 27.
    Laboratory determination of CBR. Bureau of Indian Standards, IS: 2720 (Part 16), 1987Google Scholar
  28. 28.
    Determination of unconfined compressive strength. Bureau of Indian Standards, IS: 2720 (Part 10), 1991Google Scholar
  29. 29.
    Determination of free swell index of soils. Bureau of Indian Standards, IS: 2720 (Part 40), 1977Google Scholar
  30. 30.
    Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015a, 102: 304–313CrossRefGoogle Scholar
  31. 31.
    Hamdia K M, Msekh M A, Silani M, Vu-Bac N, Zhuang X, Nguyen-Thoi T, Rabczuk T. Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling. Composite Structures, 2015b, 133: 1177–1190CrossRefGoogle Scholar
  32. 32.
    Mahata A, Mukhopadhyay T, Adhikari S. A polynomial chaos expansion based molecular dynamics study for probabilistic strength analysis of nano-twinned copper. Materials Research Express, 2016, 3(3): 036501CrossRefGoogle Scholar
  33. 33.
    Mukhopadhyay T, Mahata A, Dey S, Adhikari S. Probabilistic analysis and design of HCP nanowires: An efficient surrogate based molecular dynamics simulation approach. Journal of Materials Science and Technology, 2016a, 32(12): 1345–1351CrossRefGoogle Scholar
  34. 34.
    Mukhopadhyay T, Adhikari S. Equivalent in-plane elastic properties of irregular honeycombs: An analytical approach. International Journal of Solids and Structures, 2016b, 91: 169–184CrossRefGoogle Scholar
  35. 35.
    Mukhopadhyay T, Naskar S, Dey S, Adhikari S. On quantifying the effect of noise in surrogate based stochastic free vibration analysis of laminated composite shallow shells. Composite Structures, 2016c, 140: 798–805CrossRefGoogle Scholar
  36. 36.
    Mukhopadhyay T, Adhikari S. Free vibration analysis of sandwich panels with randomly irregular honeycomb core. Journal of Engineering Mechanics, 2016d, 142(11): 06016008CrossRefGoogle Scholar
  37. 37.
    Mukhopadhyay T, Adhikari S. Effective in-plane elastic properties of auxetic honeycombs with spatial irregularity. Mechanics of Materials, 2016e, 95: 204–222CrossRefGoogle Scholar
  38. 38.
    Mukhopadhyay T, Adhikari S. Stochastic mechanics of metamaterials. Composite Structures, 2017a, 162: 85–97CrossRefGoogle Scholar
  39. 39.
    Mukhopadhyay T, Adhikari S. Effective in-plane elastic moduli of quasi-random spatially irregular hexagonal lattices. International Journal of Engineering Science, 2017b, 119: 142–179CrossRefGoogle Scholar
  40. 40.
    Dey S, Mukhopadhyay T, Khodaparast H H, Kerfriden P, Adhikari S. Rotational and ply-level uncertainty in response of composite shallow conical shells. Composite Structures, 2015, 131: 594–605CrossRefGoogle Scholar
  41. 41.
    Dey S, Mukhopadhyay T, Khodaparast H H, Adhikari S. Fuzzy uncertainty propagation in composites using Gram-Schmidt polynomial chaos expansion. Applied Mathematical Modelling, 2016a, 40(7–8): 4412–4428MathSciNetCrossRefGoogle Scholar
  42. 42.
    Dey S, Mukhopadhyay T, Spickenheuer A, Adhikari S, Heinrich G. Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite plates. Composite Structures, 2016b, 140: 712–727CrossRefGoogle Scholar
  43. 43.
    Dey S, Mukhopadhyay T, Sahu S K, Adhikari S. Effect of cutout on stochastic natural frequency of composite curved panels. Composites. Part B, Engineering, 2016c, 105: 188–202CrossRefGoogle Scholar
  44. 44.
    Dey S, Mukhopadhyay T, Spickenheuer A, Gohs U, Adhikari S. Uncertainty quantification in natural frequency of composite plates—An Artificial neural network based approach. Advanced Composites Letters, 2016d, 25(2): 43–48Google Scholar
  45. 45.
    Dey S, Naskar S, Mukhopadhyay T, Gohs U, Spickenheuer A, Bittrich L, Sriramula S, Adhikari S, Heinrich G. Uncertain natural frequency analysis of composite plates including effect of noise—A polynomial neural network approach. Composite Structures, 2016e, 143: 130–142CrossRefGoogle Scholar
  46. 46.
    Dey S, Mukhopadhyay T, Adhikari S. Metamodel based highfidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment. Composite Structures, 2017a, 171: 227–250CrossRefGoogle Scholar
  47. 47.
    Dey S, Mukhopadhyay T, Naskar S, Dey T K, Chalak H D, Adhikari S. Probabilistic characterization for dynamics and stability of laminated soft core sandwich plates. Journal of Sandwich Structures & Materials, 2016, doi: 10.1177/1099636217694229Google Scholar
  48. 48.
    Naskar S, Mukhopadhyay T, Sriramula S, Adhikari S. Stochastic natural frequency analysis of damaged thin-walled laminated composite beams with uncertainty in micromechanical properties. Composite Structures, 2017, 160: 312–334CrossRefGoogle Scholar
  49. 49.
    Metya S, Mukhopadhyay T, Adhikari S, Bhattacharya G. System reliability analysis of soil slopes with general slip surfaces using multivariate adaptive regression splines. Computers and Geotechnics, 2017, 87: 212–228CrossRefGoogle Scholar
  50. 50.
    Dey S, Mukhopadhyay T, Sahu S K, Adhikari S. Stochastic dynamic stability analysis of composite curved panels subjected to non-uniform partial edge loading. European Journal of Mechanics/A Solids, 2018, 67: 108–122MathSciNetCrossRefGoogle Scholar
  51. 51.
    Mukhopadhyay T, Adhikari S, Batou A. Frequency domain homogenization for the viscoelastic properties of spatially correlated quasi-periodic lattices. International Journal of Mechanical Sciences, 2017, in press. doi: 10.1016/j.ijmecsci.2017.09.004Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Amit K. Bera
    • 1
  • Tanmoy Mukhopadhyay
    • 2
    Email author
  • Ponnada J. Mohan
    • 1
  • Tushar K. Dey
    • 3
  1. 1.Faculty of Science & TechnologyThe ICFAI UniversityDehradunIndia
  2. 2.College of EngineeringSwansea UniversitySwanseaUK
  3. 3.Department of Civil EngineeringNational Institute of Technical Teachers’ Training and Research (NITTTR)KolkataIndia

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