Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions
Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/15/5207 |
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| author | Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Ayaz Ahmad Sohaib Nazar Anas Abdulalim Alabdullah |
| author_facet | Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Ayaz Ahmad Sohaib Nazar Anas Abdulalim Alabdullah |
| author_sort | Muhammad Nasir Amin |
| collection | DOAJ |
| description | Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R<sup>2</sup> value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R<sup>2</sup> value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength. |
| first_indexed | 2024-03-09T05:15:55Z |
| format | Article |
| id | doaj.art-60e8fadfb1bb4d0d9fb700c756586b83 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T05:15:55Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-60e8fadfb1bb4d0d9fb700c756586b832023-12-03T12:45:17ZengMDPI AGMaterials1996-19442022-07-011515520710.3390/ma15155207Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and InteractionsMuhammad Nasir Amin0Waqas Ahmad1Kaffayatullah Khan2Ayaz Ahmad3Sohaib Nazar4Anas Abdulalim Alabdullah5Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaMaREI Centre, Ryan Institute and School of Engineering, College of Science and Engineering, National University of Ireland Galway, H91 HX31 Galway, IrelandDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaIncorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R<sup>2</sup> value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R<sup>2</sup> value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.https://www.mdpi.com/1996-1944/15/15/5207pollutionwasteaggregateconcretebuildingconstruction |
| spellingShingle | Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Ayaz Ahmad Sohaib Nazar Anas Abdulalim Alabdullah Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions Materials pollution waste aggregate concrete building construction |
| title | Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions |
| title_full | Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions |
| title_fullStr | Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions |
| title_full_unstemmed | Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions |
| title_short | Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions |
| title_sort | use of artificial intelligence for predicting parameters of sustainable concrete and raw ingredient effects and interactions |
| topic | pollution waste aggregate concrete building construction |
| url | https://www.mdpi.com/1996-1944/15/15/5207 |
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