Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

Cut Rahmawati, Siti Aisyah, . Sanusi, . Iqbal, M. Mufid Maulana, . Erdiwansyah, Jawad Ahmad


The utilization of nanosilica and cellulose nanocrystals (CNCs) in cement geopolymers remains challenged by intricacies and uncertainties regarding their concentration, posing difficulties in the formulation of systematic geopolymer mix designs. This study aims to formulate models based on Artificial Neural Networks (ANN) capable of forecasting the compressive strength of geopolymers through the utilization of experimentally acquired data. Nanosilica was applied at concentrations of 2%–4% and CNCs at 1%–3%. ANN was modeled using MATLAB to predict the compressive strength of the geopolymer. The results indicated an effect of nanosilica and CNCs on the compressive strength of geopolymer at 2%–4% concentration and 1%–3% CNCs. The best ANN was the GDX training function, purelin activation function, LGD and LGDM learning functions, Lr 0.1 and 0.01 at the number of epochs 3812 out of 25000 and 1774 out of 25000, resulting in the best correlation values of 0.994 and 0.959; the lowest RMSE values are 0.022 and 0.110. The results of the ANN model built based on actual data prove that the model is helpful for accurate simulation to predict the compressive strength of geopolymer cement. This study contributes novelty by optimizing the design model for Geopolymer Cements incorporating nanosilica and CNCs.


Doi: 10.28991/CEJ-SP2024-010-03

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Artificial Neural Network; Nanosilica; Cellulose Nanocrystals (CNCs); A.Donax L.


Handayani, L., Aprilia, S., Abdullah, Rahmawati, C., Bakri, A. M. M. Al, Aziz, I. H., & Azimi, E. A. (2021). Synthesis of Sodium Silicate from Rice Husk Ash as an Activator to Produce Epoxy-Geopolymer Cement. Journal of Physics: Conference Series, 1845(1), 1–8,. doi:10.1088/1742-6596/1845/1/012072.

Ahmad, J., Zhou, Z., & Martínez-García, R. (2022). A study on the microstructure and durability performance of rubberized concrete with waste glass as binding material. Journal of Building Engineering, 49(January), 104054. doi:10.1016/j.jobe.2022.104054.

Wongsa, A., Kunthawatwong, R., Naenudon, S., Sata, V., & Chindaprasirt, P. (2020). Natural fiber reinforced high calcium fly ash geopolymer mortar. Construction and Building Materials, 241. doi:10.1016/j.conbuildmat.2020.118143.

Rahmawati, C., Handayani, L., Muhtadin, Faisal, M., Zardi, M., Sapuan, S. M., Hadi, A. E., Ahmad, J., & Isleem, H. F. (2023). Optimization of Mortar Compressive Strength Prepared with Waste Glass Aggregate and Coir Fiber Addition Using Response Surface Methodology. Journal of Renewable Materials, 11(10), 3751–3767. doi:10.32604/jrm.2023.028987.

Opiso, E. M., Tabelin, C. B., Maestre, C. V., Aseniero, J. P. J., Park, I., & Villacorte-Tabelin, M. (2021). Synthesis and characterization of coal fly ash and palm oil fuel ash modified artisanal and small-scale gold mine (ASGM) tailings based geopolymer using sugar mill lime sludge as Ca-based activator. Heliyon, 7(4), 6654. doi:10.1016/j.heliyon.2021.e06654.

Naskar, S., & Chakraborty, A. K. (2016). Effect of nano materials in geopolymer concrete. Perspectives in Science, 8, 273–275. doi:10.1016/j.pisc.2016.04.049.

Alvee, A. R., Malinda, R., Akbar, A. M., Ashar, R. D., Rahmawati, C., Alomayri, T., Raza, A., & Shaikh, F. U. A. (2022). Experimental study of the mechanical properties and microstructure of geopolymer paste containing nano-silica from agricultural waste and crystalline admixtures. Case Studies in Construction Materials, 16, 792. doi:10.1016/j.cscm.2021.e00792.

Phoo-ngernkham, T., Chindaprasirt, P., Sata, V., Hanjitsuwan, S., & Hatanaka, S. (2014). The effect of adding nano-SiO2 and nano-Al2O3 on properties of high calcium fly ash geopolymer cured at ambient temperature. Materials and Design, 55, 58–65. doi:10.1016/j.matdes.2013.09.049.

Assaedi, H., Shaikh, F. U. A., & Low, I. M. (2016). Influence of mixing methods of nano silica on the microstructural and mechanical properties of flax fabric reinforced geopolymer composites. Construction and Building Materials, 123, 541–552. doi:10.1016/j.conbuildmat.2016.07.049.

Roopchund, R., Andrew, J., & Sithole, B. (2022). Using cellulose nanocrystals to improve the mechanical properties of fly ash-based geopolymer construction materials. Engineering Science and Technology, an International Journal, 25, 100989. doi:10.1016/j.jestch.2021.04.008.

Rahmawati, C., Aprilia, S., Saidi, T., Aulia, T. B., Amin, A., Ahmad, J., & Isleem, H. F. (2022). Mechanical properties and fracture parameters of geopolymers based on cellulose nanocrystals from Typha sp. fibers. Case Studies in Construction Materials, 17, e01498. doi:10.1016/j.cscm.2022.e01498.

Rahmawati, C., Aprilia, S., Saidi, T., Aulia, T. B., & Ahmad, I. (2022). Preparation and Characterization of Cellulose Nanocrystals from Typha sp. as a Reinforcing Agent. Journal of Natural Fibers, 19(13), 6182–6195. doi:10.1080/15440478.2021.1904486.

Lazorenko, G., Kasprzhitskii, A., Mischinenko, V., & Kruglikov, A. (2022). Fabrication and characterization of metakaolin-based geopolymer composites reinforced with cellulose nanofibrils. Materials Letters, 308, 131146. doi:10.1016/j.matlet.2021.131146.

Rocha Ferreira, S., Ukrainczyk, N., Defáveri do Carmo e Silva, K., Eduardo Silva, L., & Koenders, E. (2021). Effect of microcrystalline cellulose on geopolymer and Portland cement pastes mechanical performance. Construction and Building Materials, 288, 123053. doi:10.1016/j.conbuildmat.2021.123053.

Ma, W., Qin, Y., Li, Y., Chai, J., Zhang, X., Ma, Y., & Liu, H. (2020). Mechanical properties and engineering application of cellulose fiber-reinforced concrete. Materials Today Communications, 22, 100818. doi:10.1016/j.mtcomm.2019.100818.

Balea, A., Fuente, E., Blanco, A., & Negro, C. (2019). Nanocelluloses: Natural-based materials for fiber- reinforced cement composites. A critical review. Polymers, 11(3). doi:10.3390/polym11030518.

da Gloria, M. Y. R., & Toledo Filho, R. D. (2021). Innovative sandwich panels made of wood bio-concrete and sisal fiber reinforced cement composites. Construction and Building Materials, 272, 121636. doi:10.1016/j.conbuildmat.2020.121636.

Xie, X., Zhou, Z., Jiang, M., Xu, X., Wang, Z., & Hui, D. (2015). Cellulosic fibers from rice straw and bamboo used as reinforcement of cement-based composites for remarkably improving mechanical properties. Composites Part B: Engineering, 78, 153–161. doi:10.1016/j.compositesb.2015.03.086.

Fu, T., Moon, R. J., Zavattieri, P., Youngblood, J., & Weiss, W. J. (2017). Cellulose nanomaterials as additives for cementitious materials. Cellulose-Reinforced Nanofibre Composites, Woodhead Publishing, Sawston, United Kingdom. doi:10.1016/b978-0-08-100957-4.00020-6.

Fiore, V., Scalici, T., & Valenza, A. (2014). Characterization of a new natural fiber from Arundo Donax L. as potential reinforcement of polymer composites. Carbohydrate Polymers, 106(1), 77–83. doi:10.1016/j.carbpol.2014.02.016.

Alvee, A. R., Malinda, R., Akbar, A. M., Ashar, R. D., Rahmawati, C., Mulyaningsih, S., & Putri, L. D. (2023). Influence of nanosilica and crystalline admixture on the short-term behaviour of buried underground geopolymer paste. AIP Conference Proceedings. doi:10.1063/5.0109331.

Rahmawati, C., Aprilia, S., Saidi, T., Aulia, T. B., & Hadi, A. E. (2021). The effects of nanosilica on mechanical properties and fracture toughness of geopolymer cement. Polymers, 13(13), 2178. doi:10.3390/polym13132178.

Hou, P., Kawashima, S., Kong, D., Corr, D. J., Qian, J., & Shah, S. P. (2013). Modification effects of colloidal nanoSiO2 on cement hydration and its gel property. Composites Part B: Engineering, 45(1), 440–448. doi:10.1016/j.compositesb.2012.05.056.

Singh, L. P., Bhattacharyya, S. K., & Ahalawat, S. (2012). Preparation of size controlled silica nano particles and its functional role in cementitious system. Journal of Advanced Concrete Technology, 10(11), 345–352. doi:10.3151/jact.10.345.

Jalal, M., Mansouri, E., Sharifipour, M., & Pouladkhan, A. R. (2012). Mechanical, rheological, durability and microstructural properties of high performance self-compacting concrete containing SiO2 micro and nanoparticles. Materials and Design, 34, 389–400. doi:10.1016/j.matdes.2011.08.037.

Korniejenko, K., Frączek, E., Pytlak, E., & Adamski, M. (2016). Mechanical Properties of Geopolymer Composites Reinforced with Natural Fibers. Procedia Engineering, 151, 388–393. doi:10.1016/j.proeng.2016.07.395.

Correia, E. A., Torres, S. M., Alexandre, M. E. O., Gomes, K. C., Barbosa, N. P., & Barros, S. D. E. (2013). Mechanical performance of natural fibers reinforced geopolymer composites. Materials Science Forum, 758, 139–145. doi:10.4028/

Chen, R., Ahmari, S., & Zhang, L. (2014). Utilization of sweet sorghum fiber to reinforce fly ash-based geopolymer. Journal of Materials Science, 49(6), 2548–2558. doi:10.1007/s10853-013-7950-0.

Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. International Journal of Sustainable Built Environment, 5(2), 355–369. doi:10.1016/j.ijsbe.2016.09.003.

Behfarnia, K., & Khademi, F. (2017). A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system. Iran University of Science & Technology, 7(1), 71-80.

Bagheri, A., Nazari, A., & Sanjayan, J. (2019). The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP. Measurement: Journal of the International Measurement Confederation, 141, 241–249. doi:10.1016/j.measurement.2019.03.001.

McElroy, P. D., Bibang, H., Emadi, H., Kocoglu, Y., Hussain, A., & Watson, M. C. (2021). Artificial neural network (ANN) approach to predict unconfined compressive strength (UCS) of oil and gas well cement reinforced with nanoparticles. Journal of Natural Gas Science and Engineering, 88, 103816. doi:10.1016/j.jngse.2021.103816.

Atoyebi, O. D., Awolusi, T. F., & Davies, I. E. E. (2018). Artificial neural network evaluation of cement-bonded particle board produced from red iron wood (Lophira alata) sawdust and palm kernel shell residues. Case Studies in Construction Materials, 9, 185. doi:10.1016/j.cscm.2018.e00185.

Rahman, S. K., & Al-Ameri, R. (2023). Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network (ANN) modelling. Construction and Building Materials, 397, 132464. doi:10.1016/j.conbuildmat.2023.132464.

Verma, N. K., Meesala, C. R., & Kumar, S. (2023). Developing an ANN prediction model for compressive strength of fly ash-based geopolymer concrete with experimental investigation. Neural Computing and Applications, 35(14), 10329–10345. doi:10.1007/s00521-023-08237-1.

Maheepala, M. M. A. L. N., Nasvi, M. C. M., Robert, D. J., Gunasekara, C., & Kurukulasuriya, L. C. (2023). Mix design development for geopolymer treated expansive subgrades using artificial neural network. Computers and Geotechnics, 161, 105534. doi:10.1016/j.compgeo.2023.105534.

Nazar, S., Yang, J., Amin, M. N., Khan, K., Ashraf, M., Aslam, F., Javed, M. F., & Eldin, S. M. (2023). Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer. Journal of Materials Research and Technology, 24, 100–124. doi:10.1016/j.jmrt.2023.02.180.

Igwe, K. C., Oyedum, O. D., Aibinu, A. M., Ajewole, M. O., & Moses, A. S. (2021). Application of artificial neural network modeling techniques to signal strength computation. Heliyon, 7(3), 6047. doi:10.1016/j.heliyon.2021.e06047.

Awoyera, P. O., Kirgiz, M. S., Viloria, A., & Ovallos-Gazabon, D. (2020). Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques. Journal of Materials Research and Technology, 9(4), 9016–9028. doi:10.1016/j.jmrt.2020.06.008.

Rahman, S. K., & Al-Ameri, R. (2022). Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres. Recycling, 7(4), 1–19. doi:10.3390/recycling7040055.

Khalaf, A. A., Kopecskó, K., & Merta, I. (2022). Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model. Polymers, 14(7). doi:10.3390/polym14071423.

Anwar, A., Wenyi, Y., Jing, L., Yanwei, W., Sun, B., Ameen, M., Shah, I., Chunsheng, L., Mustafa, Z. U., & Muhammad, Y. (2023). Predicting the compressive strength of cellulose nanofibers reinforced concrete using regression machine learning models. Cogent Engineering, 10(1), 1–26,. doi:10.1080/23311916.2023.2225278.

Yang, J., Fan, Y., Zhu, F., Ni, Z., Wan, X., Feng, C., & Yang, J. (2023). Machine learning prediction of 28-day compressive strength of CNT/cement composites with considering size effects. Composite Structures, 308, 116713. doi:10.1016/j.compstruct.2023.116713.

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DOI: 10.28991/CEJ-SP2024-010-03


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