Prediction of Sustainable Concrete Strength Incorporating Fly Ash and Recycled Aggregates Using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been used to predict the compressive strength of sustainable concrete as an alternative to large laboratory experiments․ Sustainable concrete mixes were designed by replacing natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) at varying percentages ranging from 0% to 100%․ Ordinary Portland cement was replaced with fly ash at the rate of 0%‚ 5%‚ 10%‚ 15%‚ 20%‚ 25%‚ 30%‚ 35% and 40% to obtain samples․ Twenty-five concrete mixes were tested for compressive strength at 3‚ 14 and 28 days․

In contrast‚ for moderate fly ash replacement levels (10-20 percent)‚ strength increased at later ages due to pozzolanic reactions․ At high fly ash replacement levels‚ strength development was delayed during the curing period․ Increasing RCA content decreased compressive strength because of the presence of unhydrated mortar‚ greater porosity‚ lower bonding between the aggregate and paste matrix‚ and greater water absorption compared to normal aggregate․

An ANN model has been developed to predict the concrete strength‚ using five parameters as input (NCA content‚ RCA content‚ cement content‚ fly ash content and curing age) and the concrete compressive strength as an output parameter․ Based on the seventy-five experimental results‚ the developed model is highly accurate‚ with R²-value of 0․9935 and RMSE (Root Mean Square Error) of 0․6465 MPa․ These results show that the ANN can successfully predict the sustainable concrete compressive strength․ The proposed model is very useful for mix proportion optimization with considerable reductions in the time‚ cost‚ and effort of experimental studies․

Artificial Neural Network (ANN), Sustainable Concrete, Recycled Coarse Aggregate, Fly Ash and Compressive Strength

1. F. Khalid, A.-u.-R. Khan, and S. Fareed, “Estimation of parameters for constitutive modelling of recycled aggregate concrete using artificial neural networks,” Engineering Computations, vol. 42, no. 4, pp. 1471–1501, Apr. 2025. DOI: https://doi.org/10.1108/EC-07-2024-0679 [Google Scholar] [Crossref]

2. I.-C. Yeh, “Modeling of strength of high-performance concrete using artificial neural networks,” Cement and Concrete Research, vol. 28, no. 12, pp. 1797–1808, 1998. DOI: https://doi.org/10.1016/S0008-8846(98)00165-3 [Google Scholar] [Crossref]

3. I.-C. Yeh, “Analysis of strength of concrete using design of experiments and neural networks,” Journal of Materials in Civil Engineering, vol. 18, no. 4, pp. 597–604, 2006. DOI: https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597) [Google Scholar] [Crossref]

4. B. K. Prasad, H. Eskandari, and B. V. V. Reddy, “Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN,” Construction and Building Materials, vol. 23, no. 1, pp. 117–128, 2009. DOI: https://doi.org/10.1016/j.conbuildmat.2008.01.014 [Google Scholar] [Crossref]

5. H. Naderpour, A. H. Rafiean, and P. Fakharian, “Compressive strength prediction of environmentally friendly concrete using artificial neural networks,” Journal of Building Engineering, vol. 16, pp. 213–219, 2018. DOI: https://doi.org/10.1016/j.jobe.2018.01.007 [Google Scholar] [Crossref]

6. A. Behnood and E. Golafshani, “Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves,” Journal of Cleaner Production, vol. 202, pp. 54–64, 2018. DOI: https://doi.org/10.1016/j.jclepro.2018.08.065 [Google Scholar] [Crossref]

7. Y. Deng, T. Shi, and Y. Zhang, “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks,” Construction and Building Materials, vol. 40, pp. 1200–1206, 2013. DOI: https://doi.org/10.1016/j.conbuildmat.2012.11.042 [Google Scholar] [Crossref]

8. J. Abellan-Garcia, J. Fernández-Gómez, M. Iqbal Khan, Y. M. Abbas, and C. Pacheco-Bustos, “ANN approach to evaluate the effects of supplementary cementitious materials on the compressive strength of recycled aggregate concrete,” Construction and Building Materials, vol. 402, p. 132992, Oct. 2023. DOI: https://doi.org/10.1016/j.conbuildmat.2023.132992 [Google Scholar] [Crossref]

9. M. J. Moradi, M. Khaleghi, J. Salimi, V. Farhangi, and A. M. Ramezanianpour, “Predicting the compressive strength of concrete containing metakaolin with different properties using ANN,” Measurement, vol. 183, p. 109790, 2021. DOI: https://doi.org/10.1016/j.measurement.2021.109790 [Google Scholar] [Crossref]

10. W. Liu, H. Mo, and C. K. Lee, “Compressive strength prediction for SCM-based concretes using multiple machine learning models and datasets approach,” Results in Engineering, vol. 30, p. 110262, 2026. DOI: https://doi.org/10.1016/j.rineng.2026.110262 [Google Scholar] [Crossref]

11. G. Pazouki, N. Saeed, Z. Tao, and W.-H. Kang, “A predictive ML framework for 28-day compressive strength using early-age–driven models for concrete with sustainable supplementary cementitious materials,” Materials Today Communications, vol. 52, p. 115052, 2026. DOI: https://doi.org/10.1016/j.mtcomm.2026.115052 [Google Scholar] [Crossref]

12. J. Xiao, J. Li, and C. Zhang, “Mechanical properties of recycled aggregate concrete under uniaxial loading,” Cement and Concrete Research, vol. 35, no. 6, pp. 1187–1194, 2005. DOI: https://doi.org/10.1016/j.cemconres.2004.09.020 [Google Scholar] [Crossref]

13. S. C. Kou and C. S. Poon, “Properties of self-compacting concrete prepared with coarse and fine recycled concrete aggregates,” Cement and Concrete Composites, vol. 31, no. 9, pp. 622–627, 2009. DOI: https://doi.org/10.1016/j.cemconcomp.2009.06.005 [Google Scholar] [Crossref]

14. Y. Limbachiya, T. Leelawat, and R. K. Dhir, “Use of recycled concrete aggregate in high-strength concrete,” Materials and Structures, vol. 33, no. 9, pp. 574–580, 2000. DOI: https://doi.org/10.1007/BF02480538 [Google Scholar] [Crossref]

15. V. Corinaldesi and G. Moriconi, “Behavior of cementitious mortars containing different kinds of recycled aggregate,” Construction and Building Materials, vol. 23, no. 1, pp. 289–294, 2009. DOI: https://doi.org/10.1016/j.conbuildmat.2007.12.006 [Google Scholar] [Crossref]

16. V. W. Y. Tam, X. F. Gao, and C. M. Tam, “Microstructural analysis of recycled aggregate concrete produced from two-stage mixing approach,” Cement and Concrete Research, vol. 35, no. 6, pp. 1195–1203, 2005. DOI: https://doi.org/10.1016/j.cemconres.2004.10.025 [Google Scholar] [Crossref]

17. P. Dinakar, K. G. Babu, and M. Santhanam, “Durability properties of high-volume fly ash self-compacting concretes,” Cement and Concrete Composites, vol. 30, no. 10, pp. 880–886, 2008. DOI: https://doi.org/10.1016/j.cemconcomp.2008.08.005 [Google Scholar] [Crossref]

18. P. Chindaprasirt, C. Jaturapitakkul, and T. Sinsiri, “Effect of fly ash fineness on compressive strength and pore size of blended cement paste,” Cement and Concrete Composites, vol. 27, no. 4, pp. 425–428, 2005. DOI: https://doi.org/10.1016/j.cemconcomp.2004.07.003 [Google Scholar] [Crossref]

19. E. Güneyisi, M. Gesoğlu, and H. Özbay, “Strength and drying shrinkage properties of self-compacting concretes incorporating multi-system blended mineral admixtures,” Construction and Building Materials, vol. 24, no. 10, pp. 1878–1887, 2010. DOI: https://doi.org/10.1016/j.conbuildmat.2010.04.015 [Google Scholar] [Crossref]

20. G. Xu and X. Shi, “Characteristics and applications of fly ash as a sustainable construction material: A state-of-the-art review,” Resources, Conservation and Recycling, vol. 136, pp. 95–109, 2018. DOI: https://doi.org/10.1016/j.resconrec.2018.04.010 [Google Scholar] [Crossref]

21. J. Miklečić and V. Jirouš-Rajković, “Effectiveness of finishes in protecting wood from liquid water and water vapor,” Journal of Building Engineering, vol. 43, p. 102621, Nov. 2021. DOI: https://doi.org/10.1016/j.jobe.2021.102621 [Google Scholar] [Crossref]

22. N. Cristelo, V. M. C. F. Cunha, A. Topa Gomes, N. Araújo, T. Miranda, and M. de Lurdes Lopes, “Influence of fibre reinforcement on the post-cracking behaviour of a cement-stabilised sandy-clay subjected to indirect tensile stress,” Construction and Building Materials, vol. 138, pp. 163–173, May 2017. DOI: https://doi.org/10.1016/j.conbuildmat.2017.02.010 [Google Scholar] [Crossref]

23. S. X. Ruan, J. Zhang, and X. Wang, “Predicting failure process of precast deck joints using physics-guided LSTM model,” Structures, vol. 59, p. 105732, Jan. 2024. DOI: https://doi.org/10.1016/j.istruc.2023.105732 [Google Scholar] [Crossref]

24. J. Wang, L. He, X. Wu, and L. Zhang, “Influence of nanoparticles on age-associated mechanical properties of cement asphalt mortar,” Construction and Building Materials, vol. 301, p. 124033, Sep. 2021. DOI: https://doi.org/10.1016/j.conbuildmat.2021.124033 [Google Scholar] [Crossref]

25. J. N. Akhtar, R. A. Khan, R. A. Khan, M. N. Akhtar, and B. S. Thomas, “A comparative study of strength and durability characteristics of concrete and mortar admixture by bacterial calcite precipitation: A review,” Materials Today: Proceedings, 2023. DOI: https://doi.org/10.1016/j.matpr.2023.03.490 [Google Scholar] [Crossref]

26. S. Spadea, I. Farina, A. Carrafiello, and F. Fraternali, “Recycled nylon fibers as cement mortar reinforcement,” Construction and Building Materials, vol. 80, pp. 200–209, Apr. 2015. DOI: https://doi.org/10.1016/j.conbuildmat.2015.01.075 [Google Scholar] [Crossref]

27. M. Nikoo, F. Torabian Moghadam, and Ł. Sadowski, “Prediction of concrete compressive strength by evolutionary artificial neural networks,” Advances in Materials Science and Engineering, vol. 2015, Article ID 849126, 2015. DOI: https://doi.org/10.1155/2015/849126 [Google Scholar] [Crossref]

28. M. G. Asteris and K. G. Kolovos, “Self-compacting concrete strength prediction using surrogate models,” Neural Computing and Applications, vol. 31, pp. 409–424, 2019. DOI: https://doi.org/10.1007/s00521-017-3007-7 [Google Scholar] [Crossref]

29. A. Mardani-Aghabaglou, M. Tuyan, and K. Ramyar, “Mechanical and durability performance of concrete incorporating fine recycled concrete aggregates,” Construction and Building Materials, vol. 64, pp. 477–488, 2014. DOI: https://doi.org/10.1016/j.conbuildmat.2014.04.017 [Google Scholar] [Crossref]

30. C. S. Poon, Z. H. Shui, and L. Lam, “Compressive behavior of fiber reinforced high-performance concrete subjected to elevated temperatures,” Cement and Concrete Research, vol. 34, no. 12, pp. 2215–2222, 2004. DOI: https://doi.org/10.1016/j.cemconres.2004.02.011 [Google Scholar] [Crossref]

31. K. Ganesan, K. Rajagopal, and K. Thangavel, “Evaluation of bagasse ash as supplementary cementitious material,” Cement and Concrete Composites, vol. 29, no. 6, pp. 515–524, 2007. DOI: https://doi.org/10.1016/j.cemconcomp.2007.03.001 [Google Scholar] [Crossref]

• Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
• Improving Sliding Mode Control with Chattering Reduction using Fuzzy Based Technique
• Cooking Quality, Fasting Blood Glucose, Glycemic Index and Load of High–Fiber Noodles Made from Wheat, Tiger Nut Residue and Cassava Flour Blends
• Matrix Rhythm Therapy Versus Interferential Therapy Combined with Lumbar Stabilization Exercises in Chronic Non-Specific Low Back Pain: A Randomized Comparative Trial
• Formulation and Sensory Evaluation of Functional Cake Prepared from Sweet Potato Powder