Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction
The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423002600 |
| _version_ | 1797858743442997248 |
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| author | Jianyu Yang Pengxiao Jiang Roz-Ud-Din Nassar Salman Ali Suhail Muhammad Sufian Ahmed Farouk Deifalla |
| author_facet | Jianyu Yang Pengxiao Jiang Roz-Ud-Din Nassar Salman Ali Suhail Muhammad Sufian Ahmed Farouk Deifalla |
| author_sort | Jianyu Yang |
| collection | DOAJ |
| description | The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector. |
| first_indexed | 2024-04-09T21:19:21Z |
| format | Article |
| id | doaj.art-4b980be4749e4a13a00db572dcb4919c |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-04-09T21:19:21Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-4b980be4749e4a13a00db572dcb4919c2023-03-28T06:47:38ZengElsevierJournal of Materials Research and Technology2238-78542023-03-012336763696Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable constructionJianyu Yang0Pengxiao Jiang1Roz-Ud-Din Nassar2Salman Ali Suhail3Muhammad Sufian4Ahmed Farouk Deifalla5School of Civil Engineering, Changsha University of Science & Technology, Hunan,Changsha, 410000, PR. ChinaChina Construction Fifth Engineering Division Corp., Lt, Hunan, Changsha, 410000, PR. ChinaDepartment of Civil and Infrastructure Engineering, American University of Ras Al Khaimah, Ras Al Khaimah 10021, United Arab EmiratesDepartment of Civil Engineering, University of Lahore (UOL), Lahore 54590, PakistanSchool of Civil Engineering, Southeast University, Nanjing 210096, PR China; Corresponding author.Structural Engineering and Construction Management Department, Future University in Egypt, 11835, New Cairo, EgyptThe ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.http://www.sciencedirect.com/science/article/pii/S2238785423002600Ceramic waste powderWasteConstruction materialBuilding materialConcrete |
| spellingShingle | Jianyu Yang Pengxiao Jiang Roz-Ud-Din Nassar Salman Ali Suhail Muhammad Sufian Ahmed Farouk Deifalla Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction Journal of Materials Research and Technology Ceramic waste powder Waste Construction material Building material Concrete |
| title | Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction |
| title_full | Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction |
| title_fullStr | Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction |
| title_full_unstemmed | Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction |
| title_short | Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction |
| title_sort | experimental investigation and ai prediction modelling of ceramic waste powder concrete an approach towards sustainable construction |
| topic | Ceramic waste powder Waste Construction material Building material Concrete |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423002600 |
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