Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Mechanical and durability performance prediction of geopolymer incorporating ferrosilicon slag and aluminum powder using machine learning techniques
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 30 January 2026

Mechanical and durability performance prediction of geopolymer incorporating ferrosilicon slag and aluminum powder using machine learning techniques

  • K. Narshimha Raju1 &
  • G. K. Arunvivek1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Environmental sciences
  • Materials science

Abstract

The present study explores the mechanical, durability, and environmental performance of geopolymer concrete (GPC) synthesized using ferrosilicon slag (FS) and aluminum powder (AP) as sustainable binder constituents. Eight mixes were prepared by varying FS: AP ratios (100:0, 95:5, 90:10, 85:15) and activating them with 6 M and 9 M sodium hydroxide at a constant activator-to-binder ratio. Experimental tests included compressive, flexural, and split tensile strengths, water sorptivity, and rapid chloride penetration (RCPT). Results showed that increasing AP content improved workability up to an optimum of 10%, after which excessive porosity reduced strength. The 90% FS–10% AP mix at 9 M NaOH (M7) achieved the highest performance, yielding 53.2% higher compressive strength, 23.8% higher flexural strength, and 24.0% higher split tensile strength than the corresponding 6 M mix. Durability also improved significantly, with sorptivity reduced by 25.0% and RCPT charge by 52.9% at higher molarity. Machine learning (ML) models artificial neural networks (ANN), random forest (RF), and support vector regression (SVR) were trained to predict compressive strength, with RF achieving the best accuracy (R2 = 0.98). A cradle-to-gate carbon footprint analysis demonstrated that AP-free mixes had the lowest embodied CO2, whereas the optimal M7 mix provided the best balance between performance and sustainability. Overall, the study highlights the synergistic potential of FS and AP in geopolymer concretes and provides an integrated experimental–ML–carbon framework for designing high-performance, low-carbon GPC mixtures.

Data availability

The experimental data pertaining to the results submitted shall be provided upon request to the corresponding author.

References

  1. Dighade, R. et al. Emission of carbon footprint from Building construction materials: A review. IOP Conf. Series: Earth Environ. Sci. https://doi.org/10.1088/1755-1315/1409/1/012010 (2024).

    Google Scholar 

  2. Chamasemani, N., Kelishadi, M., Mostafaei, H., Najvani, M. & Mashayekhi, M. Environmental impacts of reinforced concrete buildings: comparing common and sustainable materials: A case study. Constr. Mater. https://doi.org/10.3390/constrmater4010001 (2023).

    Google Scholar 

  3. Sizirici, B., Fseha, Y., Cho, C., Yildiz, I. & Byon, Y. A review of carbon footprint reduction in construction Industry, from design to operation. Materials. https://doi.org/10.3390/ma14206094 (2021).

    Google Scholar 

  4. Onat, N. & Kucukvar, M. Carbon footprint of construction industry: A global review and supply chain analysis. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2020.109783 (2020).

    Google Scholar 

  5. Osorio-Gomez, C., Alzate-Buitrago, A., Amariles-López, C., Aristizábal-Torres, D. & Mancilla-Rico, E. Interaction of life cycle assessment (LCA) and BIM in a construction project to reduce the environmental footprint. Civil Eng. J. https://doi.org/10.28991/cej-2025-011-01-08 (2025).

    Google Scholar 

  6. Labaran, Y., Mathur, V., Muhammad, S. & Musa, A. Carbon footprint management: A review of construction industry. Clean. Eng. Technol. https://doi.org/10.1016/j.clet.2022.100531 (2022).

    Google Scholar 

  7. Dolmatov, S., Kolesnikov, P. & Smertin, N. Methods for reducing the carbon footprint of industrial and construction facilities. IOP Conf. Series: Earth Environ. Sci. https://doi.org/10.1088/1755-1315/981/4/042005 (2022).

    Google Scholar 

  8. Yardimci, Y. & Kuruçay, E. LCA-TOPSIS integration for minimizing material waste in the construction sector: A BIM-Based Decision-Making. Buildings https://doi.org/10.3390/buildings14123919 (2024).

    Google Scholar 

  9. Akan, M., Dhavale, D. & Sarkis, J. Greenhouse gas emissions in the construction industry: an analysis and evaluation of a concrete supply chain. J. Clean. Prod.. https://doi.org/10.1016/J.JCLEPRO.2017.07.225 (2017).

    Google Scholar 

  10. Labaran, Y., Musa, A., Mathur, V. & Saini, G. Exploring the carbon footprint of nigeria’s construction sector: a quantitative insight. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-024-05111-5 (2024).

    Google Scholar 

  11. Yang, Z., Zhang, B., Yang, Y., Qin, B. & Wang, Z. Interprovincial inequality between economic benefit and carbon footprint: perspective from china’s construction industry. Environ. Impact Assess. Rev. https://doi.org/10.1016/j.eiar.2023.107293 (2024).

    Google Scholar 

  12. Chaudhury, S., Sharma, R., Thapliyal, U., Singh, L. & P., & Low-CO2 emission strategies to achieve net zero target in cement sector. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2023.137466 (2023).

    Google Scholar 

  13. Ahmad, M., Fernández-Jiménez, A., Chen, B., Leng, Z. & Dai, J. Low-carbon cementitious materials: Scale-up potential, environmental impact and barriers. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2024.139087 (2024).

    Google Scholar 

  14. Dunant, C., Joseph, S., Prajapati, R. & Allwood, J. Electric recycling of Portland cement at scale. Nature. https://doi.org/10.1038/s41586-024-07338-8 (2024).

    Google Scholar 

  15. Wang, W., Ye, M., Shi, Y. & Xiao, D. Plant-level intensity of energy and CO2 emissions for Portland cement in Guizhou of Southwest China 2019–2022. Sci. Data. https://doi.org/10.1038/s41597-024-03621-5 (2024).

    Google Scholar 

  16. Meng, D., Unluer, C., Yang, E. & Qian, S. Recent advances in magnesium-based materials: CO2 sequestration and utilization, mechanical properties and environmental impact. Cem. Concr. Compos. https://doi.org/10.1016/j.cemconcomp.2023.104983 (2023).

    Google Scholar 

  17. Ige,O., Olanrewaju, O., Duffy, K. & Collins, O. Environmental impact analysis of Portland cement (CEM1) using the midpoint method. Energies 15, 2708 (2022). https://doi.org/10.3390/en15072708

    Google Scholar 

  18. Sánchez, A., Ramos, V., Polo, M., Ramón, M. & Utrilla, J. Life cycle assessment of cement production with marble waste sludges. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph182010968 (2021).

    Google Scholar 

  19. Tarpani, R. et al. Environmental assessment of cement production with added graphene. Clean. Environ. Syst. https://doi.org/10.1016/j.cesys.2024.100206 (2024).

    Google Scholar 

  20. Sousa, V., Bogas, J., Real, S. & Meireles, I. Industrial production of recycled cement: energy consumption and carbon dioxide emission Estimation. Environ. Sci. Pollut. Res.. https://doi.org/10.1007/s11356-022-20887-7 (2022).

    Google Scholar 

  21. Neupane, K. Evaluation of environmental sustainability of One-part geopolymer binder concrete. Clean. Mater. https://doi.org/10.1016/j.clema.2022.100138 (2022).

    Google Scholar 

  22. Singh, N. & Middendorf, B. Geopolymers as an alternative to Portland cement: An overview. Constr. Build. Mater.. https://doi.org/10.1016/j.conbuildmat.2019.117455 (2020).

    Google Scholar 

  23. Jalal, P., Srivastava, V. & Tiwari, A. Geopolymer concrete: an alternative to conventional concrete for sustainable construction. J. Environ. Nanatechnol. https://doi.org/10.13074/jent.2024.12.2441122 (2024).

    Google Scholar 

  24. Farooq, F. et al. GPCas sustainable material: A state of the Art review. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2021.124762 (2021).

    Google Scholar 

  25. Oyebisi, S. et al. Sustainability assessment of gpcsynthesized by slag and corncob Ash. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2022.e01665 (2022).

    Google Scholar 

  26. Amran, Y., Alyousef, R., Alabduljabbar, H. & MohamedEl Clean production and properties of geopolymer concrete; A review. J. Clean. Prod.. https://doi.org/10.1016/j.jclepro.2019.119679 (2020).

    Google Scholar 

  27. Ikotun, J., Aderinto, G., Madirisha, M. & Katte, V. Geopolymer cement in pavement applications: bridging sustainability and performance. Sustainability https://doi.org/10.3390/su16135417 (2024).

    Google Scholar 

  28. Nodehi, M. & Taghvaee, V. Alkali-Activated materials and geopolymer: a review of common precursors and activators addressing circular economy. Circular Econ. Sustain.. https://doi.org/10.1007/s43615-021-00029-w (2021).

    Google Scholar 

  29. Ahmed, H. et al. GPCas a cleaner construction material: an overview on materials and structural performances. Clean. Mater. https://doi.org/10.1016/j.clema.2022.100111 (2022).

    Google Scholar 

  30. Verma, M. et al. Geopolymer concrete: A material for sustainable development in Indian construction industries. Crystals https://doi.org/10.3390/cryst12040514 (2022).

    Google Scholar 

  31. Meskhi, B. et al. Anal. Rev. Geopolymer Concrete: Retrospective Curr. Issues Mater.. https://doi.org/10.3390/ma16103792 (2023).

    Google Scholar 

  32. Amin,M., Elsakhawy, Y., El-hassan, K. & Abdelsalam, B. Behavior evaluation of sustainable high strength GPCbased on fly ash, metakaolin, and slag. Case Stud. Constr. Mater. 16, e00976 (2022). https://doi.org/10.1016/j.cscm.2022.e00976

    Google Scholar 

  33. Wasim, M., Ngo, T. & Law, D. A state-of-the-art review on the durability of GPCfor sustainable structures and infrastructure. Constr. Build. Mater. https://doi.org/10.1016/J.CONBUILDMAT.2021.123381 (2021).

    Google Scholar 

  34. Chen, K., Wu, D., Xia, L., Cai, Q. & Zhang, Z. GPCdurability subjected to aggressive environments – A review of influence factors and comparison with ordinary Portland cement. Constr. Build. Mater.. https://doi.org/10.1016/J.CONBUILDMAT.2021.122496 (2021).

    Google Scholar 

  35. Almutairi, A., Tayeh, B., Adesina, A., Isleem, H. & Zeyad, A. Potential applications of GPCin construction: A review. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2021.e00733 (2021).

    Google Scholar 

  36. Delgado-Plana,P., Bueno-Rodríguez, S., Pérez-Villarejo, L. & Eliche-Quesada, D. Effect of Portland Cement Addition in Ferrosilicon Slag Alkali Activated Materials. Mater. Proc. 8, 122 (2022). https://doi.org/10.3390/materproc2022008122

    Google Scholar 

  37. Khoroshev, A., Makarevich, А., Chernyshev, S., Lesyuk, V. & Shishlov, А. Use of ferrosilicon slag in steel production. Metallurgist. https://doi.org/10.1007/s11015-022-01356-5 (2022).

    Google Scholar 

  38. Hou, Y., Zhang, G. & Chou, K. Reaction behavior of SiC with CaO–SiO2–Al2O3 slag. ISIJ Int. https://doi.org/10.2355/ISIJINTERNATIONAL.ISIJINT-2020-605 (2021).

    Google Scholar 

  39. Kim, H. & Ann, K. Applicability of ferrosilicon slag for a cementitious binder in concrete mix. Constr. Build. Mater.. https://doi.org/10.1016/j.conbuildmat.2020.121873 (2021).

    Google Scholar 

  40. Ahmed, M. et al. Fabrication of thermal insulation geopolymer bricks using ferrosilicon slag and alumina waste. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2021.e00737 (2021).

    Google Scholar 

  41. Amin, M., Zeyad, A., Tayeh, B. & Agwa, I. Effect of ferrosilicon and silica fume on mechanical, durability, and microstructure characteristics of ultra high-performance concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2021.126233 (2022).

    Google Scholar 

  42. Tayeh, B., Hakamy, A., Amin, M., Zeyad, A. & Agwa, I. Effect of air agent on mechanical properties and microstructure of lightweight gpcunder high temperature. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2022.e00951 (2022).

    Google Scholar 

  43. Chen, Z. H., Ba, Y., Shao, N. & M., & Properties of alkali activated materials prepared with secondary aluminum Ash sintered powder. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2024.139516 (2025).

    Google Scholar 

  44. Zhang, S., Wang, K., Li, H., Zhang, X. & Jiang, Y. Novel SCMs produced by the calcination of secondary aluminium Dross with dolomite and their potential usage in cemented paste backfill. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.130119 (2023).

    Google Scholar 

  45. Singh, G. & Scrivener, K. Performance of limestone calcined clay cement (LC3)-Based lightweight blocks. RILEM Bookseries. (2020). https://doi.org/10.1007/978-981-15-2806-4_94

  46. Hameed, S. et al. Effect of aluminium waste powder on the strength properties of cement mortar. Neutron 23, 1 (2023).

    Google Scholar 

  47. Gharieb, M. & Khater, H. Valorization study of mixing aluminum slag with binary geopolymer blends to produce lightweight geopolymer concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2025.140288 (2025).

    Google Scholar 

  48. Abdellatief, M. et al. Physico-mechanical, thermal insulation properties, and microstructure of geopolymer foam concrete containing sawdust Ash and egg shell. J. Building Eng. https://doi.org/10.1016/j.jobe.2024.109374 (2024).

    Google Scholar 

  49. Sanjayan, J., Nazari, A., Chen, L. & Nguyen, G. Physical and mechanical properties of lightweight aerated geopolymer. Constr. Build. Mater. https://doi.org/10.1016/J.CONBUILDMAT.2015.01.043 (2015).

    Google Scholar 

  50. Font, A., Borrachero, M., Soriano, L., Monzó, J. & Payá, J. Geopolymer eco-cellular concrete (GECC) based on fluid catalytic cracking catalyst residue (FCC) with addition of recycled aluminium foil powder. J. Clean. Prod. https://doi.org/10.1016/J.JCLEPRO.2017.09.110 (2017).

    Google Scholar 

  51. Keawpapasson, P. et al. Metakaolin-Based porous geopolymer with aluminium powder. Key Eng. Mater. https://doi.org/10.4028/www.scientific.net/KEM.608.132 (2014).

    Google Scholar 

  52. Hajimohammadi, A., Ngo, T. & Mendis, P. How does aluminium foaming agent impact the geopolymer formation mechanism. Cement Concr. Compos.. https://doi.org/10.1016/J.CEMCONCOMP.2017.03.022 (2017).

    Google Scholar 

  53. Durak, U. The improvement of strength and microstructural properties of fly ash-based geopolymer by adding elemental aluminum powder. J. Mater. Cycles Waste Manage.. https://doi.org/10.1007/s10163-022-01520-8 (2022).

    Google Scholar 

  54. Hajimohammadi, A., Ngo, T. & Mendis, P. The effect of aluminium reaction on formation mechanism and structural properties of geopolymers. (2016).

  55. Matei, E. et al. Ferrous industrial Wastes—Valuable resources for water and wastewater decontamination. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph192113951 (2022).

    Google Scholar 

  56. Jiménez, A. et al. Synthesis of pollucite and analcime zeolites by recovering aluminum from a saline slag. J. Clean. Prod.. https://doi.org/10.1016/J.JCLEPRO.2021.126667 (2021).

    Google Scholar 

  57. Veliyev, E. & Aliyev, A. Design of a lightweight cementing material on basis of geopolymer and gas-forming agent. SOCAR Proceedings. (2023). https://doi.org/10.5510/ogp20230100813

  58. Kanagaraj, B., Anand, N., Andrushia, A. D. & Lubloy, E. Investigation on engineering properties and micro-structure characteristics of low strength and high strength geopolymer composites subjected to standard temperature exposure. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2022.e01608 (2022).

    Google Scholar 

  59. Mostafa, S., Agwa, I., Elboshy, B., Zeyad, A. & Hassan, A. The effect of lightweight GPC containing air agent on Building envelope performance and internal thermal comfort. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2024.e03365 (2024).

    Google Scholar 

  60. Kioupis, D., Skaropoulou, A., Tsivilis, S. & Kakali, G. Development of lightweight geopolymer composites by combining various CDW streams. Ceramics https://doi.org/10.3390/ceramics6020048 (2023).

    Google Scholar 

  61. Lima, J. et al. Use of rice husk Ash to produce alternative sodium silicate for geopolymerization reactions. Cerâmica https://doi.org/10.1590/0366-69132021673812891 (2021).

    Google Scholar 

  62. Hou, D. et al. Molecular insights into the reaction process of Alkali-Activated Metakaolin by sodium hydroxide. Langmuir: ACS J. Surf. Colloids. https://doi.org/10.1021/acs.langmuir.2c01631 (2022).

    Google Scholar 

  63. Tong, K., Vinai, R. & Soutsos, M. Use of Vietnamese rice husk Ash for the production of sodium silicate as the activator for alkali-activated binders. J. Clean. Prod. https://doi.org/10.1016/J.JCLEPRO.2018.08.025 (2018).

    Google Scholar 

  64. Karaaslan, C. & Yener, E. The effect of alkaline activator components on the properties of fly Ash added pumice based geopolymer. J. Inst. Sci. Technol. https://doi.org/10.21597/JIST.840872 (2021).

    Google Scholar 

  65. Li, H., Li, J. & Lu, Z. Effect of Na/Al on formation, structures and properties of Metakaolin based Na-geopolymer. Constr. Build. Mater. https://doi.org/10.1016/J.CONBUILDMAT.2019.07.171 (2019).

    Google Scholar 

  66. Rihan, M., Alahmari, T., Onchiri, R., Gathimba, N. & Sabuni, B. Impact of alkaline concentration on the mechanical properties of GPCMade up of fly Ash and sugarcane Bagasse Ash. Sustainability https://doi.org/10.3390/su16072841 (2024).

    Google Scholar 

  67. Venkatesan, G., Alengaram, U., Ibrahim, S. & Ibrahim, M. Effect of fly Ash characteristics, sodium-based alkaline activators, and process variables on the compressive strength of siliceous fly Ash geopolymers with microstructural properties: A comprehensive review. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2024.136808 (2024).

    Google Scholar 

  68. Shilar,F., Ganachari, S., Patil, V., Khan, T. & Dawood, S. Molarity activity effect on mechanical and microstructure properties of geopolymer concrete: A review. Case Stud. Constr. Mater. 16, e01014 (2022). https://doi.org/10.1016/j.cscm.2022.e01014

    Google Scholar 

  69. Bidwe,S. S. & Hamane, A. A. Effect of different molarities of sodium hydroxide solution on the strength of geopolymer concrete. Am. J. Eng. Res. 4, 139 (2015). https://doi.org/10.6084/M9.FIGSHARE.1390492.V1

    Google Scholar 

  70. Pane,I., Imran, I. & Budiono, B. Compressive strength of fly ash-based GPCwith a variable of sodium hydroxide (NaOH) solution molarity. MATEC Web Conf. 147, 01004 (2018). https://doi.org/10.1051/MATECCONF/201814701004

    Google Scholar 

  71. Mortar,N., Kamarudin, H., Rafiza, R., Meor, T. & Rosnita, M. Compressive strength of fly ash GPCby varying sodium hydroxide molarity and aggregate to binder ratio. ioP Conf. Series: Mater. Sci. Eng. 864, 012037 (2020). https://doi.org/10.1088/1757-899X/864/1/012037

    Google Scholar 

  72. Memon, F. et al. Effect of sodium hydroxide concentration on fresh properties and compressive strength of self-compacting geopolymer concrete. J. Eng. Sci. Technol. 8, 44 (2013).

    Google Scholar 

  73. Reddy, S., Krishna, V., Rao, S. & Shrihari, S. Effect of molarity of sodium hydroxide and molar ratio of alkaline activator solution on the strength development of geopolymer concrete. E3S Web Conferences. https://doi.org/10.1051/e3sconf/202130901058 (2021).

    Google Scholar 

  74. Amarender, R. & Rayana, H. Study on the molarity effect of sodium hydroxide on gpcincorporating Nanosilica. J. Phys: Conf. Ser. https://doi.org/10.1088/1742-6596/2779/1/012040 (2024).

    Google Scholar 

  75. Ganesh, A. et al. Effect of molarity of sodium hydroxide solution over GGBS-based self compacting geopolymer concrete. E3S Web Conferences. https://doi.org/10.1051/e3sconf/202452901004 (2024).

    Google Scholar 

  76. Lavanya, G. & Jegan, J. Durability study on high calcium fly Ash based geopolymer concrete. Adv. Mater. Sci. Eng. https://doi.org/10.1155/2015/731056 (2015).

    Google Scholar 

  77. Nikolov, A. Alkali-activated geopolymers based on iron-rich slag from copper industry. IOP Conf. Series: Mater. Sci. Eng. https://doi.org/10.1088/1757-899X/951/1/012006 (2020).

    Google Scholar 

  78. Simon, S. et al. The fate of iron during the alkali-activation of synthetic (CaO‐)FeOx‐SiO2 slags: an Fe K‐edge XANES study. J. Am. Ceram. Soc.. https://doi.org/10.1111/JACE.15354 (2018).

    Google Scholar 

  79. Nikolov, A. Alkali and acid activated geopolymers based on iron-silicate fines - by-product from copper industry. Int. Sci. J. Mach. Technol. Mater. 14, 37 (2020).

    Google Scholar 

  80. Kanagaraj, B., Lubloy, E., Anand, N., Hlavicka, V. & Kiran, T. Investigation of physical, chemical, mechanical, and microstructural properties of cement-less concrete–state-of-the-art review. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.130020 (2023).

    Google Scholar 

  81. Wu, J. Study on the effect of different air-entraining agents on the properties of concrete. E3S Web Conferences. https://doi.org/10.1051/e3sconf/202561802001 (2025).

    Google Scholar 

  82. Song, X., Ng, S., Ni, T., Wang, Y. & Ke, F. Effect of air entraining agents on the air void structure of concrete. J. Phys: Conf. Ser.. https://doi.org/10.1088/1742-6596/2011/1/012051 (2021).

    Google Scholar 

  83. Yang, Y. Study on the influence of Air-Entraining agent on concrete pore structure based on nuclear magnetic resonance technology. Acad. J. Sci. Technol. https://doi.org/10.54097/evveb837 (2025).

    Google Scholar 

  84. Paruthi, S., Rahman, I., Husain, A., Hasan, M. A. & Khan, A. H. Effects of chemicals exposure on the durability of geopolymer concrete incorporated with silica fumes and nano-sized silica at varying curing temperatures. Materials https://doi.org/10.3390/ma16186332 (2023).

    Google Scholar 

  85. Lee, Y. H. et al. Thermal performance of structural lightweight concrete composites for potential energy saving. Crystals https://doi.org/10.3390/cryst11050461 (2021).

    Google Scholar 

  86. Chaabene, W., Flah, M. & Nehdi, M. Machine learning prediction of mechanical properties of concrete: critical review. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2020.119889 (2020).

    Google Scholar 

  87. Vatin, N., Hematibahar, M. & Gebre, T. Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties. Front. Built Environ. https://doi.org/10.3389/fbuil.2025.1558394 (2025).

    Google Scholar 

  88. Asteris, P., Skentou, A., Bardhan, A., Samui, P. & Pilakoutas, K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem. Concr. Res. https://doi.org/10.1016/J.CEMCONRES.2021.106449 (2021).

    Google Scholar 

  89. Imran, M., Amjad, H., Khan, S. & Ali, S. Machine learning assisted prediction of the mechanical properties of carbon nanotube-incorporated concrete. Struct. Concrete. https://doi.org/10.1002/suco.202400727 (2024).

    Google Scholar 

  90. Liu, Y. High-Performance concrete strength prediction based on machine learning. Comput. Intell. Neurosci.. https://doi.org/10.1155/2022/5802217 (2022).

    Google Scholar 

  91. Kashem, A. et al. Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses. Case Stud. Constr. Mater. https://doi.org/10.1016/j.cscm.2024.e02991 (2024).

    Google Scholar 

  92. Pal, A., Ahmed, K., Hossain, F. & Alam, M. Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2023.138673 (2023).

    Google Scholar 

  93. Hosseinzadeh, M., Dehestani, M. & Hosseinzadeh, A. Prediction of mechanical properties of recycled aggregate fly Ash concrete employing machine learning algorithms. J. Building Eng. https://doi.org/10.1016/j.jobe.2023.107006 (2023).

    Google Scholar 

  94. Çiftçioğlu, Ӧ., Kazemi, F. & Shafighfard, T. Grey Wolf optimizer integrated within boosting algorithm: application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes. Appl. Mater. Today. https://doi.org/10.1016/j.apmt.2025.102601 (2025).

    Google Scholar 

  95. Moein, M. et al. Predictive models for concrete properties using machine learning and deep learning approaches: A review. J. Building Eng. https://doi.org/10.1016/j.jobe.2022.105444 (2022).

    Google Scholar 

  96. Koya, B., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. Mech. Adv. Mater. Struct.. https://doi.org/10.1080/15376494.2021.1917021 (2021).

    Google Scholar 

  97. Zhang, L. & Zhao, Y. Calculation of the mechanical properties of high-performance concrete employing hybrid and ensemble‐hybrid techniques. Struct. Concrete. https://doi.org/10.1002/suco.202300418 (2024).

    Google Scholar 

  98. Kumar, R., Rai, B. & Samui, P. Prediction of mechanical properties of high-performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review. Struct. Concrete. https://doi.org/10.1002/suco.202400188 (2024).

    Google Scholar 

  99. Hematibahar, M. et al. Analysis of models to predict mechanical properties of High-Performance and Ultra-High-Performance concrete using machine learning. J. Compos. Sci. https://doi.org/10.3390/jcs8080287 (2024).

    Google Scholar 

  100. Hafez, H., Teirelbar, A., Kurda, R., Tošić, N. & De La Fuente, A. Pre-bcc: A novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2022.129019 (2022).

    Google Scholar 

  101. Hosseinzadeh, M., Samadvand, H., Hosseinzadeh, A., Mousavi, S. & Dehestani, M. Concrete strength and durability prediction through deep learning and artificial neural networks. Front. Struct. Civil Eng. https://doi.org/10.1007/s11709-024-1124-9 (2024).

    Google Scholar 

  102. Zhang, M. & Kang, R. Machine learning methods for predicting the durability of concrete materials: A review. Adv. Cem. Res. https://doi.org/10.1680/jadcr.24.00133 (2025).

    Google Scholar 

  103. Gamil,Y. Machine learning in concrete technology: A review of current researches, trends, and applications. Front. Built Environ. 9, 1145591 (2023). https://doi.org/10.3389/fbuil.2023.1145591

    Google Scholar 

  104. Arasteh-Khoshbin, O., Seyedpour, S., Mandl, L., Lambers, L. & Ricken, T. Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA. Eur. J. Environ. Civil Eng.. https://doi.org/10.1080/19648189.2024.2393881 (2024).

    Google Scholar 

  105. Upreti, K. et al. Prediction of mechanical strength by using an artificial neural network and random forest algorithm. J. Nanomaterials. https://doi.org/10.1155/2022/7791582 (2022).

    Google Scholar 

  106. Wang, L., Wu, X., Chen, H. & Zeng, T. Prediction of impermeability of the concrete structure based on random forest and support vector machine. IOP Conf. Series: Earth Environ. Sci.. https://doi.org/10.1088/1755-1315/552/1/012004 (2020).

    Google Scholar 

  107. Xu, Y. et al. Computation of High-Performance concrete compressive strength using standalone and ensembled machine learning techniques. Materials. https://doi.org/10.3390/ma14227034 (2021).

    Google Scholar 

  108. Kushal, B., Goud, K., Kumar, K. & Mohan, U. Performance prediction of Eco-Friendly concrete with artificial neural networks (ANNs). E3S Web Conferences. https://doi.org/10.1051/e3sconf/202459601021 (2024).

    Google Scholar 

  109. Farooq, F. et al. A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Appl. Sci. https://doi.org/10.3390/app10207330 (2020).

    Google Scholar 

  110. Chen, P., Wang, H., Cao, S. & Lv, X. Prediction of mechanical behaviours of FRP-Confined circular concrete columns using artificial neural network and support vector regression: modelling and performance evaluation. Materials. https://doi.org/10.3390/ma15144971 (2022).

    Google Scholar 

  111. Lunardi, L., Cornélio, P., Prado, L., Nogueira, C. & Félix, E. Hybrid machine learning model for predicting the fatigue life of plain concrete under Cyclic compression. Buildings https://doi.org/10.3390/buildings15101618 (2025).

    Google Scholar 

  112. Pfeiffer, O. et al. Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization. Cem. Concr. Res. https://doi.org/10.1016/j.cemconres.2023.107406 (2024).

    Google Scholar 

  113. Yuan, Z., Zheng, W. & Qiao, H. Machine learning based optimization for mix design of manufactured sand concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2025.140256 (2025).

    Google Scholar 

  114. Chen, B. et al. Optimization of high-performance concrete mix ratio design using machine learning. Eng. Appl. Artif. Intell.. https://doi.org/10.1016/j.engappai.2023.106047 (2023).

    Google Scholar 

  115. Wang, M. et al. Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. Front. Struct. Civil Eng. https://doi.org/10.1007/s11709-025-1152-0 (2025).

    Google Scholar 

  116. Wakjira, T., Kutty, A. & Alam, M. A novel framework for developing environmentally sustainable and cost-effective ultra-high-performance concrete (UHPC) using advanced machine learning and multi-objective optimization techniques. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2024.135114 (2024).

    Google Scholar 

  117. Liu, J., Liu, F. & Wang, L. Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey Wolf optimization. J. Traffic Transp. Eng. (English Edition). https://doi.org/10.1016/j.jtte.2023.10.002 (2024).

    Google Scholar 

  118. Pham, T., Le-Hong, T. & Tran, X. Efficient Estimation and optimization of Building costs using machine learning. Int. J. Constr. Manag.. https://doi.org/10.1080/15623599.2021.1943630 (2021).

    Google Scholar 

  119. Arabani, M., Effati, M., Safari, M., Shalchian, M. M. & Hassanjani, M. H. The comprehensive review on the mechanisms and performance of different bio-extenders in the bitumen. Int. J. Pavement Res. Technol. https://doi.org/10.1007/s42947-024-00457-5 (2024).

    Google Scholar 

  120. Hassanjani, M. H., Arabani, M., Shalchian, M. M. & Amiri, A. Evaluation of different biomass conversion methods to bio-oil for partial replacement of petroleum bitumen: analysis of effects and implications. Int. J. Pavement Eng. https://doi.org/10.1080/10298436.2025.2472860 (2025).

    Google Scholar 

  121. Arabani, M., Hassanjani, M. H., Farkhondeh, J. & Taleghani, M. Y. Enhancing mechanical properties of hot mix asphalt with Olive kernel ash: A sustainable modifier. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2024.138740 (2024).

    Google Scholar 

  122. Arabani, M., Sadeghnejad, M., Haghanipour, J. & Hassanjani, M. H. The influence of rice Bran oil and nano-calcium oxide into bitumen as sustainable modifiers, case stud. Construct Mater. https://doi.org/10.1016/j.cscm.2024.e03458 (2024).

    Google Scholar 

  123. McLellan, B. C., Williams, R. P., Lay, J., van Riessen, A. & Corder, G. D. Costs and carbon emissions for geopolymer pastes in comparison to ordinary Portland cement. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2011.02.010 (2011).

    Google Scholar 

  124. Norgate, T. E. & Rankin, W. J. The role of metals in sustainable development. In: Green Processing 2002. Carlton, Australia: AusIMM; (2002).

    Google Scholar 

  125. Turner, L. K. & Collins, F. G. Carbon dioxide equivalent (CO2-e) emissions: A comparison between geopolymer and OPC cement concrete. Constr. Build. Mater. https://doi.org/10.1016/j.conbuildmat.2013.01.023 (2013).

    Google Scholar 

  126. Duxson, P., Provis, J. L., Lukey, G. C. & van Deventer, J. S. J. The role of inorganic polymer technology in the development of ‘green concrete’. Cem. Concr Res. https://doi.org/10.1016/j.cemconres.2007.08.018 (2007).

    Google Scholar 

  127. Flower, D. J. M. & Sanjayan, J. G. Greenhouse gas emissions due to concrete manufacture. Int. J. Life Cycle Assess. https://doi.org/10.1065/lca2007.05.327 (2007).

    Google Scholar 

Download references

Funding

The authors declare that no funding/grants were received during this project and preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Mohan Babu University, Tirupati, India

    K. Narshimha Raju & G. K. Arunvivek

Authors
  1. K. Narshimha Raju
    View author publications

    Search author on:PubMed Google Scholar

  2. G. K. Arunvivek
    View author publications

    Search author on:PubMed Google Scholar

Contributions

**K. Narshimha Raju** : Investigation, methodology, visualization, writing—original draft.**G. K. Arunvivek** : conceptualization, methodology, validation, review and editing.

Corresponding author

Correspondence to K. Narshimha Raju.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raju, K.N., Arunvivek, G.K. Mechanical and durability performance prediction of geopolymer incorporating ferrosilicon slag and aluminum powder using machine learning techniques. Sci Rep (2026). https://doi.org/10.1038/s41598-025-32654-y

Download citation

  • Received: 15 August 2025

  • Accepted: 11 December 2025

  • Published: 30 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-32654-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Geopolymer concrete
  • Ferrosilicon slag
  • Aluminum powder
  • Machine learning
  • Strength prediction
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing