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Influence of Aluminosilicate for the Prediction of Mechanical Properties of Geopolymer Concrete – Artificial Neural Network

  • S. NagajothiEmail author
  • S. Elavenil
Original Paper
  • 2 Downloads

Abstract

In this paper, details and results of experimental and predictive studies carried out to determine the mechanical properties of Aluminosilicate materials like Ground Granulated Blast furnace Slag (GGBS) and Fly Ash (FA) based geopolymer concrete specimens are presented and discussed. The major parameters considered in the experimental study are the percentages of GGBS and Fly ash and the percentage of manufactured sand (m-sand) used to replace conventional river sand used in the production of geopolymer concrete. Sodium hydroxide and sodium silicate solutions were used as the activator in the production of geopolymer concrete. The mechanical properties of the geopolymer concrete determined were the compressive strength, split-tensile strength and flexural strength. The test results showed that the mechanical properties of geopolymer concrete improved with increase in the percentage use of GGBS. Also, it was observed from the test results that increase in the percentage use of m-sand increased the mechanical properties of the geopolymer concrete up to an optimum dosage beyond which reduction in the mechanical properties was observed. The predicted mechanical properties of the geopolymer concrete using Artificial Neural Network (ANN) was found to be in good agreement with the test results.

Keywords

Geopolymer concrete Aluminosilicate materials Alkali activated solutions Artificial neural network 

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Notes

Acknowledgements

The author gratefully acknowledging Dr.S.Elavenil for her fabulous guidance and greater support for valuable suggestions on time. The author also acknowledges sincere thanks to the laboratory staffs of Vellore Institute of Technology, Chennai for their kind support.

References

  1. 1.
    Sakulich AR (2011) Reinforced geopolymer composites for enhanced material greenness and durability. Sustain Cities Soc 1:195–210CrossRefGoogle Scholar
  2. 2.
    Neupane K (2016) Fly ash and GGBFS based powder-activated geopolymer binders: a viable sustainable alternative of Portland cement in concrete industry. Mech Mater 103:110–122.  https://doi.org/10.1016/j.mechmat.2016.09.012 CrossRefGoogle Scholar
  3. 3.
    Davidovits J (1991) Geopolymers-inorganic polymeric new materials. J Therm Anal Calorim 37(8):1633–1656CrossRefGoogle Scholar
  4. 4.
    Kong LY, Daniel SJG (2008) Damage behavior of geopolymer composites exposed to elevated temperatures. Cem Concr Compos – Accepted manuscript 30:986–991.  https://doi.org/10.1016/j.cemconcomp.2008.08.001 CrossRefGoogle Scholar
  5. 5.
    Mehta A, Siddique R (2016) An overview of geopolymers derived from industrial by-products. Constr Build Mater 127:183–198.  https://doi.org/10.1016/j.conbuildmat.2016.09.136 CrossRefGoogle Scholar
  6. 6.
    Al Bakiri AMM, Kamarudin H, Bnhussain M, Rafiza AR, Zarina Y (2012) Effect of Na2SiO3/NaOH ratios and naoh molarities on compressive strength of fly-ash-based geopolymer. ACI Mater J 109(5):503–508Google Scholar
  7. 7.
    Manjunath GS, Radhakrishna GC, Jadhav M (2011) Compressive strength development in ambient cured geo-polymer mortar. Int J Earth Sci Eng 4(6):830–834Google Scholar
  8. 8.
    Pavithra P, Srinivasula Reddy M, Dinakar P, Hanumantha Rao B, Satpathy BK, Mohanty AN (2016) A mix design procedure for geopolymer concrete with fly ash. J Clean Prod 133:117–125.  https://doi.org/10.1016/j.jclepro.2016.05.041 CrossRefGoogle Scholar
  9. 9.
    Reddy MS, Dinakar P, Rao BH (2018) Mix design development of fly ash and ground granulated blast furnace slag based geopolymer concrete. J Build Eng – Accepted manuscript 20:712–722.  https://doi.org/10.1016/j.jobe.2018.09.010 CrossRefGoogle Scholar
  10. 10.
    Nath P, Sarker PK (2014) Effect of GGBFS on setting, workability and early strength properties of fly ash geopolymer concrete cured in ambient condition. Constr Build Mater 66:163–171CrossRefGoogle Scholar
  11. 11.
    Fernandez-Jiminez AM, Palomo A, Lopez-Hombrados C (2006) Engineering properties of alkali-activated fly ash concrete. ACI Mater J 103(2):106–112Google Scholar
  12. 12.
    Hardjito D, Wallah SE, Sumajouw MJ, Rangan BV (2004) On the development of fly ash-based geopolymer concrete. ACI Mater J 101(6):467–472Google Scholar
  13. 13.
    Diaz-Loya EI, Allouche EN, Vaidya S (2011) Mechanical properties of fly ash-based geopolymer concrete. ACI Mater J 108(3):300–306Google Scholar
  14. 14.
    Sofi M, van Deventer JSJ, Mendis PA, Lukey GC (2007) Engineering properties of inorganic polymer concretes (IPCs). Engineering Properties of Inorganic Polymer Concretes Cement Concrete Res 37(2):251–257CrossRefGoogle Scholar
  15. 15.
    Rafeet A, Vinai R, Soutsos M, Sha W (2017) Guidelines for mix proportioning of fly ash/GGBS based alkali activated concretes. Constr Build Mater 147:130–142.  https://doi.org/10.1016/j.conbuildmat.2017.04.036 CrossRefGoogle Scholar
  16. 16.
    Soutsos M, Boyle AP, Vinai R, Hadjierakleous A, Barnett SJ (2016) Factors influencing the compressive strength of fly ash based geopolymers. Constr Build Mater 110:355–368.  https://doi.org/10.1016/j.conbuildmat.2015.11.045 CrossRefGoogle Scholar
  17. 17.
    Aguilar AR, Escalante-Garcia JI, Gorokhovsky A, Burciaga-Diaz O, Almanza-Robles M (2007) Mortars of inorganic polymers based metakaolin: effect of chemical composition and temperature on compressive strength. Proceedings of IX Latin American Congress of Pathology and XI Congress of Quality Control in the Construction, Quito, EcuadorGoogle Scholar
  18. 18.
    Bakharev T (2005) Durability of geopolymer materials in sodium and magnesium sulfate solutions. Cem Concr Res 35:1233–1246CrossRefGoogle Scholar
  19. 19.
    Burciaga-Diaz O (2007) Inorganic polymers based on metakaolin, thermal and chemical resistance as a function of the chemical composition. Ceramic Engineering CinvestavGoogle Scholar
  20. 20.
    Hardjito D, Wallah SE, Sumajouw DMJ, Vijaya Rangan B (2004) On the development of fly ash-based geopolymer concrete. ACI Mater J 6:467–472Google Scholar
  21. 21.
    Kong DLY, Sanjayan JG (2008) Damage behaviour of geopolymer composites exposed to elevated temperatures. Cem Concr Compos 30:986–991CrossRefGoogle Scholar
  22. 22.
    Salvador V, Lange DAJ, Roesler R (2005) Evaluation, testing and comparison between crushed manufactured sand and natural sand. University of IllinoisGoogle Scholar
  23. 23.
    Kim JK, Lee CS, Park CK, Eo SH (1997) The fracture characteristics of crushed limestone sand concrete. Cem Concr Res 27(11):1719–1729CrossRefGoogle Scholar
  24. 24.
    Nanthagopalan P, Santhanam M (2011) Fresh and hardened properties of selfcompacting concrete produced with manufactured sand. Cem Concr Compos 33(3):353–358CrossRefGoogle Scholar
  25. 25.
    Gonçalves JP, Tavares LM, Toledo Filho RD, Fairbairn EMR, Cunha ER (2007) Comparison of natural and manufactured fine aggregates in cement mortars. Cem Concr Compos 37(6):924–932CrossRefGoogle Scholar
  26. 26.
    Aleem MIA, Arumairaj PD, Vairam S (2013) Chemical formulation of geopolymer concrete with m-sand. Int J Res Civ Eng, Archi Des 1(2):54–60Google Scholar
  27. 27.
    Janani R, Revathi A (2015) Experimental study of geopolymer concrete with manufactured sand. Int J Sci Eng Technol Res 4(4):1054–1057Google Scholar
  28. 28.
    Zhou M (2003) Intelligent systems technology and applications CRC press LLC, Washington DCGoogle Scholar
  29. 29.
    Chopra P (2014) Regression models for the prediction of compressive strength of concrete with & without fly ash Int J Latest Trends Eng Technol (IJLTET 3 (4) 400–406Google Scholar
  30. 30.
    Rumelhart DE, McClelland JL (1986) Parallel distributed processing: exploration in the microstructure of cognition. MIT Press, CambridgeGoogle Scholar
  31. 31.
    Raghu Prasad BK, Eskandari H, Venkatarama Reddy BV (2009) Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr Build Mater 23:117–128CrossRefGoogle Scholar
  32. 32.
    Vidivelli B, Jayaranjini A (2016) Prediction of compressive strength of high performance concrete containing industrial by products using artificial neural networks. Int J Civ Eng Technol 7(2):302–314Google Scholar
  33. 33.
    Jamalaldin S, Hakim S, Noorzaei J, Jaafar MS, Jameel M (2011) Application of artificial neural networks to predict compressive strength of high strength concrete. Int J of Phys Sci 6(5):975–981Google Scholar
  34. 34.
    Chopra P, Sharma RK, Kumar M (2016) Prediction of compressive strength of concrete using artificial neural network and genetic programming. Adv Mater Sci Eng Article ID:7648467Google Scholar
  35. 35.
    Nazari A (2012a) Artificial neural networks to prediction compressive strength of geopolymers with seeded waste ashes. Neural Comput & Applic 23(2):391–402.  https://doi.org/10.1007/s00521-012-0931-4 CrossRefGoogle Scholar
  36. 36.
    Nazari A (2012b) Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput & Applic 23(2):507–518.  https://doi.org/10.1007/s00521-012-0945-y CrossRefGoogle Scholar
  37. 37.
    Riahi S, Nazari A (2012) Prediction the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks. Neural Comput & Applic 31:1–8.  https://doi.org/10.1007/s00521-012-1085-0 Google Scholar
  38. 38.
    Ali N, Torgal PF (2013) Predicting compressive strength of different geopolymers by artificial neural networks. Ceram Int 39:2247–2257CrossRefGoogle Scholar
  39. 39.
    Yadollahi MM, Benli A, Demirboga R (2015) Prediction of compressive strength of geopolymer composites using an artificial neural network. Mater Res Innov 19(6):453–458CrossRefGoogle Scholar
  40. 40.
    Kong X, Khambadkone AM (2009) Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based sub-models. IEEE Trans Ind Electron 56(12):4903–4914CrossRefGoogle Scholar
  41. 41.
    Anderson JA (1983) Cognitive and psychological computation with neural models. IEEE Transactions on Systems Man and Cybernetics SMC-13(5):799–814CrossRefGoogle Scholar
  42. 42.
    Shafigh P, Jumaat MZ, Mahmud HB, Alengaram UJ (2013b) Oil palm shell lightweight concrete containing high volum ground granulated blast furnace slag. Constr Build Mater 40:231–238CrossRefGoogle Scholar
  43. 43.
    Mo KH, Johnson Alengaram U, Jumaat MZ, Liu MYJ, Lim J (2016) Assessing some durability properties of sustainable lightweight oil palm shell concrete incorporating slag and manufactured sand. J Clean Prod 112:763–770CrossRefGoogle Scholar
  44. 44.
    Ravikumar D, Peethamparam S, Neithalath N (2010) Structure and strength of NaOH activated concrete containing fly ash or GGBFS as the sole binder. Cem Concr Compos 32:399–410CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Mechanical and Building Sciences, VITChennaiIndia

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