Data-driven approaches for strength prediction of alkali-activated composites
Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509524000718 |
| _version_ | 1797320988128444416 |
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| author | Mohammed Awad Abuhussain Ayaz Ahmad Muhammad Nasir Amin Fadi Althoey Yaser Gamil Taoufik Najeh |
| author_facet | Mohammed Awad Abuhussain Ayaz Ahmad Muhammad Nasir Amin Fadi Althoey Yaser Gamil Taoufik Najeh |
| author_sort | Mohammed Awad Abuhussain |
| collection | DOAJ |
| description | Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs. |
| first_indexed | 2024-03-08T04:51:01Z |
| format | Article |
| id | doaj.art-d2e0c0f4565c47578cae835380b3f015 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-08T04:51:01Z |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-d2e0c0f4565c47578cae835380b3f0152024-02-08T05:08:56ZengElsevierCase Studies in Construction Materials2214-50952024-07-0120e02920Data-driven approaches for strength prediction of alkali-activated compositesMohammed Awad Abuhussain0Ayaz Ahmad1Muhammad Nasir Amin2Fadi Althoey3Yaser Gamil4Taoufik Najeh5Architectural Engineering Department, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan; Corresponding authors.Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, MalaysiaOperation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden; Corresponding authors.Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs.http://www.sciencedirect.com/science/article/pii/S2214509524000718Alkali-activated compositesInput parametersCompressive strengthPredictionMachine learning |
| spellingShingle | Mohammed Awad Abuhussain Ayaz Ahmad Muhammad Nasir Amin Fadi Althoey Yaser Gamil Taoufik Najeh Data-driven approaches for strength prediction of alkali-activated composites Case Studies in Construction Materials Alkali-activated composites Input parameters Compressive strength Prediction Machine learning |
| title | Data-driven approaches for strength prediction of alkali-activated composites |
| title_full | Data-driven approaches for strength prediction of alkali-activated composites |
| title_fullStr | Data-driven approaches for strength prediction of alkali-activated composites |
| title_full_unstemmed | Data-driven approaches for strength prediction of alkali-activated composites |
| title_short | Data-driven approaches for strength prediction of alkali-activated composites |
| title_sort | data driven approaches for strength prediction of alkali activated composites |
| topic | Alkali-activated composites Input parameters Compressive strength Prediction Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214509524000718 |
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