Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions
Abstract The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promisin...
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Nature Portfolio
2025-02-01
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| author | Kennedy C. Onyelowe Viroon Kamchoom Ahmed M. Ebid Shadi Hanandeh José Luis Llamuca Llamuca Fabián Patricio Londo Yachambay José Luis Allauca Palta M. Vishnupriyan Siva Avudaiappan |
| author_facet | Kennedy C. Onyelowe Viroon Kamchoom Ahmed M. Ebid Shadi Hanandeh José Luis Llamuca Llamuca Fabián Patricio Londo Yachambay José Luis Allauca Palta M. Vishnupriyan Siva Avudaiappan |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Abstract The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables researchers to derive valuable predictions and optimize critical performance parameters efficiently. In this research work, 235 records were collected from extensive literature search for compressive strength for different mixing ratios of pre-cured metakaolin-based geopolymer concrete with concrete at different ages. Each record contains MK: The content of metakaolin (kg/m3), SHS: Sodium hydroxide solution content (kg/m3), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m3), W: Extra water content (not including the water in alkaline solutions) (kg/m3), W/S: Water to Solid ratio (Total water content / Solid part of activator solutions + MK), Na2O/Al2O3: Sodium oxide to aluminium oxide ratio, SiO2/Al2O3: Silicon oxide to aluminium oxide ratio, H2O/Na2O: Water to Sodium oxide ratio, CA/FA: Coarse to Fine aggregate ratio, CAg: The content of coarse aggregates (kg/m3), SP: The content of super-plasticizer (kg/m3), PCC: 0 for no pre-curing, 1 for pre-curing at 60 °C, and 2 for pre-curing at 80 °C, CT: Curing temperature (°C), Age: The concrete age at testing (days) and CS: Compressive strength (MPa). The collected records were portioned into training set (180 records≈75%) and validation set (55 records≈ 25%) and modeled with ensemble and symbolic regression methods. At the end of the model work, performance metrics were used to evaluate the models’ ability and Hoffman and Gardener’s sensitivity analysis was used to evaluate the impact of the variables on the compressive strength of the pre-cured geopolymer concrete mixed with metakaolin. GB and KNN models became the decisive models with excellent performance which outclassed others and the sensitivity analysis indicated that SHSM, SSS, W/S, and Na2O/Al2O3 are the most influential to the predicted compressive strength. |
| format | Article |
| id | doaj-art-5d6a1cfd5e4041bba3e4531d55bbafaa |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-5d6a1cfd5e4041bba3e4531d55bbafaa2025-08-20T02:59:32ZengNature PortfolioScientific Reports2045-23222025-02-0115113410.1038/s41598-025-91049-1Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressionsKennedy C. Onyelowe0Viroon Kamchoom1Ahmed M. Ebid2Shadi Hanandeh3José Luis Llamuca Llamuca4Fabián Patricio Londo Yachambay5José Luis Allauca Palta6M. Vishnupriyan7Siva Avudaiappan8Department of Civil Engineering, College of Eng & Eng Technology, Michael Okpara University of AgricultureExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang (KMITL)Department of Civil Engineering, Faculty of Engineering, Future University in EgyptDepartment of Civil Engineering, al-Balqa Applied UniversityFacultad de Administración de Empresas, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Ciencias, Escuela Superior Politécnica de Chimborazo (ESPOCH)Instituto Superior Tecnológico General Eloy Alfaro (ISTGEA)Department of Civil Engineering, School of Engineering, SR UniversityDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica MetropolitanaAbstract The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables researchers to derive valuable predictions and optimize critical performance parameters efficiently. In this research work, 235 records were collected from extensive literature search for compressive strength for different mixing ratios of pre-cured metakaolin-based geopolymer concrete with concrete at different ages. Each record contains MK: The content of metakaolin (kg/m3), SHS: Sodium hydroxide solution content (kg/m3), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m3), W: Extra water content (not including the water in alkaline solutions) (kg/m3), W/S: Water to Solid ratio (Total water content / Solid part of activator solutions + MK), Na2O/Al2O3: Sodium oxide to aluminium oxide ratio, SiO2/Al2O3: Silicon oxide to aluminium oxide ratio, H2O/Na2O: Water to Sodium oxide ratio, CA/FA: Coarse to Fine aggregate ratio, CAg: The content of coarse aggregates (kg/m3), SP: The content of super-plasticizer (kg/m3), PCC: 0 for no pre-curing, 1 for pre-curing at 60 °C, and 2 for pre-curing at 80 °C, CT: Curing temperature (°C), Age: The concrete age at testing (days) and CS: Compressive strength (MPa). The collected records were portioned into training set (180 records≈75%) and validation set (55 records≈ 25%) and modeled with ensemble and symbolic regression methods. At the end of the model work, performance metrics were used to evaluate the models’ ability and Hoffman and Gardener’s sensitivity analysis was used to evaluate the impact of the variables on the compressive strength of the pre-cured geopolymer concrete mixed with metakaolin. GB and KNN models became the decisive models with excellent performance which outclassed others and the sensitivity analysis indicated that SHSM, SSS, W/S, and Na2O/Al2O3 are the most influential to the predicted compressive strength.https://doi.org/10.1038/s41598-025-91049-1Geopolymer concreteMetakaolinMachine learningSensitivity analysisSustainable concreteCompressive strength |
| spellingShingle | Kennedy C. Onyelowe Viroon Kamchoom Ahmed M. Ebid Shadi Hanandeh José Luis Llamuca Llamuca Fabián Patricio Londo Yachambay José Luis Allauca Palta M. Vishnupriyan Siva Avudaiappan Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions Scientific Reports Geopolymer concrete Metakaolin Machine learning Sensitivity analysis Sustainable concrete Compressive strength |
| title | Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions |
| title_full | Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions |
| title_fullStr | Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions |
| title_full_unstemmed | Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions |
| title_short | Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions |
| title_sort | optimizing the utilization of metakaolin in pre cured geopolymer concrete using ensemble and symbolic regressions |
| topic | Geopolymer concrete Metakaolin Machine learning Sensitivity analysis Sustainable concrete Compressive strength |
| url | https://doi.org/10.1038/s41598-025-91049-1 |
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