Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete
This study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. A random forest (RF) model was optimized using six metaheuristic algorithms: diffe...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Journal of Natural Fibers |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2025.2509123 |
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| author | Mana Alyami Muhammad Faisal Javed Irfan Ullah Hisham Alabduljabbar Furqan Ahmad |
| author_facet | Mana Alyami Muhammad Faisal Javed Irfan Ullah Hisham Alabduljabbar Furqan Ahmad |
| author_sort | Mana Alyami |
| collection | DOAJ |
| description | This study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. A random forest (RF) model was optimized using six metaheuristic algorithms: differential evolution (DEA), human felicity algorithm (HFA), nuclear reaction optimization (NRO), lightning search algorithm (LSA), Harris hawks optimization (HHO), and tunicate swarm algorithm (TSA). Among these, the NRO-RF model achieved the highest performance with an R2 of 0.931, surpassing TSA-RF (0.918), HHO-RF (0.846), LSA-RF (0.828), HFA-RF (0.811), and DEA-RF (0.794). These hybrid models not only offered high predictive accuracy but also delivered stable and generalizable predictions across varied mix proportions, supporting more reliable AAC design and quality control. Interpretability techniques revealed that higher SiO₂/Na₂O ratios (S/N), sodium hydroxide (NaOH), and blast furnace slag ratio (BFSR) positively influenced CS, while excessive water (W), aggregate (Agg), and precursor content (PC) had negative effects. These insights provide practical guidance for optimizing mix designs. Additionally, a user-friendly graphical interface was developed to facilitate easy CS prediction, reducing reliance on physical testing and promoting efficient, data-driven decision-making in AAC development. |
| format | Article |
| id | doaj-art-7d6fdb6ab9794bd192c60a78dad5e4b1 |
| institution | OA Journals |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-7d6fdb6ab9794bd192c60a78dad5e4b12025-08-20T02:02:25ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2025-12-0122110.1080/15440478.2025.2509123Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated ConcreteMana Alyami0Muhammad Faisal Javed1Irfan Ullah2Hisham Alabduljabbar3Furqan Ahmad4Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi, PakistanDepartment of Civil and Transportation Engineering, Hohai University, Nanjing, ChinaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaUNHCR, AfghanistanThis study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. A random forest (RF) model was optimized using six metaheuristic algorithms: differential evolution (DEA), human felicity algorithm (HFA), nuclear reaction optimization (NRO), lightning search algorithm (LSA), Harris hawks optimization (HHO), and tunicate swarm algorithm (TSA). Among these, the NRO-RF model achieved the highest performance with an R2 of 0.931, surpassing TSA-RF (0.918), HHO-RF (0.846), LSA-RF (0.828), HFA-RF (0.811), and DEA-RF (0.794). These hybrid models not only offered high predictive accuracy but also delivered stable and generalizable predictions across varied mix proportions, supporting more reliable AAC design and quality control. Interpretability techniques revealed that higher SiO₂/Na₂O ratios (S/N), sodium hydroxide (NaOH), and blast furnace slag ratio (BFSR) positively influenced CS, while excessive water (W), aggregate (Agg), and precursor content (PC) had negative effects. These insights provide practical guidance for optimizing mix designs. Additionally, a user-friendly graphical interface was developed to facilitate easy CS prediction, reducing reliance on physical testing and promoting efficient, data-driven decision-making in AAC development.https://www.tandfonline.com/doi/10.1080/15440478.2025.2509123Alkali-activated concretehybrid machine learningrandom forestmodel interpretabilitymix design optimizationcompressive strength |
| spellingShingle | Mana Alyami Muhammad Faisal Javed Irfan Ullah Hisham Alabduljabbar Furqan Ahmad Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete Journal of Natural Fibers Alkali-activated concrete hybrid machine learning random forest model interpretability mix design optimization compressive strength |
| title | Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete |
| title_full | Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete |
| title_fullStr | Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete |
| title_full_unstemmed | Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete |
| title_short | Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete |
| title_sort | hybrid metaheuristic optimized random forest models for predicting compressive strength of alkali activated concrete |
| topic | Alkali-activated concrete hybrid machine learning random forest model interpretability mix design optimization compressive strength |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2025.2509123 |
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