Innovative Impact of Chemical Additives Combination on Long-Term Compressive Strength of Concrete Using Machine Learning Algorithms

Subject Areas : Information Technology in Engineering Design (ITED) Journal

seyed iman Ghafoorian Heidri ¹ , Majid Safehian ² , shabnam shadroo ³ , FRAMARZ MOODI ⁴

1 -
2 - Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of computer science, Faculty of engineering, Mashhad Azad university, Iran
4 - Associate Professor Concrete Technology and Durability Research Center, Amirkabir University of Technology, Tehran, Iran

Received: 2024-12-15 Accepted : 2025-02-15 Published : 2025-08-02

Keywords: Chemical Additives, Compressive Strength Prediction, Machine Learning, NARX, RBF, RF,

Abstract :

The combination of chemical additives is critical in concrete production, as an understanding of their mechanical properties ensures the safety and durability of structures. Compressive strength, a fundamental property of concrete, directly influences structural performance and longevity. This study investigates the long-term effects of chemical additives on compressive strength, utilizing a dataset of 7,845 samples tested at ages ranging from 3 days to 3 years, thereby providing a more comprehensive dataset than previous research.Various machine learning models including Nonlinear Autoregressive Exogenous, Random Forest, Radial Basis Function, Multilayer Perceptron, Decision Tree, and Support Vector Regression—were employed for predictive analysis.The Nonlinear Autoregressive Exogenous model demonstrated the highest performance, achieving an R² value of 0.9932 and a NMSE of 18.97. The results indicated reductions in compressive strength after one and three years compared to the 90-day mark, which has implications for load-bearing capacity and service life. These findings underscore the necessity for revising current design standards to address long-term strength reductions.This study illustrates the significance of advanced machine learning techniques in enhancing predictive accuracy and addressing economic and environmental challenges. It provides valuable insights for optimizing concrete mixtures to create more sustainable and reliable structures.

References:

[1] K. Bedada, A. Nyabuto, I. Kınotı, and J. Marangu, “Review on advances in bio-based admixtures for concrete,” J. Sustain. Constr. Mater. Technol., vol. 8, no. 4, pp. 344–367, 2023.
[2] A. Harapin, M. Jurišić, N. Bebek, and M. Sunara, “Long-Term Effects in Structures: Background and Recent Developments,” Applied Sciences (Switzerland), vol. 14, no. 6. Multidisciplinary Digital Publishing Institute (MDPI), 2024. doi: 10.3390/app14062352.
[3] M. R. Garcez, A. B. Rohden, and L. G. G. de Godoy, “The role of concrete compressive strength on the service life and life cycle of a RC structure: Case study,” J. Clean. Prod., vol. 172, pp. 27–38, 2018, doi: 10.1016/j.jclepro.2017.10.153.
[4] M. Husem and S. Gozutok, “The effects of low temperature curing on the compressive strength of ordinary and high performance concrete,” Constr. Build. Mater., vol. 19, no. 1, pp. 49–53, Feb. 2005, doi: 10.1016/j.conbuildmat.2004.04.033.
[5] J. Bashir, S. A. Rather, F. A. Laherwal, and V. Garg, “IMPACT OF COLD WEATHER ON WORKABILITY, COMPRESSIVE AND FLEXURAL STRENGTH OF CONCRETE,” Int. Res. J. Eng. Technol., 2019, [Online]. Available: www.irjet.net
[6] V. Rathakrishnan, S. Bt. Beddu, and A. N. Ahmed, “Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms,” Sci. Rep., vol. 12, no. 1, p. 9539, 2022, doi: 10.1038/s41598-022-12890-2.
[7] X. Dong, Y. Liu, and J. Dai, “Application of Fully Connected Neural Network‐Based PyTorch in Concrete Compressive Strength Prediction,” Adv. Civ. Eng., vol. 2024, no. 1, p. 8048645, 2024.
[8] P. Chopra, R. K. Sharma, M. Kumar, and T. Chopra, “Comparison of machine learning techniques for the prediction of compressive strength of concrete,” Adv. Civ. Eng., vol. 2018, no. 1, p. 5481705, 2018.
[9] X. Hu, B. Li, Y. Mo, and O. Alselwi, “Progress in artificial intelligence-based prediction of concrete performance,” J. Adv. Concr. Technol., vol. 19, no. 8, pp. 924–936, 2021, doi: 10.3151/jact.19.924.
[10] H. Naderpour, A. H. Rafiean, and P. Fakharian, “Compressive strength prediction of environmentally friendly concrete using artificial neural networks,” J. Build. Eng., vol. 16, no. January, pp. 213–219, 2018, doi: 10.1016/j.jobe.2018.01.007.
[11] K. L. Chung, L. Wang, M. Ghannam, M. Guan, and J. Luo, “Prediction of concrete compressive strength based on early-age effective conductivity measurement,” J. Build. Eng., vol. 35, p. 101998, 2021, doi: https://doi.org/10.1016/j.jobe.2020.101998.
[12] A. Mohammed, L. Burhan, K. Ghafor, W. Sarwar, and W. Mahmood, “Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers,” Neural Comput. Appl., vol. 33, no. 13, pp. 7851–7873, Jul. 2021, doi: 10.1007/s00521-020-05525-y.
[13] G. G. Agbi, O. Z. Tachere, and H. O. Juwah, “Evaluation of the impact of chemical admixtures on the compressive strength properties of concrete,” Appl. J. Phys. Sci., vol. 3, pp. 72–80, 2021.
[14] M. Nithurshan and Y. Elakneswaran, “A systematic review and assessment of concrete strength prediction models,” Case Stud. Constr. Mater., vol. 18, p. e01830, 2023, doi: https://doi.org/10.1016/j.cscm.2023.e01830.
[15] ACI Committee 318, “Building Code Requirements for Structural Concrete ( ACI 318-19 ) Commentary on Building Code Requirements for Structural Concrete ( ACI 318R-19 ) Reported by ACI Committee 318,” 318-19 Build. Code Requir. Struct. Concr. Comment., 2019.
[16] M. I. Khan et al., “Prediction of compressive strength of cementitious grouts for semi-flexible pavement application using machine learning approach,” Case Stud. Constr. Mater., vol. 19, no. February, p. e02370, 2023, doi: 10.1016/j.cscm.2023.e02370.
[17] M. Al-Gburi and S. A. Yusuf, “Investigation of the effect of mineral additives on concrete strength using ANN,” Asian J. Civ. Eng., vol. 23, no. 3, pp. 405–414, Apr. 2022, doi: 10.1007/s42107-022-00431-1.
[18] H. Yaprak, A. Karaci, and I. Demir, “Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks,” Neural Comput. Appl., vol. 22, no. 1, pp. 133–141, 2013, doi: 10.1007/s00521-011-0671-x.
[19] J. Yao et al., “Mix design of equal strength high volume fly ash concrete with artificial neural network,” Case Stud. Constr. Mater., vol. 19, no. June, p. e02294, 2023, doi: 10.1016/j.cscm.2023.e02294.
[20] B. Vidivelli, “PREDICTION OF COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE CONTAINING INDUSTRIAL BY PRODUCTS USING ARTIFICIAL NEURAL NETWORKS,” Int. J. Civ. Eng. Technol., vol. 7, no. 2, pp. 302–314, [Online]. Available: http://www.iaeme.com/IJCIET/index.asp302http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=2JournalImpactFactor.
[21] 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.
[22] M. Sarıdemir, İ. B. Topçu, F. Özcan, and M. H. Severcan, “Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic,” Constr. Build. Mater., vol. 23, no. 3, pp. 1279–1286, 2009, doi: https://doi.org/10.1016/j.conbuildmat.2008.07.021.
[23] M. R. Garcez, A. B. Rohden, and L. G. Graupner de Godoy, “The role of concrete compressive strength on the service life and life cycle of a RC structure: Case study,” J. Clean. Prod., vol. 172, pp. 27–38, Jan. 2018, doi: 10.1016/j.jclepro.2017.10.153.
[24] V. Çetin and O. Yıldız, “A comprehensive review on data preprocessing techniques in data analysis,” Pamukkale Univ. J. Eng. Sci., vol. 28, no. 2, pp. 299–312, 2022, doi: 10.5505/pajes.2021.62687.
[25] I. Nunez, A. Marani, M. Flah, and M. L. Nehdi, “Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review,” Constr. Build. Mater., vol. 310, p. 125279, 2021, doi: https://doi.org/10.1016/j.conbuildmat.2021.125279.
[26] M. Shariati, D. J. Armaghani, M. Khandelwal, J. Zhou, and M. Khorami, “Assessment of Longstanding Effects of Fly Ash and Silica Fume on the Compressive Strength of Concrete Using Extreme Learning Machine and Artificial Neural Network,” J. Adv. Eng. Comput., vol. 5, no. 1, p. 50, Mar. 2021, doi: 10.25073/jaec.202151.308.
[27] L. Jin, W. Yu, D. Li, and X. Du, “Numerical and theoretical investigation on the size effect of concrete compressive strength considering the maximum aggregate size,” Int. J. Mech. Sci., vol. 192, p. 106130, 2021.
[28] A. International, “Standard Specification for Concrete Aggregates- ASTM C33 / C33M-23,” 2023. [Online]. Available: 10.1520/C0033_C0033M-23.
[29] N. Zeminian, G. Guarino, and Q. Xu, “Chemical admixtures for the optimization of the AAC production,” Ce/papers, vol. 2, no. 4, pp. 235–240, 2018.
[30] A. International, “Standard Practice for Making and Curing Concrete Test Specimens in the Laboratory: ASTM C192/C192M-24,” West Conshohocken, PA, 2024, 2024. doi: 10.1520/C0192_C0192M-24.
[31] J. Pachouly, S. Ahirrao, K. Kotecha, G. Selvachandran, and A. Abraham, “A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools,” Eng. Appl. Artif. Intell., vol. 111, no. November 2021, p. 104773, 2022, doi: 10.1016/j.engappai.2022.104773.
[32] A. L. Kaminsky, Y. Wang, and K. Pant, “An efficient batch K-fold cross-validation voronoi adaptive sampling technique for global surrogate modeling,” J. Mech. Des., vol. 143, no. 1, p. 11706, 2021.
[33] K. Phinzi, D. Abriha, and S. Szabó, “Classification efficacy using k-fold cross-validation and bootstrapping resampling techniques on the example of mapping complex gully systems,” Remote Sens., vol. 13, no. 15, p. 2980, 2021.
[34] J. S. Chou and A. D. Pham, “Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength,” Constr. Build. Mater., vol. 49, pp. 554–563, 2013, doi: 10.1016/j.conbuildmat.2013.08.078.
[35] H. Song et al., “Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms,” Constr. Build. Mater., vol. 308, p. 125021, 2021.
[36] “Anaconda, Anaconda,” World’s Most Pop. Data Sci. Platf., 2019.
[37] T. W. M. P. D. S. Platform, “Matlab R2021a,” World’s Most Pop. Data Sci. Platf., [Online]. Available: The World’s Most Popular Data Science Platform.
[38] X. Hu, B. Li, Y. Mo, and O. Alselwi, “Progress in artificial intelligence-based prediction of concrete performance,” J. Adv. Concr. Technol., vol. 19, no. 8, pp. 924–936, 2021.
[39] D. H. de Bem, D. P. B. Lima, and R. A. Medeiros-Junior, “Effect of chemical admixtures on concrete’s electrical resistivity,” Int. J. Build. Pathol. Adapt., vol. 36, no. 2, pp. 174–187, 2018, doi: 10.1108/IJBPA-11-2017-0058.
[40] M. Saridemir, I. B. Topçu, F. Özcan, and M. H. Severcan, “Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic,” Constr. Build. Mater., vol. 23, no. 3, pp. 1279–1286, Mar. 2009, doi: 10.1016/j.conbuildmat.2008.07.021.