Transactions on Machine Intelligence

Transactions on Machine Intelligence

Comparative Evaluation of Support Vector Machine and Multiple Linear Regression for Predicting the Compressive Strength of Fly Ash-Containing Concrete

Document Type : Original Article

Authors
S. R. SalimBahrami 1
M. Imani 2
1 Assistant Professor, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Technical and Vocational University (TVU), Tehran, Iran
2 B.Sc. Student, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Technical and Vocational University (TVU), Tehran, Iran
10.22034/tmi.2026.250984
Abstract
Fly ash-containing concrete has attracted considerable attention as an effective approach to sustainable concrete development due to its potential to reduce cement consumption, reuse an industrial by-product, and mitigate the environmental impacts associated with cement production. However, the complex and nonlinear relationships among mix-design constituents, concrete age, and compressive strength make accurate strength prediction challenging. This study investigates and compares the performance of a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and Multiple Linear Regression (MLR) for predicting the compressive strength of fly ash-containing concrete. For this purpose, a dataset comprising 471 laboratory observations with seven input variables representing mix-design parameters and concrete age was employed. The data were divided into training and testing sets, and five-fold cross-validation was performed to assess the stability and generalizability of the models. The SVM hyperparameters were optimized using grid search. The results demonstrated that SVM outperformed MLR in modeling the complex relationships governing compressive strength. Specifically, during the training phase, SVM achieved a correlation coefficient of 0.988, compared with 0.814 for MLR. The cross-validation results further confirmed the superior performance and greater stability of the SVM model. These findings highlight the potential of nonlinear machine-learning models for estimating compressive strength and supporting the design of sustainable fly ash-containing concrete.
Keywords
Fly ash-containing concrete
compressive strength
five-fold cross-validation
Support Vector Machine
Multiple Linear Regression
 
[1]      Akbulut, Z. F., Yavuz, D., Tawfik, T. A., Smarzewski, P., & Guler, S. (2024). Enhancing concrete performance through sustainable utilization of class-C and class-F fly ash: A comprehensive review. Sustainability, 16(12), 4905. https://doi.org/10.3390/su16124905
[2]      Orozco, C., Babel, S., Tangtermsirikul, S., & Sugiyama, T. (2024). Comparison of environmental impacts of fly ash and slag as cement replacement materials for mass concrete and the impact of transportation. Sustainable Materials and Technologies, 39, e00796. https://doi.org/10.1016/j.susmat.2023.e00796
[3]      Orozco, C., Tangtermsirikul, S., Sugiyama, T., & Babel, S. (2023). Examining the endpoint impacts, challenges, and opportunities of fly ash utilization for sustainable concrete construction. Scientific Reports, 13(1), 18254. https://doi.org/10.1038/s41598-023-45632-z
[4]      Sharma, V., Dash, S., & Gupta, P. (2024). Comprehensive review of fly ash: Environmental impact and applications. Environmental Quality Management, 34(2), e22338. https://doi.org/10.1002/tqem.22338
[5]      Rathnayaka, M., Karunasinghe, D., Gunasekara, C., Wijesundara, K., Lokuge, W., & Law, D. W. (2024). Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review. Construction and Building Materials, 419, 135519. https://doi.org/10.1016/j.conbuildmat.2024.135519
[6]      Azimi-Pour, 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, 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021
[7]      Song, Y., et al. (2021). Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches. Applied Sciences, 12(1), 361. https://doi.org/10.3390/app12010361
[8]      Li, H., Chung, H., Li, Z., & Li, W. (2024). Compressive strength prediction of fly ash-based concrete using single and hybrid machine learning models. Buildings, 14(10), 3299. https://doi.org/10.3390/buildings14103299
[9]      Kumar, S., Kumar, R., Rai, B., & Samui, P. (2024). Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques. Construction and Building Materials, 438, 136933. https://doi.org/10.1016/j.conbuildmat.2024.136933
[10]   Dou, B., et al. (2023). Machine learning methods for small data challenges in molecular science. Chemical Reviews, 123(13), 8736. https://doi.org/10.1021/acs.chemrev.3c00189
[11]   Umutoni, L., & Samadi, V. (2024). Application of machine learning approaches in supporting irrigation decision making: A review. Agricultural Water Management, 294, 108710. https://doi.org/10.1016/j.agwat.2024.108710
[12]   Aalami, M. T., Hejabi, N., Nourani, V., & Saghebian, S. (2019). Investigation of artificial intelligence approaches capability in predicting the wastewater treatment plant performance (case study: Tabriz wastewater treatment plant). Amirkabir Journal of Civil Engineering. https://doi.org/10.22060/ceej.2019.16757.6334
[13]   Pham, V.-N. (2025). Practical formulation for estimating the compressive strength of self-compacting fly ash concrete using gene-expression programming. Journal of Science and Technology in Civil Engineering (JSTCE-HUCE), 19(3), 98–109. https://doi.org/10.31814/stce.huce2025-19(3)-08
[14]   Pham, V.-N., & Nguyen, V.-Q. (2024). Effects of alkaline activators and other factors on the properties of geopolymer concrete using industrial wastes based on GEP-based models. European Journal of Environmental and Civil Engineering, 28(16), 3770–3792. https://doi.org/10.1080/19648189.2024.2357677
[15]   Pham, V. N. (2025). Fly ash concrete (Version 1) [Data set]. Mendeley Data. https://doi.org/10.17632/c8g7smdgzs.1
Transactions on Machine Intelligence
Volume 9, Issue 1
Winter 2026
Pages 74-83

  • Receive Date 14 January 2026
  • Revise Date 01 March 2026
  • Accept Date 18 March 2026