Advertisement

Neural Computing and Applications

, Volume 31, Supplement 2, pp 743–750 | Cite as

Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks

  • Shadi Riahi
  • Ali NazariEmail author
Original Article

Abstract

In the present work, compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nano alumina, and nano silica has been predicted by artificial neural networks. The neural network models were constructed by 12 input parameters including the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO2 nanoparticles content, Al2O3 nanoparticles content, oven curing temperature, oven curing time, and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated, and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

Keywords

Geopolymer Compressive strength Nanoparticles mixture Artificial neural networks 

References

  1. 1.
    Zhang YJ, Li S, Wang YC, Xu DL (2012) Microstructural and strength evolutions of geopolymer composite reinforced by resin exposed to elevated temperature. J Non Cryst Solids 358:620–624CrossRefGoogle Scholar
  2. 2.
    Pala M, Ozbay O, Oztas A, Yuce MI (2005) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Constr Build Mater 21(2):384–394CrossRefGoogle Scholar
  3. 3.
    Wongpa J, Kiattikomol K, Jaturapitakkul C, Chindaprasirt P (2010) Compressive strength, modulus of elasticity, and water permeability of inorganic polymer concrete. Mater Des 31:4748–4754CrossRefGoogle Scholar
  4. 4.
    Yeh IC (1998) Modeling of strength of HPC using ANN. Cem Concr Res 28(12):1797–1808CrossRefGoogle Scholar
  5. 5.
    Lai S, Sera M (1997) Concrete strength prediction by mean of neural networks. Constr Build Mater 11(2):93–98CrossRefGoogle Scholar
  6. 6.
    Lee SC (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25(7):849–857CrossRefGoogle Scholar
  7. 7.
    Hong-Guang N, Ji-Zong W (2000) Prediction of compressive strength of concrete by neural networks. Cem Concr Res 30(8):1245–1250CrossRefGoogle Scholar
  8. 8.
    Dias WPS, Pooliyadda SP (2001) Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 15(7):371–379CrossRefGoogle Scholar
  9. 9.
    Oztas A, Pala M, Ozbay E, Kanca E, Caglar N, Asghar Bhatti M (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 20(9):769–775CrossRefGoogle Scholar
  10. 10.
    Akkurt S, Ozdemir S, Tayfur G, Akyol B (2003) The use of GA-ANNs in the modelling of compressive strength of cement mortar. Cem Concr Res 33(7):973–979CrossRefGoogle Scholar
  11. 11.
    Mukherjee A, Biswas SN (1997) Artificial neural networks in prediction of mechanical behavior of concrete at high temperature. Nucl Eng Des 178(1):1–11CrossRefGoogle Scholar
  12. 12.
    Nazari A, Riahi S (2012) Experimental investigations and ANFIS prediction of water absorption of geopolymers produced by waste ashes. J Non Cryst Solids 358(1):40–46CrossRefGoogle Scholar
  13. 13.
    Nazari A, Khalaj G, Riahi S, Bohlooli H, Kaykha MM (2012) Prediction total specific pore volume of geopolymers produced from waste ashes by ANFIS. Ceram Int 38:3111–3120CrossRefGoogle Scholar
  14. 14.
    Bohlooli H, Nazari A, Khalaj G, Kaykha MM, Riahi S (2012) Experimental investigations and fuzzy logic modeling of compressive strength of geopolymers with seeded fly ash and rice husk bark ash. Compos B 43:1293–1301CrossRefGoogle Scholar
  15. 15.
    Nazari A, Riahi S (2010) Microstructural, thermal, physical and mechanical behavior of the self compacting concrete containing SiO2 nanoparticles. Mater Sci Eng A 527:7663–7672CrossRefGoogle Scholar
  16. 16.
    Nazari A, Riahi S (2011) Improvement compressive strength of concrete in different curing media by Al2O3 nanoparticles. Mater Sci Eng A 528:1183–1191CrossRefGoogle Scholar
  17. 17.
    Riahi S, Nazari A (2012) The effects of nanoparticles on early age compressive strength of ash-based geopolymers. Ceram Int 38:4467–4476CrossRefGoogle Scholar
  18. 18.
    Riahi S, Nazari A, Zaarei D, Khalaj G, Bohlooli H, Kaykha MM (2012) Compressive strength of ash-based geopolymers at early ages designed by Taguchi method. Mater Des 37:443–449CrossRefGoogle Scholar
  19. 19.
    Topcu IB, Karakurt C, Sarıdemir M (2008) Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic. Mater Des 29:1986–1991CrossRefGoogle Scholar
  20. 20.
    Ince R (2004) Prediction of fracture parameters of concrete by artificial neural networks. Eng Fract Mech 71(15):2143–2159CrossRefGoogle Scholar
  21. 21.
    McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in neural nets. Bull Math Biophys 5:115–137Google Scholar
  22. 22.
    Sarıdemir M, Topcu IB, Ozcan F, Severcan MH (2009) Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic. Constr Build Mater 23:1279–1286CrossRefGoogle Scholar
  23. 23.
    Liu SW, Huang JH, Sung JC, Lee CC (2002) Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 191(25–26):2831–2845CrossRefzbMATHGoogle Scholar
  24. 24.
    Gunaydin HM, Dogan SZ (2004) A neural network approach for early cost estimation of structural systems of building. Int J Proj Manag 22(7):595–602CrossRefGoogle Scholar
  25. 25.
    Duxson P, Mallicoat SW, Lukey GC, Kriven WM, van Deventer JSJ (2007) The effect of alkali and Si/Al ratio on the development of mechanical properties of metakaolin-based geopolymers. Colloids Surf A 292(1):8–20CrossRefGoogle Scholar
  26. 26.
    Suratgar AA, Tavakoli MB, Hoseinabadi A (2005) Modified Levenberg–Marquardt method for neural networks training. World Acad Sci Eng Technol 6:46–48Google Scholar
  27. 27.
    Guzelbey IH, Cevik A, Erklig A (2006) Prediction of web crippling strength of cold-formed steel sheetings using neural networks. J Constr Steel Res 62:962–973CrossRefGoogle Scholar
  28. 28.
    Topcu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41(3):305–311CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Materials Science and Engineering, Saveh BranchIslamic Azad UniversitySavehIran

Personalised recommendations