Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete
This study employs machine learning (ML) techniques to predict the compressive strength (fc′) of fly ash-based geopolymer concrete, utilizing a comprehensive set of experimental data. The analysis considered variables such as fly ash (FA) components, coarse and fine aggregates, alkaline activator mo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Cleaner Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666790825000229 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856681258319872 |
|---|---|
| author | Maryam Bypour Mohammad Yekrangnia Mahdi Kioumarsi |
| author_facet | Maryam Bypour Mohammad Yekrangnia Mahdi Kioumarsi |
| author_sort | Maryam Bypour |
| collection | DOAJ |
| description | This study employs machine learning (ML) techniques to predict the compressive strength (fc′) of fly ash-based geopolymer concrete, utilizing a comprehensive set of experimental data. The analysis considered variables such as fly ash (FA) components, coarse and fine aggregates, alkaline activator molarity, and other additives. Six different ML models—AdaBoost, Decision Tree, Extra Tree, Random Forest, Gradient Boosting, and Extreme Gradient Boosting were used to predict fc′ of fly ash-based geopolymer concrete.The results reveal that the AdaBoost model outperformed the other models, achieving R2 score of 0.80 and RMSE of 6.60. Furthermore, the tuned models demonstrated superior accuracy compared to their default counterparts. The feature importance analysis using the Shapley values technique identified CaO as the most influential factor on fc′, with higher CaO levels leading to an increase in compressive strength. Additionally, an increase in the molarity of the NaOH alkaline activator positively impacted the target value. |
| format | Article |
| id | doaj-art-944a5f2e18114cd6b182ebcf782515a0 |
| institution | Kabale University |
| issn | 2666-7908 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Engineering and Technology |
| spelling | doaj-art-944a5f2e18114cd6b182ebcf782515a02025-02-12T05:32:57ZengElsevierCleaner Engineering and Technology2666-79082025-03-0125100899Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concreteMaryam Bypour0Mohammad Yekrangnia1Mahdi Kioumarsi2Department of Civil Engineering, Semnan University, Semnan, IranDepartment of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, IranDepartment of Built Environment, OsloMet – Oslo Metropolitan University, Oslo, Norway; Corresponding author.This study employs machine learning (ML) techniques to predict the compressive strength (fc′) of fly ash-based geopolymer concrete, utilizing a comprehensive set of experimental data. The analysis considered variables such as fly ash (FA) components, coarse and fine aggregates, alkaline activator molarity, and other additives. Six different ML models—AdaBoost, Decision Tree, Extra Tree, Random Forest, Gradient Boosting, and Extreme Gradient Boosting were used to predict fc′ of fly ash-based geopolymer concrete.The results reveal that the AdaBoost model outperformed the other models, achieving R2 score of 0.80 and RMSE of 6.60. Furthermore, the tuned models demonstrated superior accuracy compared to their default counterparts. The feature importance analysis using the Shapley values technique identified CaO as the most influential factor on fc′, with higher CaO levels leading to an increase in compressive strength. Additionally, an increase in the molarity of the NaOH alkaline activator positively impacted the target value.http://www.sciencedirect.com/science/article/pii/S2666790825000229Geopolymer concreteFly ashMachine learningAdaBoostShapley values |
| spellingShingle | Maryam Bypour Mohammad Yekrangnia Mahdi Kioumarsi Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete Cleaner Engineering and Technology Geopolymer concrete Fly ash Machine learning AdaBoost Shapley values |
| title | Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| title_full | Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| title_fullStr | Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| title_full_unstemmed | Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| title_short | Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| title_sort | machine learning driven optimization for predicting compressive strength in fly ash geopolymer concrete |
| topic | Geopolymer concrete Fly ash Machine learning AdaBoost Shapley values |
| url | http://www.sciencedirect.com/science/article/pii/S2666790825000229 |
| work_keys_str_mv | AT maryambypour machinelearningdrivenoptimizationforpredictingcompressivestrengthinflyashgeopolymerconcrete AT mohammadyekrangnia machinelearningdrivenoptimizationforpredictingcompressivestrengthinflyashgeopolymerconcrete AT mahdikioumarsi machinelearningdrivenoptimizationforpredictingcompressivestrengthinflyashgeopolymerconcrete |