Enhancing durability and sustainability in fly ash-slag concrete using advanced metaheuristic algorithms and explainable ML for compressive strength prediction
Abba BASHIR, Daha S. ALIYU, Salim I. MALAMI, Abdulazeez ROTIMI, Shaban Ismael Albrka ALI, Sani. I ABBA
Abstract. Fly ash slag concrete (FASC), a supplementary cementitious material, has transformed construction by lowering the carbon footprint, minimizing waste, reducing labor costs, and improving durability and precision. Predicting compressive strength (CS), a key mechanical property, is essential for optimal performance. Due to the nonlinear nature of FASC mixtures, researchers now utilize machine learning tools. This study evaluates three machine learning models by combining traditional AI algorithms, such as artificial neural networks (ANN), with nature-inspired optimization techniques, such as chicken swarm optimization (CSO), moth flame optimization algorithm (MOFA), and whale optimization algorithm (WOA). By addressing the gaps in mechanical property variation, dataset scope, and model comparison, this study demonstrated high accuracy in CS prediction for all three models. The ANN optimized by WOA consistently excelled across multiple metrics. Visual evidence supports the models’ effectiveness, suggesting benefits like better quality control, cost savings, increased safety, and a cleaner environment.
Keywords
Fly Ash-Slag Concrete, Chicken Swarm Optimization, Moth Flame Optimization, Whale Optimization, Artificial Neural Network, Supplementary Cementitious Materials
Published online 2/25/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Abba BASHIR, Daha S. ALIYU, Salim I. MALAMI, Abdulazeez ROTIMI, Shaban Ismael Albrka ALI, Sani. I ABBA, Enhancing durability and sustainability in fly ash-slag concrete using advanced metaheuristic algorithms and explainable ML for compressive strength prediction, Materials Research Proceedings, Vol. 48, pp 378-386, 2025
DOI: https://doi.org/10.21741/9781644903414-42
The article was published as article 42 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] C. Kina, H. Tanyildizi, and K. Turk, “Forecasting the compressive strength of GGBFS-based geopolymer concrete via ensemble predictive models,” Constr. Build. Mater., vol. 405, no. September, p. 133299, 2023. https://doi.org/10.1016/j.conbuildmat.2023.133299.
[2] H. Cao et al., “Research into Carbon Dioxide Curing’s Effects on the Properties of Reactive Powder Concrete with Assembly Unit of Sulphoaluminate Cement and Ordinary Portland Cement,” Coatings, vol. 12, no. 2, 2022. https://doi.org/10.3390/coatings12020209.
[3] B. Singh, G. Ishwarya, M. Gupta, and S. K. Bhattacharyya, “Geopolymer concrete: A review of some recent developments,” Constr. Build. Mater., vol. 85, pp. 78–90, 2015. https://doi.org/10.1016/j.conbuildmat.2015.03.036.
[4] R. Mehta, “Concrete carbonation,” Mater. World, vol. 16, no. 10, p. 18, 2008.
[5] W. M. Hale, S. F. Freyne, T. D. Bush, and B. W. Russell, “Properties of concrete mixtures containing slag cement and fly ash for use in transportation structures,” vol. 22, pp. 1990–2000, 2008. https://doi.org/10.1016/j.conbuildmat.2007.07.004.
[6] H. Vanoutrive et al., “Report of RILEM TC 281-CCC: outcomes of a round robin on the resistance to accelerated carbonation of Portland, Portland-fly ash and blast-furnace blended cements,” Mater. Struct. Constr., vol. 55, no. 3, 2022. https://doi.org/10.1617/s11527-022-01927-7.
[7] J. Ahmad et al., “A Comprehensive Review on the Ground Granulated Blast Furnace Slag (GGBS) in Concrete Production,” Sustain., vol. 14, no. 14, 2022. https://doi.org/10.3390/su14148783.
[8] S. H. Han, J. K. Kim, and Y. D. Park, “Prediction of compressive strength of fly ash concrete by new apparent activation energy function,” Cem. Concr. Res., vol. 33, no. 7, pp. 965–971, 2003. https://doi.org/10.1016/S0008-8846(03)00007-3.
[9] J. Huang et al., “Predicting the Compressive Strength of the Cement-Fly Ash – Slag Ternary Concrete Using the Firefly Algorithm ( FA ),” 2022.
[10] C. Qi, B. Huang, M. Wu, K. Wang, S. Yang, and G. Li, “Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer,” Materials (Basel)., vol. 15, no. 15, 2022. https://doi.org/10.3390/ma15155369.
[11] M. I. Shah, S. A. Memon, M. S. Khan Niazi, M. N. Amin, F. Aslam, and M. F. Javed, “Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete,” Adv. Civ. Eng., vol. 2021, 2021. https://doi.org/10.1155/2021/6682283.
[12] S. Chithra, S. R. R. S. Kumar, K. Chinnaraju, and F. Alfin Ashmita, “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks,” Constr. Build. Mater., vol. 114, pp. 528–535, 2016. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
[13] Y. Dodo, K. Arif, M. Alyami, M. Ali, T. Najeh, and Y. Gamil, “Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI),” Sci. Rep., vol. 14, no. 1, pp. 1–23, 2024. https://doi.org/10.1038/s41598-024-54513-y.
[14] Y. Yang, G. Liu, H. Zhang, Y. Zhang, and X. Yang, “Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms,” Buildings, vol. 14, no. 1, p. 190, Jan. 2024. https://doi.org/10.3390/buildings14010190.
[15] W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proc. Natl. Acad. Sci. U. S. A., vol. 116, no. 44, pp. 22071–22080, 2019. https://doi.org/10.1073/pnas.1900654116.
[16] R. I. Hamilton and P. N. Papadopoulos, “Using_SHAP_Values_and_Machine_Learning_to_Understa (1),” vol. 1, pp. 1–12.
