A Field-Based Polynomial Model for Estimating the 28-Day Compressive Strength of Concrete from Slump, Temperature, and Density

Document Type : Original Article

Authors

Faculty of Engineering, University of Bojnord, Bojnord, Iran

Abstract

Concrete is one of the most widely used materials in the construction industry. Quality control of ready-mixed concrete is important due to the many factors that influence its quality from the production phase to placement and curing. Slump, temperature, density, and compressive strength are commonly used tests for evaluating the quality of concrete. Since the compressive strength test requires 28 days, predicting it using site-specific in situ tests can help engineers assess on-site conditions. To develop a prediction model for compressive strength, 92 samples of ready-mixed concrete were collected in collaboration with the National Standard Organization of Iran in accordance with the relevant national standards. The samples were divided into two groups of training and testing datasets, including 83 and nine randomly selected samples, respectively. According to the proposed model, compressive strength can be predicted from a twelve-term polynomial function in terms of powers of density, slump, and temperature, and their products. The performance of the model was assessed by calculating statistical indicators for the training samples as well as external validation with testing samples not used during model development. The model predicted the 28-day compressive strength with a mean absolute error (MAE) of 0.849 MPa and a coefficient of determination (R²) of 0.744 for new samples.

Keywords

Main Subjects

References

  1. Skrzypczak, I., Kokoszka, W., Zięba, J., Leśniak, A., Bajno, D., Bednarz, L. A Proposal of a Method for Ready-Mixed Concrete Quality Assessment Based on Statistical-Fuzzy Approach. Materials, 2020; 13: 5674. doi:10.3390/ma13245674.
  2. Tattersall, G. H., Banfill, P. The rheology of fresh concrete. 1st ed. London (UK): Pitman Publishing 1983.
  3. Wang, Z., Sun, T., Sun, Y., Liu, N. Evaluating the strength properties of high-performance concrete in the form of ensemble and hybrid models using deep learning techniques. Scientific Reports, 2025; 15: 25453. doi:10.1038/s41598-025-10860-y.
  4. Mahboub, K. C., Cutshaw, Q. A. Effects of Fresh Concrete Temperature and Mixing Time on Compressive Strength of Concrete. ACI Materials Journal, 98: doi:10.14359/10161.
  5. Kaleta-Jurowska, A., Jurowski, K. The Influence of Ambient Temperature on High Performance Concrete Properties. Materials, 2020; 13: 4646. doi:10.3390/ma13204646.
  6. Neville, A. M. Properties of concrete. 4th ed. Longman; 1995.
  7. K. Mehta, P. J. M. Concrete: Microstructure, properties, and materials. 3rd ed. Columbus (OH): McGraw-Hill; 2006.
  8. Iffat, S. Relation Between Density and Compressive Strength of Hardened Concrete. Challenge Journal of Concrete Research Letters; Vol 6, No 4 (2015), 2017; doi:10.20528/cjcrl.
  9. Sánchez-Mendieta, C., Galán-Díaz, J. J., Martinez-Lage, I. Relationships between density, porosity, compressive strength and permeability in porous concretes: Optimization of properties through control of the water-cement ratio and aggregate type. Journal of Building Engineering, 2024; 97: 110858. doi:10.1016/j.jobe.2024.110858.
  10. Nguyen, D.-L., Phan, T.-D. Enhanced prediction of compressive strength in high-strength concrete using a hybrid adaptive boosting - particle swarm optimization. Asian Journal of Civil Engineering, 2025; 26: 1059–1076. doi:10.1007/s42107-024-01233-3.
  11. Mahajan, A., Sharma, P. K. Machine learning-based prediction of high-strength concrete compressive strength incorporating limestone aggregates using ensemble and pruned tree models. Asian Journal of Civil Engineering, 2025; 26: 4595–4613. doi:10.1007/s42107-025-01445-1.
  12. Brahmeswari, L. C., Rao, B. D. V. C. M. Ultra-high performance concrete compressive strength prediction using machine learning boosting algorithms. Asian Journal of Civil Engineering, 2025; 26: 5139–5154. doi:10.1007/s42107-025-01476-8.
  13. Zviazhynski, B., White, C., Lees, J. M., Conduit, G. J. Predicting concrete strength using machine learning and fresh-state measurements. Data-Centric Engineering, 2025; 6: e37. doi:10.1017/dce.2025.10018.
  14. Mostofinejad, D., Bahmani, H., Eshaghi-Milasi, S., Nozhati, M. Empirical Relationships for Prediction of Mechanical Properties of High-Strength Concrete. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023; 47: 315–332. doi:10.1007/s40996-022-01023-4.
  15. Iranian National Standardization Organization (INSO). 11268: Concrete – Measuring of temperature of freshly mixed hydraulic-cement concrete – Test method (2nd rev.). Tehran (IR): INSO; 2021.
  16. Iranian National Standardization Organization (INSO). 3201-1: Fresh concrete – Part 1: Sampling. Tehran (IR): INSO; 2010.
  17. Iranian National Standardization Organization (INSO). 3203-2: Fresh concrete – Part 2: Determining the consistency by slump – Test method (3rd rev.). Tehran (IR): INSO; 2023.
  18. Iranian National Standardization Organization (INSO). 3203-6: Fresh concrete – Part 6: Density – Test method (2nd rev.). Tehran (IR): INSO; 2025.
  19. Iranian National Standardization Organization (INSO). 1608-2: Hardened concrete – Part 2: Making and curing specimens for strength tests. Tehran (IR): INSO; 2023.
  20. ASTM International. C1064/C1064M-17: Standard test method for temperature of freshly mixed hydraulic-cement concrete. West Conshohocken (PA): ASTM; 2017. doi:10.1520/C1064_C1064M-17.
  21. European Committee for Standardization (CEN). EN 12350-2: Testing fresh concrete – Part 2: Slump test. Brussels (BE): EN; 2019.
  22. European Committee for Standardization (CEN). EN 12350-6: Testing fresh concrete – Part 6: Density. London (UK): EN; 2019.
  23. European Committee for Standardization (CEN). EN 12390-2: Testing hardened concrete – Part 2: Making and curing specimens for strength tests. Brussels (BE): EN; 2019.
  24. International Organization for Standardization (ISO). 1920-1: Testing of concrete – Part 1: Sampling of fresh concrete. Geneva (SW): ISO; 2004.
  25. Shapiro, S. S., Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika, 1965; 52: 591–611. doi:10.2307/2333709.
  26. Montgomery, D. C., Runger, G. C. Applied statistics and probability for engineers. 6th ed. Hoboken (NJ): John wiley and sons; 2014.
  27. Garbade, K. D. The initialization problem in variable parameter regression [PhD Thesis]. New York (NY): New York University, Graduate School of Business Administration; 1975.
  28. ASTM International. C192/C192M-24: Standard practice for making and curing concrete test specimens in the laboratory. West Conshohocken (PA): ASTM; 2024. doi:10.1520/C0192_C0192M-26.
Civil Engineering and Applied Solutions
Volume 3, Issue 1
Issue in Progress
January 2027
Pages 51-60

Files

History

  • Receive Date: 22 February 2026
  • Revise Date: 31 March 2026
  • Accept Date: 12 May 2026
  • First Publish Date: 18 May 2026

Share

How to cite

Statistics