A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis
This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423014552 |
| _version_ | 1797745180843966464 |
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| author | Xinliang Zheng Yi Xie Xujiao Yang Muhammad Nasir Amin Sohaib Nazar Suleman Ayub Khan Fadi Althoey Ahmed Farouk Deifalla |
| author_facet | Xinliang Zheng Yi Xie Xujiao Yang Muhammad Nasir Amin Sohaib Nazar Suleman Ayub Khan Fadi Althoey Ahmed Farouk Deifalla |
| author_sort | Xinliang Zheng |
| collection | DOAJ |
| description | This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs. |
| first_indexed | 2024-03-12T15:19:42Z |
| format | Article |
| id | doaj.art-3902bf02691b41a5913cef23e85fb122 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-12T15:19:42Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-3902bf02691b41a5913cef23e85fb1222023-08-11T05:33:55ZengElsevierJournal of Materials Research and Technology2238-78542023-07-012540744093A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysisXinliang Zheng0Yi Xie1Xujiao Yang2Muhammad Nasir Amin3Sohaib Nazar4Suleman Ayub Khan5Fadi Althoey6Ahmed Farouk Deifalla7School of Civil Engineering and Transportation, Beihua University, Jilin, 132013, ChinaSchool of Civil Engineering and Transportation, Beihua University, Jilin, 132013, China; Corresponding author.School of Civil Engineering and Transportation, Beihua University, Jilin, 132013, ChinaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; Corresponding author.Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Structural Engineering and Construction Management, Future University in Egypt, New Cairo City, 11835, EgyptThis research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs.http://www.sciencedirect.com/science/article/pii/S2238785423014552Alkali-activated materialsSustainable materialsCompressive strengthPrediction modelsSHAP analysis |
| spellingShingle | Xinliang Zheng Yi Xie Xujiao Yang Muhammad Nasir Amin Sohaib Nazar Suleman Ayub Khan Fadi Althoey Ahmed Farouk Deifalla A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis Journal of Materials Research and Technology Alkali-activated materials Sustainable materials Compressive strength Prediction models SHAP analysis |
| title | A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis |
| title_full | A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis |
| title_fullStr | A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis |
| title_full_unstemmed | A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis |
| title_short | A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis |
| title_sort | data driven approach to predict the compressive strength of alkali activated materials and correlation of influencing parameters using shapley additive explanations shap analysis |
| topic | Alkali-activated materials Sustainable materials Compressive strength Prediction models SHAP analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423014552 |
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