Compressive Strength Prediction for Industrial Waste-Based SCC Using Artificial Neural Network

  • Md Akram Hossain Chittagong University of Engineering & Technology (CUET)
  • G M Sadiqul Islam Chittagong University of Engineering & Technology (CUET)
  • Amit Mallick Chittagong University of Engineering & Technology (CUET)
Keywords: Self-compacting concrete, Industrial Waste, Artificial Neural Network, Back-propagation, Fly ash


Concrete is the most used construction material in the world. Sustainable construction practice demands durable material. A particular type of concrete that flows and consolidates under its weight is proposed to reduce labor dependency during construction, called self-compacting concrete. It is installed without vibration due to its excellent deformability and flowability while remaining cohesive enough to be treated without difficulty. Evaluating its compressive strength is essential as it is used in important construction projects. An artificial neural network (ANN) is a predicting tool that can predict output in various sectors. This study evaluated the compressive strength of industrial waste such as fly ash and silica fume incorporated in self-compacting concrete at various ages. A non-linear relationship was used to develop the model relating mix composition and SCC compressive strength using an Artificial Neural Network (ANN). The experimental and expected outcomes were compared with the model prediction to evaluate the predictive capacity, generalize the generated model, and observe suitable matches. The developed ANN network can predict the desired output, i.e., compressive strength incorporating industrial waste. Furthermore, the influence of individual parameters viz. cement, silica fume, and fly ash, w/b were also evaluated using parametric analysis, which shows the sensitivity of various materials on the compressive strength of Self-compacting concrete. As a result, a higher correlation coefficient of 0.9835 with a smaller value of MAPE (0.0347) and RMSE (2.4503) is obtained. Finally, a process of creating tools for practical engineers and field users is proposed, which would be very handy and fast for predicting the strength of SCC.


Ahmad, J., Ihsan, M. T., Manan, A., Zaid, O., Ul-lah, R. and Abbas, G. (2020), ‘Evaluating the effect of fly ash on the rheological and mechanical performance self-compacted concrete’, p. 10.

Aicha, M. B. (2020), The superplasticizer effect on the rheological and mechanical properties of self-compacting concrete, in ‘New Materials in Civil Engineering’, Elsevier, pp. 315–331.

Al-Hadithi, A. I. and Hilal, N. N. (2016), ‘The possibility of enhancing some properties of self-compacting concrete by adding waste plastic fibers’, Journal of Building Engineering 8, 20–28.

American Concrete Institute (2019), ‘Aci prc-237-07 self-consolidating concrete’, p. 30.

Apostolopoulou, M., Armaghani, D. J., Bakolas, A., Douvika, M. G., Moropoulou, A. and Asteris, P. G. (2019), 'Compressive strength of natural hydraulic lime mortars using soft computing techniques’, Procedia Structural Integrity 17, 914–923.

Armaghani, D. J., Hatzigeorgiou, G. D., Karamani, C., Skentou, A., Zoumpoulaki, I. and Asteris, P. G. (2019), ‘Soft computing-based techniques for concrete beams shear strength’, Procedia Structural Integrity 17, 924–933.

Ashteyat, A. A. and Ismeik, M. (2018), ‘Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks’, Computers and Concrete, 21(1) p. 47.

Asteris, P. G., Apostolopoulou, M., Skentou, A. D. and Moropoulou, A. (2019), ‘Predicting application of artificial neural networks for the prediction of the compressive strength of cement-based mortars’, Computers and Concrete, 24(4) p. 329.

Asteris, P. G., Armaghani, D. J., Hatzigeorgiou, G. D., Karayannis, C. G. and Pilakoutas, K. (2019), ‘Predicting the shear strength of reinforced concrete beams using artificial neural networks’, Computers and Concrete, 24(5) p. 469.

Asteris, P. G., Tsaris, A. K., Cavaleri, L., Repapis, C. C., Papalou, A., Trapani, F. D. and Karypidis, D. F. (2016), ‘Prediction of the fundamental period of infilled RC frame structures using artificial neural networks’, Computational Intelligence and Neuroscience 2016, 1–12.

Aïtcin, P.-C. (1995), ‘Developments in the application of high-performance concretes’, Construction and Building Materials 9(1), 13–17.

Banthia, N. and Onuaguluchi, O. (2021), ‘Recycling scrap tire-derived fibers in concrete’, Transactions of the Indian National Academy of Engineering 7(1), 207–217.

Beaudoin, J. and Odler, I. (2019), Hydration, setting and hardening of portland cement, in ‘Lea's Chemistry of Cement and Concrete’, Elsevier, pp. 157–250.

Cavaleri, L., Chatzarakis, G. E., Trapani, F. D., Douvika, M. G., Roinos, K., Vaxevanidis, N. M. and Asteris, P. G. (2017), ‘Modeling of surface roughness in electro-discharge machining using artificial neural networks’, Advances in Materials Research (South Korea), 6(2) p. 169–184.

Chandra, S. and Bendapudi, K. (2015), ‘Contribution of fly ash to the properties of mortar and concrete’, International Journal of Earth Sciences and Engineering, 04(October 2011) p. 1017–1023.

Chugh, A. (n.d.), ‘Coefficient of determination, adjusted r squared — which metric is better?’.

Deilami, S., Aslani, F. and Elchalakani, M. (2017), ‘Durability assessment of self-compacting concrete with fly ash’, Computers and Concrete, 19(5) p. 489.

Grünewald, S. and Walraven, J. C. (2001), ‘Parameter-study on the influence of steel fibers and coarse aggregate content on the fresh properties of self-compacting concrete’, Cement and Concrete Research 31(12), 1793–1798.

Heniegal, A. M. (2012), ‘Numerical analysis for predicting of self compacting concrete mixtures using artificial neural networks’, JES. Journal of Engineering Sciences 40(6), 1575–1597.

Hinton, G. E., Osindero, S. and Teh, Y.-W. (2006), ‘A fast learning algorithm for deep belief nets’, Neural Computation 18(7), 1527–1554.

Hodhod, O. A., Said, T. E. and Ataya, A. M. (2018), ‘Prediction of creep in concrete using genetic programming hybridized with ann’, Computers and Concrete, 21(5) p. 513.

Hornik, K., Stinchcombe, M. and White, H. (1989), ‘Multilayer feedforward networks are universal approximators’, Neural Networks 2(5), 359–366.

Intezar, T. M., Haque, S., Islam, G. M. S.and Roy, S. and Mushfiq, M. (2019), ‘Properties of self-compacting concrete using fly ash and polypropylene fibre’, 5th International Conference on Engineering Research, Innovation and Education ICERIE 2019.

Islam, G. M. S., Raihan, M. T., Hasan, M. M. and Rashadin, M. (2019), ‘Effect of retarding super-plasticizers on the properties of cement paste, mortar and concrete’, Asian Journal of Civil Engineering 20(4), 591–601.

Joshi, R. C. and Lohtia, R. P. (1997), ‘Fly ash in concrete, production, properties and uses’, Advances in Concrete technology (Volume 2) .

Lee, S. C. and Han, S. W. (2002), ‘Neural-network-based models for generating artificial earthquakes and response spectra’, Computers &amp Structures 80(20-21), 1627–1638.

Lee, S. C., Park, S. K. and Lee, B. H. (2001), ‘Development of the approximate analytical model for the stub-girder system using neural networks’, Computers &amp Structures 79(10), 1013–1025.

Levy, S. M. (2012), Calculations relating to concrete and masonry, in ‘Construction Calculations Manual’, Elsevier, pp. 211–264.

Lourakis, M. (2005), ‘A brief description of the levenberg-marquardt algorithm implemened by levmar’, Matrix, 3(January 2005) p. 2.

Mazloom, M., Soltani, A., karamloo, M., Hassan-loo, A. and Ranjbar, A. (2018), ‘Effects of silica fume, superplasticizer dosage and type of superplasticizer on the properties of normal and self-compacting concrete’, Advances in Materials Research, 7(1) p. 45.

Mazloom, M. and Yoosefi, M. M. (2013), ‘Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks’, Computers and Concrete, 12(3) p. 285.

McCarthy, M. J., Islam, G. M. S., Csetenyi, L. J. and Jones, M. R. (2013), ‘Evaluating test methods for rapidly assessing fly ash reactivity for use in concrete’, World of Coal Ash Conference, April 22-25.

Moghadam, M. A. and Izadifard, R. A. (2019), ‘Experimental investigation on the effect of silica fume and zeolite on mechanical and durability properties of concrete at high temperatures’, SN Applied Sciences 1(7).

Mohammadhosseini, H. and Yatim, J. M. (2017), ‘Evaluation of the effective mechanical properties of concrete composites using industrial waste carpet fiber’, INAE Letters 2(1), 1–12.

Naik, T. R., Kumar, R., Ramme, B. W. and Canpolat, F. (2012), ‘Development of high-strength, economical self-consolidating concrete’, Construction and Building Materials 30, 463–469.

Neville, A. M. (2011), Properties of Concrete (5th ed.). Pearson Education Limited.

Nguyen, T. T., Duy, H. P., Thanh, T. P. and Vu, H. H. (2020), ‘Compressive strength evaluation of fiber-reinforced high-strength self-compacting concrete with artificial intelligence’, Advances in Civil Engineering 2020, 1–12.

Nikoo, M., Sadowski, Ł., Khademi, F. and Nikoo, M. (2017), ‘Determination of damage in reinforced concrete frames with shear walls using self-organizing feature map’, Applied Computational Intelligence and Soft Computing 2017, 1–10.

Okamura, H. and Ouchi, M. (2003), ‘Self-compacting concrete’, Journal of Advanced Concrete Technology 1(1), 5–15.

Raheman, A. and Modani, P. O. (2013), Prediction of Properties of Self Compacting Concrete Using Artificial Neural Network. 3(4) pp. 333–339.

Saini, G. and Vattipalli, U. (2020), ‘Assessing properties of alkali activated GGBS based self-compacting geopolymer concrete using nano-silica’, Case Studies in Construction Materials 12, e00352.

Schmidhuber, J. (2015), ‘Deep learning in neural networks: An overview’, Neural Networks 61, 85–117.

Siddique, R. (2011), ‘Properties of self-compacting concrete containing class f fly ash’, Materials & Design 32(3), 1501–1507.

Siddique, R., Aggarwal, P., Aggarwal, Y. and Gupta, S. M. (2008), ‘Modeling properties of self-compacting concrete: support vector machines approach’, Computers and Concrete, 5(5) p. 461.

Taylor, J. G., ed. (1992), Neural Network Applications, Springer London.

Turk, K., Karatas, M. and Gonen, T. (2012), ‘Effect of fly ash and silica fume on compressive strength, sorptivity and carbonation of SCC’, KSCE Journal of Civil Engineering 17(1), 202–209.

Uysal, M. and Tanyildizi, H. (2011), ‘Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network’, Construction and Building Materials 25(11), 4105–4111.

Yadollahi, M. M., Benli, A. and Demirboğa, R. (2015), ‘Prediction of compressive strength of geopolymer composites using an artificial neural network’, Materials Research Innovations 19(6), 453–458.

Ye, X. W., Jin, T. and Yun, C. B. (2019), ‘A review on deep learning-based structural health monitoring of civil infrastructures’, Smart Structures and Systems, 24(5) p. 567.

How to Cite
Md Akram Hossain, Islam, G. M. S., & Amit Mallick. (2022). Compressive Strength Prediction for Industrial Waste-Based SCC Using Artificial Neural Network. Journal of the Civil Engineering Forum, 9(1), 11-26.