Models for Predictions of Mechanical Properties of Low-Density Self-compacting Concrete Prepared from Mineral Admixtures and Pumice Stone

  • B. Arun Kumar
  • G. Sangeetha
  • A. Srinivas
  • P. O. Awoyera
  • R. GobinathEmail author
  • V. Venkata Ramana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


This study applies the principle of artificial neural networks for modelling the mechanical characteristics of a lightweight self-compacting concrete containing pumice and mineral admixtures. Models for predicting compressive strength, split tensile strength and flexural strengths were developed based on several measures of the materials as obtained from the experimental stage. The input parameters for the model were contents of cement, ground granulated blast furnace slag (GGBS), rice husk ash (RHA), fine aggregates, coarse aggregates, pumice stone, water, super-plasticizers and micro-silica. Three output parameters, including compressive strength, tensile strength and flexural strength were considered. The data were trained, tested and validated using the feedforward backpropagation algorithm. The study established the best model for the tested concrete, based on the minimal error criteria, as 9 (input), 12 (hidden layer) and 3 (output layer). This model is expected to serve as a useful tool for concrete designers and constructors.


Compressive Split tensile Flexural strengths Artificial neural network Mineral admixtures Pumice stone 


  1. 1.
    Imbabi, M.S., Carrigan, C., McKenna, S.: Trends and developments in green cement and concrete technology. Int. J. Sustain. Built Environ. 1(2), 194–216 (2012)CrossRefGoogle Scholar
  2. 2.
    Schneider, M., Romer, M., Tschudin, M., Bolio, H.: Sustainable cement production—present and future. Cem. Concr. Res. 41(7), 642–650 (2011)CrossRefGoogle Scholar
  3. 3.
    Rujanu, M., Diaconu, L.I., Babor, D., Plian, D., Diaconu, A.C.: Study on the optimization of some cement based mixing binders’ characteristics. Proc. Manuf. 22, 114–120 (2018)Google Scholar
  4. 4.
    Murthi, P., Awoyera, P., Selvaraj, P., Dharsana, D., Gobinath, R.: Using silica mineral waste as aggregate in a green high strength concrete: workability, strength, failure mode, and morphology assessment. Aust. J. Civ. Eng., 1–7 (2018)Google Scholar
  5. 5.
    Karthika, V., Awoyera, P.O., Akinwumi, I.I., Gobinath, R., Gunasekaran, R., Lokesh, N.: Structural properties of lightweight self-compacting concrete made with pumice stone and mineral admixtures. Revista Romana de Materiale/Rom. J. Mater. 48(2), 208–213 (2018)Google Scholar
  6. 6.
    Chen, J.J., Ng, P.L., Kwan, A.K.H., Li, L.G.: Lowering cement content in mortar by adding superfine zeolite as cement replacement and optimizing mixture proportions. J. Clean. Prod. 210, 66–76 (2019)CrossRefGoogle Scholar
  7. 7.
    Karri, R.R.: Evaluating and estimating the complex dynamic phenomena in nonlinear chemical systems. Int. J. Chem. Reactor Eng. 9 (2011)Google Scholar
  8. 8.
    Busahmin, B., Maini, B., Karri, R.R., Sabet, M.: Studies on the stability of the foamy oil in developing heavy oil reservoirs. Defect Diffus. Forum 371, 111–116 (2016)CrossRefGoogle Scholar
  9. 9.
    Maini, B.B., Busahmin, B.: Foamy Oil Flow and Its Role in Heavy Oil Production, pp. 103–108 (2010)Google Scholar
  10. 10.
    Anandaraj, S., Rooby, J., Awoyera, P.O., Gobinath, R.: Structural distress in glass fibre-reinforced concrete under loading and exposure to aggressive environments. Constr. Build. Mater. (2018)Google Scholar
  11. 11.
    Muthukanf, C., Suji, D., Mariappan, M., Gobinath, R.: Studies on recycling of sludge from bleaching and dyeing industries in cement industries. Pollut. Res. 34(1), 209–214 (2015)Google Scholar
  12. 12.
    Gobinath, R., Ganapathy, G.P., Akinwumi, I.I.: Evaluating the use of lemon grass roots for the reinforcement of a landslide affected soil from Nilgiris district, Tamil Nadu, India. J. Mater. Environ. Sci. 6(10), 2681–2687 (2015)Google Scholar
  13. 13.
    Liu, G., Cheng, W., Chen, L.: Investigating and optimizing the mix proportion of pumping wet-mix shotcrete with polypropylene fiber. Constr. Build. Mater. 150, 14–23 (2017)CrossRefGoogle Scholar
  14. 14.
    Abusahmin, B.S., Karri, R.R., Maini, B.B.: Influence of fluid and operating parameters on the recovery factors and gas oil ratio in high viscous reservoirs under foamy solution gas drive. Fuel 197, 497–517 (2017)CrossRefGoogle Scholar
  15. 15.
    Rao, K.R., Srinivasan, T., Venkateswarlu, C.: Mathematical and kinetic modeling of biofilm reactor based on ant colony optimization. Process Biochem. (Amsterdam, Neth.) 45(6), 961–972 (2010)CrossRefGoogle Scholar
  16. 16.
    Rao, K.R., Rao, D.P., Venkateswarlu, C.: Soft Sensor Based Nonlinear Control of a Chaotic Reactor (2009)Google Scholar
  17. 17.
    Karri, R.R., Sahu, J.N., Jayakumar, N.S.: Optimal isotherm parameters for phenol adsorption from aqueous solutions onto coconut shell based activated carbon: error analysis of linear and non-linear methods. J. Taiwan Inst. Chem. Eng. 80, 472–487 (2017)CrossRefGoogle Scholar
  18. 18.
    Lanka, S., Madhavim, R., Abusahmin, B.S., Puvvada, N., Lakshminarayana, V.: Predictive data mining techniques for management of high dimensional big-data. J. Ind. Pollut. Control 33, 1430–1436 (2017)Google Scholar
  19. 19.
    Madhavi, R., Karri, R.R., Sankar, D.S., Nagesh, P., Lakshminarayana, V.: Nature inspired techniques to solve complex engineering problems. J. Ind. Pollut. Control 33(1), 1304–1311 (2017)Google Scholar
  20. 20.
    Lingamdinne, L.P., Singh, J., Choi, J.S., Chang, Y.Y., Yang, J.K., Karri, R.R., Koduru, J.R.: Multivariate modeling via artificial neural network applied to enhance methylene blue sorption using graphene-like carbon material prepared from edible sugar. J. Mol. Liq. 265, 416–427 (2018)CrossRefGoogle Scholar
  21. 21.
    Lingamdinne, L.P., Koduru, J.R., Chang, Y.Y., Karri, R.R.: Process optimization and adsorption modeling of Pb(II) on nickel ferrite-reduced graphene oxide nano-composite. J. Mol. Liq. 250, 202–211 (2018)CrossRefGoogle Scholar
  22. 22.
    Karri, R.R., Tanzifi, M., Tavakkoli Yaraki, M., Sahu, J.N.: Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network. J. Environ. Manage. 223, 517–529 (2018)CrossRefGoogle Scholar
  23. 23.
    Karri, R.R., Sahu, J.N.: Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment. J. Envi. Manage. 206, 178–191 (2018)CrossRefGoogle Scholar
  24. 24.
    Karri, R.R., Sahu, J.N.: Process optimization and adsorption modeling using activated carbon derived from palm oil kernel shell for Zn (II) disposal from the aqueous environment using differential evolution embedded neural network. J. Mol. Liq. 265, 592–602 (2018)CrossRefGoogle Scholar
  25. 25.
    Eskandari-Naddaf, H., Kazemi, R.: ANN prediction of cement mortar compressive strength, influence of cement strength class. Constr. Build. Mater. 138, 1–11 (2017)CrossRefGoogle Scholar
  26. 26.
    Eskandari, H., Tayyebinia, M.: Effect of 32.5 and 42.5 cement grades on ANN prediction of fibrocement compressive strength. Proc. Eng. 150, 2193–2201 (2016)CrossRefGoogle Scholar
  27. 27.
    Azimi-Pour, M., Eskandari-Naddaf, H.: ANN and GEP prediction for simultaneous effect of nano and micro silica on the compressive and flexural strength of cement mortar. Constr. Build. Mater. 189, 978–992 (2018)CrossRefGoogle Scholar
  28. 28.
    Awoyera, P.O.: Mechanical and Microstructural Characterization of Ceramic-Laterized Concrete Composite. Ph.D. Thesis, Covenant University, Ota, Nigeria (2018)Google Scholar
  29. 29.
    Awoyera, P.O.: Predictive models for determination of compressive and split-tensile strengths of steel slag aggregate concrete. Mater. Res. Innov. 22, 287–293 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. Arun Kumar
    • 1
    • 2
  • G. Sangeetha
    • 1
    • 2
  • A. Srinivas
    • 1
  • P. O. Awoyera
    • 3
  • R. Gobinath
    • 1
    Email author
  • V. Venkata Ramana
    • 1
    • 2
  1. 1.S R Engineering CollegeWarangalIndia
  2. 2.Center for Artificial Intelligence and Deep LearningS R Engineering CollegeWarangalIndia
  3. 3.Department of Civil EngineeringCovenant UniversityOtaNigeria

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