The production of concrete is expensive, energy-intensive, and a significant source of carbon emissions, with energy use accounting for 30–40% of production costs. Since micronised rubber powder (MRP) and ground granulated blast furnace slag (GGBFS) are industrial byproducts of the rubber and steel industries, respectively, they offer sustainable substitutes. The compressive strength (CS), a crucial measure of structural performance, of GGBFS-MRP modified concrete is examined in this work. Five predictive models Linear Regression (LR), Non-Linear Regression (NLR), Multiple Linear Regression (MLR), M5P-tree, and Artificial Neural Network (ANN) were used to analyse a total of 135 samples. CS varied from 25.01 to 59.27 MPa, while the input variables were cement content (325–425 kg/m3), water-to-binder ratio (0.35–0.45), GGBFS (0–40%), MRP (0–5%), fine aggregate (905–1105 kg/m3), coarse aggregate (711–1082 kg/m3), and curing time (7–91 days). The ANN model outperformed the M5P-tree model in terms of predictive accuracy. Sensitivity analysis revealed that the three most important factors influencing CS were curing time, MRP dosage, and GGBFS content. These findings provide reliable predictive models and insights into optimizing sustainable GGBFS-MRP concrete mixtures.
- Open Access
- Article
Hybrid Data-Driven and Explainable Modeling of Compressive Strength in GGBFS–MRP Concrete with Environmental Optimization
- Zahraa Raheem 1,
- Zainab Mohammed 1,
- Zarya Barzan 1,
- Ahmed Salih Mohammed 2,*
Author Information
Received: 02 Oct 2025 | Revised: 12 Nov 2025 | Accepted: 14 Nov 2025 | Published: 05 Jan 2026
Abstract
Keywords
ground granulated blast furnace slag | micronized rubber powder | compressive strength | age | predictive models | sustainability
References
- 1.
Madlool, N.A.; Saidur, R.; Hossain, M.S.; et al. A Critical Review on Energy Use and Savings in the Cement Industries. Renew. Sustain. Energy Rev. 2011, 15, 2042–2060. https://doi.org/10.1016/j.rser.2011.01.005.
- 2.
Soria, A.; Szabó, L.; Fermoso, E.G.; et al. Energy Consumption and CO2 Emissions from the World Cement Industry. In JRC Science for Policy Report; European Commission, Joint Research Centre: Luxembourg, 2016.
- 3.
Penttala, V. Concrete and sustainable development. ACI Mater. J. 1997, 94, 409–416.
- 4.
Mhaya, A.M.; Abidin AR, Z.; Sarbini, N.N.; et al. Role of Crumb Tyre Aggregates in Rubberised Concrete Contained Granulated Blast-Furnace Slag. IOP Conf. Ser. Earth Environ. Sci. 2019, 220, 012029. https://doi.org/10.1088/1755-1315/220/1/012029.
- 5.
Thomas, B.S.; Gupta, R.C. A Comprehensive Review on the Applications of Waste Tire Rubber in Cement Concrete. Renew. Sustain. Energy Rev. 2016, 54, 1323–1333. https://doi.org/10.1016/j.rser.2015.10.092.
- 6.
Amiri, M.; Hatami, F.; Mohammadi Golafshani, E. Evaluating Simultaneous Impact of Slag and Tire Rubber Powder on Mechanical Characteristics and Durability of Concrete. J. Renew. Mater. 2022, 10, 2155–2177. https://doi.org/10.32604/jrm.2022.019726.
- 7.
Press, W.H.; Teukolsky, S.A.; Vetterling, W.T.; et al. Statistical Description of Data (Chap. 14, pp. 721–726). In Numerical Recipes: The Art of Scientific Computing, 3rd Ed.; Cambridge University Press: Cambridge, UK, 2007.
- 8.
Khan, K.; Iqbal, M.; Jalal, F.E.; et al. Hybrid ANN models for durability of GFRP rebars in alkaline concrete environment using three swarm-based optimization algorithms. Constr. Build. Mater. 2022, 352, 128862.
- 9.
Ahmed, H.U.; Mohammed, A.S.; Faraj, R.H.; et al. Compressive strength of geopolymer concrete modified with nano-silica: Experimental and modeling investigations. Case Stud. Constr. Mater. 2022, 16, e01036. https://doi.org/10.1016/j.cscm.2022.e01036.
- 10.
Rahimzadeh, C.Y.; Salih, A.; Barzinjy, A.A. Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages. Sustainability 2022, 14, 1723. https://doi.org/10.3390/su14031723.
- 11.
Qu, K. Research on linear regression algorithm. MATEC Web Conf. 2024, 395, 01046. https://doi.org/10.1051/matecconf/202439501046.
- 12.
Khademi, F.; Jamal, S.M.; Deshpande, N.; et al. Predicting the compressive strength of ready mix concrete using a hybrid data-intelligence model. Constr. Build. Mater. 2017, 138, 805–815. https://doi.org/10.1016/j.conbuildmat.2017.02.164.
- 13.
Hariaji, J. Simple Linear Regression (SLR) Model and Multiple Linear Regression (MLR) Model; Faculty of Mathematics and Natural Science State University of Medan: Medan, Indonesia, 2021.
- 14.
Tranmer, M.; Murphy, J.; Elliot, M.; et al. Multiple Linear Regression, 2nd Ed.; Cathie Marsh Institute Working: Manchester, UK, 2020.
- 15.
Qamar, R.; Zardari, B.A. Artificial neural networks: An overview. Mesopotamian J. Comput. Sci. 2023, 2023, 124–133. https://doi.org/10.58496/MJCSC/2023/015.
- 16.
Mann, S.; Singh, G. Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India. Civ. Environ. Eng. Rep. 2024, 34, 45–62. https://doi.org/10.59440/ceer/188375.
- 17.
Ali, I.; Suthar, M. Comparison between Random forest and M5P to predict the compressive strength of concrete modified with solid wastes. IOP Conf. Ser. Earth Environ. Sci. 2023, 1110, 012085. https://doi.org/10.1088/1755-1315/1110/1/012085.
- 18.
Chicco, D.; Warrens, M.J.; Jurman, G. The Coefficient of Determination R-squared Is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Comput. Sci. 2021, 7, e623. https://doi.org/10.7717/peerj-cs.623.
- 19.
Jierula, A.; Wang, S.; Oh, T.M.; et al. Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data. Appl. Sci. 2021, 11, 2314. https://doi.org/10.3390/app11052314.
- 20.
Wang, W.; Lu, Y. Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conf. Ser. Mater. Sci. Eng. 2018, 324. https://doi.org/10.1088/1757-899x/324/1/012049.
- 21.
Bhattacharya, D.; Sinha, N. Scatter Index: An Alternative Measure of Dispersion Based on Relative Frequency of Occurrence of Observations. In Lecture Notes in Networks and Systems, Proceedings of the 5th ICICC 2021, Bhubaneswar, India, 29–30 January 2022; Springer: Singapore. https://doi.org/10.1007/978-981-19-1559-8_7.
- 22.
Hasanah, U.; Putrawangsa, S.; Kumoro, D.T. Applying Linear Programming in Business Decision Making: A Case of Profit Maximization of a Commercial Housing Development. Eur. J. Bus. Manag. 2019, 11, 19. https://doi.org/10.7176/ejbm/11-19-06.
- 23.
Emad, W.; Salih Mohammed, A.; Kurda, R.; et al. Prediction of concrete materials compressive strength using surrogate models. Structures 2022, 46, 1243–1267. https://doi.org/10.1016/j.istruc.2022.11.002.
- 24.
Robeson, S.M.; Willmott, C.J. Decomposition of the mean absolute error (MAE) into systematic and unsystematic components. PLoS ONE 2023, 18, e0279774. https://doi.org/10.1371/journal.pone.0279774.
- 25.
Jaf, I. Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash. Sustainability 2023, 15, 11554. https://doi.org/10.3390/su151511554.
- 26.
Asteris, P.G.; Apostolopoulou, M.; Armaghani, D.J.; et al. On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength. Metaheuristic Comput. Appl. 2020, 1, 63–99.
- 27.
Pan, Y.; Wang, L.; Zhang, D. Effect of curing time on the mechanical properties of blended cementitious materials. Constr. Build. Mater. 2022, 334, 127445. https://doi.org/10.1016/j.conbuildmat.2022.127445.
- 28.
Sadek, D.M. Influence of curing regimes on compressive strength development of eco-concrete. J. Clean. Prod. 2024, 407, 137228.
- 29.
Rokhsana, S.; Uddin, M.; Ahmed, T. Utilization of GGBFS in concrete: A review on strength and durability. Mater. Today Proc. 2024, 80, 1480–1486.
- 30.
Kumar, R.; Prasad, B.K. Performance evaluation of GGBFS-based concrete mixes under different curing regimes. J. Sustain. Cem. Based Mater. 2023, 12, 66–79.
- 31.
Zhao, Y.; Huang, J.; Wu, B. Effect of recycled mineral powder on mechanical properties of blended cement mortar. J. Mater. Civ. Eng. 2021, 33, 04021088.
- 32.
Ali, S.F.; Mahmood, T.; Khan, A. Utilization of mineral powders as cement replacement: Influence on strength and microstructure. Case Stud. Constr. Mater. 2022, 16, e00950.
- 33.
Hatami, F.; Amiri, M. Experimental study of mechanical properties and durability of green concrete containing slag, waste rubber powder and recycled aggregate with artificial neural network. Clean. Mater. 2022, 5, 100112. https://doi.org/10.1016/j.clema.2022.100112.
- 34.
Turumella, V.; Sravana, P.; Srinivasa, P. On the relationship between compressive strength and water binder ratio of high volumes of slag concrete. Int. J. Appl. Eng. Res. 2016, 11, 1436–1442.
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