An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

Musa Adamu, A. Batur Çolak, Ibrahim K. Umar, Yasser E. Ibrahim, Mukhtar F. Hamza


Plastic waste (PW) is a major soild waste, which its generation continues to increase globally year in and year out. Proper management of the PW is still a challenge due to its non-biodegradable nature. One of the most convenient ways of managing plastic waste is by using it in concrete as a partial substitute for natural aggregate. However, the main shortcomings of adding plastic waste to concrete are a reduction in strength and durability. Hence, to reduce the undesirable impact of the PW in concrete, highly reactive additives are normally added. In this research, 240 experimental datasets were used to train an artificial neural network (ANN) model using Levenberg Marquadt algorithms for the prediction of the mechanical properties and durability of high-volume fly ash (HVFA) concrete containing fly ash and PW as partial substitutes for cement and coarse aggregate, respectively, and graphene nanoplatlets (GNP) as additives to cementitious materials. The optimized model structure has five input parameters, 17 hidden neurons, and one output layer for each of the physical parameters. The results were analyzed graphically and statistically. The obtained results revealed that the generated network model can forecast with deviations less than 0.48%. The efficiency of the ANN model in predicting concrete properties was compared with that of the SVR (support vector regression) and SWLR (stepwise regression) models. The ANN outperformed SVR and SWLR for all the models by up to 6% and 74% for SVR and SWLR, respectively, in the confirmation stage. The graphical analysis of the results further demonstrates the higher prediction ability of the ANN.


Doi: 10.28991/CEJ-2023-09-09-04

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Plastic Waste; Fly Ash; Graphene Nanoplatelets (GNP); ANN; SVM; SWLR.


Mrowiec, B. (2018). Plastics in the circular economy (CE). Environmental Protection and Natural Resources, 29(4), 16–19. doi:10.2478/oszn-2018-0017.

Adamu, M., Trabanpruek, P., Jongvivatsakul, P., Likitlersuang, S., & Iwanami, M. (2021). Mechanical performance and optimization of high-volume fly ash concrete containing plastic wastes and graphene nanoplatelets using response surface methodology. Construction and Building Materials, 308, 125085. doi:10.1016/j.conbuildmat.2021.125085.

Adamu, M., Trabanpruek, P., Limwibul, V., Jongvivatsakul, P., Iwanami, M., & Likitlersuang, S. (2022). Compressive Behavior and Durability Performance of High-Volume Fly-Ash Concrete with Plastic Waste and Graphene Nanoplatelets by Using Response-Surface Methodology. Journal of Materials in Civil Engineering, 34(9), 4022222. doi:10.1061/(asce)mt.1943-5533.0004377.

United Nation Environment Programme. (2022). Our planet is choking on plastic - Beat Plastic Pollution. United Nations, New York, United States. Available online: (accessed on July 2023).

Denta, S. M. (2022). End Plastic Pollution and Plastic Waste-Regulations and Collaboration. Copenhagen Business School, CBS LAW Research Paper.

Bahij, S., Omary, S., Feugeas, F., & Faqiri, A. (2020). Fresh and hardened properties of concrete containing different forms of plastic waste – A review. Waste Management, 113, 157–175. doi:10.1016/j.wasman.2020.05.048.

Steyn, Z. C., Babafemi, A. J., Fataar, H., & Combrinck, R. (2021). Concrete containing waste recycled glass, plastic and rubber as sand replacement. Construction and Building Materials, 269, 121242. doi:10.1016/j.conbuildmat.2020.121242.

Jain, A., Siddique, S., Gupta, T., Jain, S., Sharma, R. K., & Chaudhary, S. (2021). Evaluation of concrete containing waste plastic shredded fibers: Ductility properties. Structural Concrete, 22(1), 566–575. doi:10.1002/suco.201900512.

Anandan, S., & Alsubih, M. (2021). Mechanical strength characterization of plastic fiber reinforced cement concrete composites. Applied Sciences (Switzerland), 11(2), 1–21. doi:10.3390/app11020852.

Khalid, F. S., Irwan, J. M., Ibrahim, M. H. W., Othman, N., & Shahidan, S. (2018). Performance of plastic wastes in fiber-reinforced concrete beams. Construction and Building Materials, 183, 451–464. doi:10.1016/j.conbuildmat.2018.06.122.

Nistratov, A. V., Klimenko, N. N., Pustynnikov, I. V., & Vu, L. K. (2022). Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites. Emerging Science Journal, 6(5), 967-984. doi:10.28991/ESJ-2022-06-05-04.

Záleská, M., Pavlíková, M., Pokorný, J., Jankovský, O., Pavlík, Z., & Černý, R. (2018). Structural, mechanical and hygrothermal properties of lightweight concrete based on the application of waste plastics. Construction and Building Materials, 180, 1–11. doi:10.1016/j.conbuildmat.2018.05.250.

Ruiz-Herrero, J. L., Velasco Nieto, D., López-Gil, A., Arranz, A., Fernández, A., Lorenzana, A., Merino, S., De Saja, J. A., & Rodríguez-Pérez, M. Á. (2016). Mechanical and thermal performance of concrete and mortar cellular materials containing plastic waste. Construction and Building Materials, 104, 298–310. doi:10.1016/j.conbuildmat.2015.12.005.

Farajzadehha, S., Ziaei Moayed, R., & Mahdikhani, M. (2020). Comparative study on uniaxial and triaxial strength of plastic concrete containing nano silica. Construction and Building Materials, 244, 118212. doi:10.1016/j.conbuildmat.2020.118212.

Ahmad, F., Qureshi, M. I., & Ahmad, Z. (2022). Influence of nano graphite platelets on the behavior of concrete with E-waste plastic coarse aggregates. Construction and Building Materials, 316, 125980. doi:10.1016/j.conbuildmat.2021.125980.

Punitha, V., Sakthieswaran, N., & Babu, O. G. (2020). Experimental investigation of concrete incorporating HDPE plastic waste and metakaolin. Materials Today: Proceedings, 37(Part 2), 1032–1035. doi:10.1016/j.matpr.2020.06.288.

Balasubramanian, B., Gopala Krishna, G. V. T., Saraswathy, V., & Srinivasan, K. (2021). Experimental investigation on concrete partially replaced with waste glass powder and waste E-plastic. Construction and Building Materials, 278, 122400. doi:10.1016/j.conbuildmat.2021.122400.

Liu, T., Nafees, A., khan, S., Javed, M. F., Aslam, F., Alabduljabbar, H., Xiong, J. J., Ijaz Khan, M., & Malik, M. Y. (2022). Comparative study of mechanical properties between irradiated and regular plastic waste as a replacement of cement and fine aggregate for manufacturing of green concrete. Ain Shams Engineering Journal, 13(2), 101563. doi:10.1016/j.asej.2021.08.006.

Khan, M. I., Sutanto, M. H., Napiah, M. Bin, Khan, K., & Rafiq, W. (2021). Design optimization and statistical modeling of cementitious grout containing irradiated plastic waste and silica fume using response surface methodology. Construction and Building Materials, 271, 121504. doi:10.1016/j.conbuildmat.2020.121504.

Çolak, A. B., Akçaözoğlu, K., Akçaözoğlu, S., & Beller, G. (2021). Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study. Arabian Journal for Science and Engineering, 46(5), 4867–4881. doi:10.1007/s13369-020-05280-1.

Çelik, F., Çolak, A. B., Yıldız, O., & Bozkır, S. M. (2022). An Experimental Investigation on Workability and Bleeding Features. ACI Materials Journal, 119(5), 63–76. doi:10.14359/51735949.

Çolak, A. B., Yıldız, O., Çelik, F., & Bozkır, S. M. (2022). Developing Prediction Model on Workability Parameters of Ultrasonicated Nano Silica (n-SiO2) and Fly Ash Added Cement-Based Grouts by Using Artificial Neural Networks. Advances in Civil Engineering Materials, 11(1), 20210124. doi:10.1520/acem20210124.

Rezvan, S., Moradi, M. J., Dabiri, H., Daneshvar, K., Karakouzian, M., & Farhangi, V. (2023). Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials. Applied Sciences (Switzerland), 13(4), 2033. doi:10.3390/app13042033.

Sau, D., Hazra, T., & Shiuly, A. (2023). Assessment of Sustainable Green Concrete Properties Using Recycled Plastic Waste as Replacement for Fine Aggregate Using Machine Learning Technique. Composites: Mechanics, Computations, Applications, 14(2), 1–12. doi:10.1615/COMPMECHCOMPUTAPPLINTJ.2022044775.

Ofuyatan, O. M., Agbawhe, O. B., Omole, D. O., Igwegbe, C. A., & Ighalo, J. O. (2022). RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement. Cleaner Materials, 4, 100065. doi:10.1016/j.clema.2022.100065.

Nafees, A., Khan, S., Javed, M. F., Alrowais, R., Mohamed, A. M., Mohamed, A., & Vatin, N. I. (2022). Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF. Polymers, 14(8), 1583. doi:10.3390/polym14081583.

Jamii, J., Mansouri, M., Trabelsi, M., Mimouni, M. F., & Shatanawi, W. (2022). Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management. Frontiers in Energy Research, 10. doi:10.3389/fenrg.2022.898413.

Rehman, K. U., Çolak, A. B., & Shatanawi, W. (2022). Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects. Mathematics, 10(14), 2394. doi:10.3390/math10142394.

Rehman, K. U., Shatanawi, W., & Çolak, A. B. (2023). Artificial neural networking estimation of skin friction coefficient at cylindrical surface: a Casson flow field. European Physical Journal Plus, 138(1), 1–15. doi:10.1140/epjp/s13360-023-03704-z.

Quadros, J. D., Nagpal, C., Khan, S. A., Aabid, A., & Baig, M. (2022). Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach. PLOS ONE, 17(10), e0276074. doi:10.1371/journal.pone.0276074.

Moriyama, M., Takeuchi, M., Uwate, Y., & Nishio, Y. (2016). Firefly Algorithm combined with chaotic map. IEEE Workshop on Nonlinear Circuit Networks, 9-10 December, 2016, Tokushima, Japan.

Umar, I. K., Gökçekuş, H., & Nourani, V. (2022). An intelligent soft computing technique for prediction of vehicular traffic noise. Arabian Journal of Geosciences, 15(19), 1571. doi:10.1007/s12517-022-10858-0.

Muthukumar, M., Mohan, D., & Rajendran, M. (2003). Optimization of mix proportions of mineral aggregates using Box Behnken design of experiments. Cement and Concrete Composites, 25(7), 751–758. doi:10.1016/S0958-9465(02)00116-6.

Wang, W. C., Xu, D. M., Chau, K. W., & Chen, S. (2013). Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD. Journal of Hydroinformatics, 15(4), 1377–1390. doi:10.2166/hydro.2013.134.

Yasar, A., Bilgili, M., & Simsek, E. (2012). Water Demand Forecasting Based on Stepwise Multiple Nonlinear Regression Analysis. Arabian Journal for Science and Engineering, 37(8), 2333–2341. doi:10.1007/s13369-012-0309-z.

Adamu, M., Çolak, A. B., Ibrahim, Y. E., Haruna, S. I., & Hamza, M. F. (2023). Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique. Axioms, 12(1), 81. doi:10.3390/axioms12010081.

Adamu, M., Haruna, S. I., Malami, S. I., Ibrahim, M. N., Abba, S. I., & Ibrahim, Y. E. (2022). Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model versus support vector machine. Modeling Earth Systems and Environment, 8(3), 3435–3445. doi:10.1007/s40808-021-01301-6.

Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research Atmospheres, 106(D7), 7183–7192. doi:10.1029/2000JD900719.

Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S., & Nehdi, M. L. (2018). Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Advances in Engineering Software, 115, 112–125. doi:10.1016/j.advengsoft.2017.09.004.

Zhu, S., & Heddam, S. (2020). Prediction of dissolved oxygen in urban rivers at the three Gorges reservoir, China: Extreme learning machines (ELM) versus artificial neural network (ANN). Water Quality Research Journal, 55(1), 106–118. doi:10.2166/WQRJ.2019.053.

Haruna, S. I., Malami, S. I., Adamu, M., Usman, A. G., Farouk, A., Ali, S. I. A., & Abba, S. I. (2021). Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN. Arabian Journal for Science and Engineering, 46(11), 11207–11222. doi:10.1007/s13369-021-05715-3.

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DOI: 10.28991/CEJ-2023-09-09-04


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