Submit your papers Submit Now
International Peer-Reviewed Journal
For Enquiries: editor@iiardjournals.org
📄 Download Paper

A Computational Model for Concrete Based on Sum of the Mix Ratios, Water-Cement Ratio and the Aggregate Size

John A. T., Robert B. A., Toscanini D. S., and Nelson A

Abstract

This paper presents a computational model for predicting concrete strength based on coarse sum of mix ratios, aggregate size, and water-cement ratio.Laboratory data from John (2024) were used to establish the model, incorporating three different coarse aggregate sizes (7 mm, 18 mm, and 22 mm), three mix ratios by weight (1:3:6, 1:2:4, and 1:1.5:3), and six water-cement ratios (0.3–0.6). After successfully conducting the Design of Experiment (DOE) on the laboratory data obtained from John (2024), the data was analyzed and used to develop a regression model. A multiple regression model was employed to analyse the relationship between these variables and compressive strength. Analysis of Variance (ANOVA) was conducted to measure the implication of the predictive model and validate the reliability of the laboratory data. The proposed model was verified using laboratory data available in existing literature.The final computational model demonstrated a strong predictive capability, with an R² value of 0.959, indicating that the model explains 95.9% of the variation in compressive strength. The model was implemented using Python, and statistical analysis confirmed its reliability and significance (p < 0.05). The findings highlight the potential of computational modeling to optimize concrete mix designs, reduce reliance on laboratory testing, and promote data-driven approaches in civil engineering.

Keywords

Strength Model Regression Aggregate Water-cement ratio Mix

References

Ahmad, S. A., Ahmed, H. U., Rafiq, S. K., & Ahmad, D. A. (2023). Machine learning approach for predicting compressive strength in foam concrete under varying mix designs and curing periods. Smart Construction and Sustainable Cities, 1(1), 16. Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & ElKenawy, E. S. M. (2025). Assessment of compressive strength of eco-concrete reinforced using machine learning tools. Scientific Reports, 15(1), 5017. Day, K. W. (2006). Concrete mix design, quality control and specification. CRC press. Demissew, A. (2022). Comparative Analysis of Selected Concrete Mix Design Methods Based on Cost?Effectiveness. Advances in Civil Engineering, 2022(1), 4240774. Garg, R., Niveda, R., Kushwaha, A., Singh, A. K., Singh, A. K., Deep, M., & Garg, R. (2023). Analysis of vital cost-effective materials and techniques for building construction. In AIP Conference Proceedings (Vol. 2901, No. 1). AIP Publishing. Hooton, R. D., & Bickley, J. A. (2014). Design for durability: The key to improving concrete sustainability. Construction and Building Materials, 67, 422-430. Ikumi, T., Salvador, R. P., & Aguado, A. (2022). Mix proportioning of sprayed concrete: A systematic literature review. Tunnelling and Underground Space Technology, 124, John A. T. (2024) Size Effect of Coarse Aggregate Within Concrete Matrix on the Mechanical Properties of Concrete. Journal of Science and Technology Research 6(3), pp. 243-252. Kumar, P., & Pratap, B. (2024). Feature engineering for predicting compressive strength of high- strength concrete with machine learning models. Asian Journal of Civil Engineering, 25(1), 723-736. Li, X., Gu, X., Xia, X., Madenci, E., Chen, X., & Zhang, Q. (2022). Effect of water-cement ratio and size on tensile damage in hardened cement paste: Insight from peridynamic simulations. Construction and Building Materials, 356, 129256. Ling, S. K., & Kwan, A. K. H. (2015). Adding ground sand to decrease paste volume, increase cohesiveness and improve passing ability of SCC. Construction and Building Materials, 84, 46-53. Lyu, Q., Dai, P., & Chen, A. (2024). Correlations among physical properties of pervious concrete with different aggregate sizes and mix proportions. arXiv preprint arXiv:2406.04372. Mbadike, E. M., & Osadebe, N. N. (2013). Application of Scheffe’s model in optimization of compressive strength of lateritic concrete. Journal of Civil Engineering and Construction Technology, 4(9), 265–274. Mohammed, G. A., & Al-Mashhadi, S. A. A. (2020). Effect of maximum aggregate size on the strength of normal and high strength concrete. Civil Engineering Journal, 6(6), 1155-1165. Nunez, I., Marani, A., Flah, M., & Nehdi, M. L. (2021). Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review. Construction and Building Materials, 310, 125279. Richardson, M. G. (2023). Fundamentals of durable reinforced concrete. CRC Press. Shafighfard, T., Kazemi, F., Asgarkhani, N., & Yoo, D. Y. (2024). Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete. Engineering Applications of Artificial Intelligence, 136, 109053. Shukla, S. K., & Kaul, S. (2025). 1 Advanced Building Materials. In Thermal Evaluation of Indoor Climate and Energy Storage in Buildings (pp. 1-36). CRC Press. Wang, Y., Wu, J., Ma, D., Yang, S., Yin, Q., & Feng, Y. (2022). Effect of aggregate sizedistribution and confining pressure on mechanical property and microstructure of cemented gangue backfill materials. Advanced Powder Technology, 33(8), 103686. Wu, K. R., Chen, B., Yao, W., & Zhang, D. (2001). Effect of coarse aggregate type on mechanical properties of high-performance concrete. Cement and concrete research, 31(10), 1421- Zhou, M., Wu, Z., Ouyang, X., Hu, X., & Shi, C. (2021). Mixture design methods for ultra-high- performance concrete-a review. Cement and Concrete Composites, 124, 104242. Ziolkowski, P. (2023). Computational complexity and its influence on predictive capabilities of machine learning models for concrete mix design. Materials, 16(17), 5956.