Have a personal or library account? Click to login
Data-Driven Prediction Model for the Compressive Strength of High-Volume Fly Ash Concrete using Machine Learning Algorithms Cover

Data-Driven Prediction Model for the Compressive Strength of High-Volume Fly Ash Concrete using Machine Learning Algorithms

Open Access
|Dec 2025

References

  1. Atiş, C. D. 2003. High-volume fly ash concrete with high strength and low drying shrinkage. Journal of materials in civil engineering, 15(2), pp. 153-156.
  2. Azimi-Pou, M., Eskandari-Naddaf, H., Pakzad, A. 2020. Linear and non-linear SVM prediction for fresh properties and compressive strength of high-volume fly ash self-compacting concrete. Construction and Building Materials, 230, p. 117021.
  3. Belalia Douma. O., Boukhatem, B., Ghrici, M., Tagnit-Hamou A. 2017. Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, 28, pp. 707-718.
  4. Breiman, L., 2001. Random forests. Machine learning, 45, pp. 5-32.
  5. Concrete Compressive Strength Data Set, Retrieved at: https://www.kaggle.com/datasets/elikplim/concrete-compressive-strength-data-set
  6. Cortes, C., Vapnik, V. 1995. Support-vector networks. Machine learning, 20, pp. 273-297.
  7. Devi, K.R., Sudhamani, J. 2017. Investigations on high volume fly ash concrete. International Journal for Technological Research in Engineering, 4(12), pp. 2620-2625.
  8. Fan, J., Wu, L., Zhang, F., Cai, H., Wang, X., Lu, X., Xiang, Y. 2018. Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature. Renewable and Sustainable Energy Reviews, 94, pp. 732-747.
  9. Ge, Q., Liu, Z. Q., Sun, H. Y., Lang, D., Shuai, F. X., Shang, Y. Q., Zhang, Y, Q. 2021. Robust design of self-starting drains using Random Forest. Journal of Mountain Science, 18(4), pp. 973-989.
  10. Hashmi, A. F., Ayaz, M., Bilal, A., Shariq, M., Baqi A. 2023. GA-based hybrid ANN optimization approach for the prediction of compressive strength of high-volume fly ash concrete mixes. Asian Journal of Civil Engineering, 24, pp. 1115–1128.
  11. Huang, J., Sabri, M.M.S., Ulrikh, D. V., Ahmad, M., Alsaffar, K. A. M. 2022. Predicting the compressive strength of the cement-fly ash–slag ternary concrete using the Firefly Algorithm (FA) and Random Forest (RF) hybrid machine-learning method. Materials, 15(12), p. 4193.
  12. IS: 10262-2009, Concrete mix proportioning.
  13. IS: 516-2021, Methods of test for strength of concrete
  14. Ji, X., Shang, X., Dahlgren, R.A., Zhang, M. 2017. Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environmental Science and Pollution Research, 24, pp. 16062-16076.
  15. Karthikeyan, R. M., Vellingiri, N. D., Jayalin, D. 2017. Experimental Study on Strength Properties of High Volume Flyash Concrete with Polypropylene Fibre. International Journal of ChemTech Research, 10(8), pp. 284-289.
  16. Keprate. A., Ratnayake, R. C. 2017. Using gradient boosting regressor to predict stress intensity factor of a crack propagating in small bore piping. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1331-1336.
  17. Khursheed. S., Jagan, J., Samui, P., Kumar, S. 2021. Compressive strength prediction of fly ash concrete by using machine learning techniques. Innovative Infrastructure Solutions, 6(149), pp. 1-21.
  18. Kumar, B., Tike, G.K., Nanda, P.K. 2007. Evaluation of properties of high-volume fly-ash concrete for pavements. Journal of Materials in Civil Engineering, 19(10), pp. 906-911.
  19. Kumar, M., Sinha, A.K., Kujur, J. 2021. Mechanical and durability studies on high-volume fly-ash concrete. Structural Concrete, 22, pp. 1036-1049.
  20. Li, C., Geng, H., Zhou, S., Dai, M., Sun, B., Li, F. 2022. Experimental Study on Preparation and Performance of Concrete with Large Content of Fly Ash. Frontiers in Materials, 8, pp. 764820.
  21. Lustosa, P. R., Magalhaes, M.S. 2019. Influence of fly ash on the compressive strength and young’s modulus of concrete. Academic Journal of Civil Engineering, 37(2), pp. 107-111.
  22. Murugan, S.B., Ganesh, G.M., Santhi, A. S. 2014. Regression models for prediction of compressive strength of high-volume fly ash (HVFA) concrete. Arabian Journal for Science and Engineering, 39, pp. 1659-1669.
  23. Natarajan., R. G., Shanmugamoorthy, S., Veerasamy, S., John, A., 2025. Flexural behavior of sustainable high volume fly ash in (HVFA) reinforced concrete beam. Građevinar, 77(01), pp. 13-25.
  24. Nguyen-Sy, T., Wakim, J., To, Q., D, Vu., M, N., Nguyen, T, D., Nguyen. T. T., 2020. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction and Building Materials, 260, pp. 119757.
  25. Okoye, F, N., Singh, N. B., 2016. Mechanical properties of high volume fly ash concrete. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 13(3), pp. 01-08.
  26. Prasad, B. R., Eskandari, H., Reddy, B, V., 2009. Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials, 23(1), pp. 117-128.
  27. Rajeshwari, R., Mandal S., 2019. Prediction of compressive strength of high-volume fly ash concrete using artificial neural network. In Sustainable Construction and Building Materials. Select Proceedings of ICSCBM, 25, pp. 471-483.
  28. Ramachandra, R., Mandal, S., 2020. Prediction of fly ash concrete compressive strengths using soft computing techniques. Computers and Concrete, An International Journal, 25(1), pp. 83-94.
  29. Roushangar, K., Alami, M.T., Shiri, J., Asl, M. M., 2018. Determining discharge coefficient of labyrinth and arced labyrinth weirs using support vector machine. Hydrology research, 49(3), pp. 924-938.
  30. Roychand, R., De Silva, S., Law, D., Setunge S., 2016. Micro and nano engineered high volume ultrafine fly ash cement composite with and without additives. International journal of concrete structures and materials, 10(1), pp. 113-124.
  31. Salain, I. M. A. K., 2019. Using calcium chloride as an accelerator for Portland pozzolan cement concrete compressive strength development. In IOP Conference Series: Materials Science and Engineering, 615(1), p. 012016.
  32. Sevim, U. K., Bilgic, H. H., Cansiz, O. F., Ozturk, M., Atis C. D. 2021. Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques. Construction and Building Materials. 271, p.121584.
  33. Sounthararajan, V.M., Sivakumar A. 2013. Accelerated engineering properties of high and low volume fly ash concretes reinforced with glued steel fibers. Frontiers of Structural and Civil Engineering. 7, pp. 429-445.
  34. Sun, J., Shen, X., Tan, G., Tanner, J., 2019. Compressive strength and hydration characteristics of high-volume fly ash concrete prepared from fly ash. Journal of Thermal Analysis and Calorimetry, 136, pp. 565-580.
  35. Sun, Y., Li G., Zhang N., Chang Q., Xu J., Zhang J., 2021. Development of ensemble learning models to evaluate the strength of coal-grout materials. International Journal of Mining Science and Technology, 31(2), pp. 153-162.
  36. Titiksh A., Wanjari, S. P., 2023. Predicting the Strength of Ultrahigh-Volume Ash Concrete Containing Fly Ash and Bottom Ash as a Substitute for Fine Aggregates. Journal of Materials in Civil Engineering, 35(2), p. 04022416.
  37. Topcu, I.B., Sarıdemir. M., 2008. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41(3), pp. 305-311.
  38. Verma, N. K, Meesala, C. R., Kumar, S., 2023. Developing an ANN prediction model for compressive strength of fly ash-based geopolymer concrete with experimental investigation. Neural Computing and Applications. [accessed 2023 march 25]: [17 p]. https://doi.org/10.1007/s00521-023-08237-1.
  39. Wang, M., Kang, J., Liu, W., Su, J., Li, M., 2022. Research on prediction of compressive strength of fly ash and slag mixed concrete based on machine learning, Plos one, 17(12), pp. 1-18.
  40. Yun. C.M., Rahman, M. R, Kuok, K. K, Mersal, M. E, Liing C N K, Bakri M K B., 2021. Compressive Strength of High-Volume Fly Ash (HVFA) Concrete as a Function of Lime Water and Curing Time. [accessed 2025 September 14]: [21 p]. https://doi.org/10.21203/rs.3.rs-919744/v1.
  41. Zhang. M., 2013. Study on mechanical properties of high-volume fly ash concrete. In Applied Mechanics and Materials, 339, pp. 638-641.
Language: English
Page range: 303 - 310
Submitted on: Aug 15, 2025
|
Accepted on: Sep 1, 2025
|
Published on: Dec 15, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 S. Venkateswaran, S. Vishnuvardhan, Beaula Jasmine Rajamohan, published by University of Oradea, Civil Engineering and Architecture Faculty
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.