Using Artificial Neural Networks for the Prediction of the Compressive Strength of Geopolymer Fly Ash


  • K. P. Rusna Department of Civil Engineering, Coimbatore Institute of Technology, India
  • V. G. Kalpana Department of Civil Engineering, Coimbatore Institute of Technology, India
Volume: 12 | Issue: 5 | Pages: 9120-9125 | October 2022 |


Geopolymers are promising cement replacement materials as their use results in a considerable reduction of CO2 emissions. Geopolymer Fly ash (GF) is a widely used geopolymer due to its low cost and waste management achievement. The compressive strength of GF depends on variables such as curing time, curing temperature, NaOH molarity, the ratio of sodium silicate to sodium hydroxide, the ratio of fly ash to alkaline solution, etc. Artificial Neural Networks are employed to predict the strength of GF due to their accurate prediction capability as well as saving time and cost of experimental work. The obtained Root Mean Square Error (RMSE) and correction coefficient (R2) values were 4.47 and 0.972 respectively. The results illustrate the ability of the ANN model to be used as an efficient tool in predicting the compressive strength and determining the optimal values of GF parameters. The maximum strength of GF was observed for 2 hours curing time at 100°C, molarity of 10, fly ash to alkaline solution ratio of 3, and sodium silicate to sodium hydroxide ratio of 1.


fly ash, alkaline solution, geopolymer fly ash, Artificial Neural Networks (ANNs), compressive strength


Download data is not yet available.


A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and R. Tavakkoli-Moghaddam, "A bi-objective green home health care routing problem," Journal of Cleaner Production, vol. 200, pp. 423–443, Nov. 2018. DOI:

I. Tekin, O. Gencel, A. Gholampour, O. H. Oren, F. Koksal, and T. Ozbakkaloglu, "Recycling zeolitic tuff and marble waste in the production of eco-friendly geopolymer concretes," Journal of Cleaner Production, vol. 268, Sep. 2020, Art. no. 122298. DOI:

M. A. Getahun, S. M. Shitote, and Z. C. Abiero Gariy, "Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes," Construction and Building Materials, vol. 190, pp. 517–525, Nov. 2018. DOI:

A. Saand, T. Ali, M. A. Keerio, and D. K. Bangwar, "Experimental Study on the Use of Rice Husk Ash as Partial Cement Replacement in Aerated Concrete," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4534–4537, Aug. 2019. DOI:

N. Bheel, A. W. Abro, I. A. Shar, A. A. Dayo, S. Shaikh, and Z. H. Shaikh, "Use of Rice Husk Ash as Cementitious Material in Concrete," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4209–4212, Jun. 2019. DOI:

S. Khoso, S. A. Abbasi, T. Ali, Z. Soomro, M. T. Naqash, and A. A. Ansari, "The Effect of Water-Binder Ratio and RHA on the Mechanical Performance of Sustainable Concrete," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8520–8524, Jun. 2022. DOI:

A. Mehta and R. Siddique, "Sustainable geopolymer concrete using ground granulated blast furnace slag and rice husk ash: Strength and permeability properties," Journal of Cleaner Production, vol. 205, pp. 49–57, Dec. 2018. DOI:

P. Duxson, A. Fernandez-Jimenez, J. L. Provis, G. C. Lukey, A. Palomo, and J. S. J. van Deventer, "Geopolymer technology: the current state of the art," Journal of Materials Science, vol. 42, no. 9, pp. 2917–2933, May 2007. DOI:

A. Nazari and F. Pacheco Torgal, "Predicting compressive strength of different geopolymers by artificial neural networks," Ceramics International, vol. 39, no. 3, pp. 2247–2257, Apr. 2013. DOI:

J. Davidovits, "High-Alkali Cements for 21st Century Concretes," Special Publication, vol. 144, pp. 383–398, Mar. 1994.

J. G. S. Van Jaarsveld, J. S. J. Van Deventer, and L. Lorenzen, "The potential use of geopolymeric materials to immobilise toxic metals: Part I. Theory and applications," Minerals Engineering, vol. 10, no. 7, pp. 659–669, Jul. 1997. DOI:

D. Hardjito, S. E. Wallah, D. M. J. Sumajouw, and B. V. Rangan, "Fly Ash-Based Geopolymer Concrete," Australian Journal of Structural Engineering, vol. 6, no. 1, pp. 77–86, Jan. 2005. DOI:

A. A. Shahmansouri, H. Akbarzadeh Bengar, and S. Ghanbari, "Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method," Journal of Building Engineering, vol. 31, Sep. 2020, Art. no. 101326. DOI:

Z. H. Duan, S. C. Kou, and C. S. Poon, "Prediction of compressive strength of recycled aggregate concrete using artificial neural networks," Construction and Building Materials, vol. 40, pp. 1200–1206, Mar. 2013. DOI:

I. B. Topcu and M. Sarıdemir, "Prediction of properties of waste AAC aggregate concrete using artificial neural network," Computational Materials Science, vol. 41, no. 1, pp. 117–125, Nov. 2007. DOI:

A. Foucquier, S. Robert, F. Suard, L. Stephan, and A. Jay, "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, vol. 23, pp. 272–288, Jul. 2013. DOI:

H. Song et al., "Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms," Construction and Building Materials, vol. 308, Nov. 2021, Art. no. 125021. DOI:

K. K. Yaswanth, J. Revathy, and P. Gajalakshmi, "Soft Computing Techniques for the Prediction and Analysis of Compressive Strength of Alkali-Activated Alumino-Silicate Based Strain-Hardening Geopolymer Composites," Silicon, vol. 14, no. 5, pp. 1985–2008, Apr. 2022. DOI:

D. Tsamatsoulis, "Prediction of Cement Compressive Strength by Combining Dynamic Models of Neural Networks," Chemical and Biochemical Engineering Quarterly, vol. 35, no. 3, pp. 295–318, Oct. 2021. DOI:

H. Chen, C. Qian, C. Liang, and W. Kang, "An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack," PLOS ONE, vol. 13, no. 1, Jan. 2018, Art. no. e0191370. DOI:

M. M. Yadollahi, A. Benli, and R. Demirboga, "Prediction of compressive strength of geopolymer composites using an artificial neural network," Energy Materials, vol. 10, no. 3, pp. 453–458, Sep. 2015. DOI:

ASTM C618-19(2008), Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete. West Conshohocken, PA, USA: ASTM International, 2008.

IS 1727 (1967), Method of test for pozzolanic materials. New Delhi, India: Bureau of Indian Standards, 1967.

A. Azadeh, J. Seif, M. Sheikhalishahi, and M. Yazdani, "An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine," International Journal of Computer Integrated Manufacturing, vol. 29, no. 1, pp. 16–24, Jan. 2016. DOI:

ASTM C109/C109M-20(2011), Standard Test Method For Compressive Strength Of Hydraulic Cement Mortars (Using 2-In. Or [50-Mm] Cube Specimens). West Conshohocken, PA, USA: ASTM International, 2011.

S. Parvathy S, A. K. Sharma, and K. B. Anand, "Comparative study on synthesis and properties of geopolymer fine aggregate from fly ashes," Construction and Building Materials, vol. 198, pp. 359–367, Feb. 2019. DOI:

S. M. Rao and I. P. Acharya, "Synthesis and Characterization of Fly Ash Geopolymer Sand," Journal of Materials in Civil Engineering, vol. 26, no. 5, pp. 912–917, May 2014. DOI:

U. S. Agrawal, S. P. Wanjari, and D. N. Naresh, "Characteristic study of geopolymer fly ash sand as a replacement to natural river sand," Construction and Building Materials, vol. 150, pp. 681–688, Sep. 2017. DOI:

P. Chindaprasirt, C. Jaturapitakkul, W. Chalee, and U. Rattanasak, "Comparative study on the characteristics of fly ash and bottom ash geopolymers," Waste Management, vol. 29, no. 2, pp. 539–543, Feb. 2009. DOI:

A. Sathonsaowaphak, P. Chindaprasirt, and K. Pimraksa, "Workability and strength of lignite bottom ash geopolymer mortar," Journal of Hazardous Materials, vol. 168, no. 1, pp. 44–50, Aug. 2009. DOI:


How to Cite

K. P. Rusna and V. G. Kalpana, “Using Artificial Neural Networks for the Prediction of the Compressive Strength of Geopolymer Fly Ash”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 5, pp. 9120–9125, Oct. 2022.


Abstract Views: 382
PDF Downloads: 195

Metrics Information
Bookmark and Share