Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer
This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopol...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423004143 |
| _version_ | 1797798885328945152 |
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| author | Sohaib Nazar Jian Yang Muhammad Nasir Amin Kaffayatullah Khan Muhammad Ashraf Fahid Aslam Mohammad Faisal Javed Sayed M. Eldin |
| author_facet | Sohaib Nazar Jian Yang Muhammad Nasir Amin Kaffayatullah Khan Muhammad Ashraf Fahid Aslam Mohammad Faisal Javed Sayed M. Eldin |
| author_sort | Sohaib Nazar |
| collection | DOAJ |
| description | This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized. |
| first_indexed | 2024-03-13T04:10:30Z |
| format | Article |
| id | doaj.art-8eecc0a0bc234309bac49d37f502a8e5 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-13T04:10:30Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-8eecc0a0bc234309bac49d37f502a8e52023-06-21T06:55:21ZengElsevierJournal of Materials Research and Technology2238-78542023-05-0124100124Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymerSohaib Nazar0Jian Yang1Muhammad Nasir Amin2Kaffayatullah Khan3Muhammad Ashraf4Fahid Aslam5Mohammad Faisal Javed6Sayed M. Eldin7State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 200240, PR China; Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Department of Civil Engineering, Comsats University Islamabad-Abbottabad Campus, PakistanState Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 200240, PR China; Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China; School of Civil Engineering, University of Birmingham, Birmingham B15 2TT, UK; Corresponding author.Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, PakistanDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, Comsats University Islamabad-Abbottabad Campus, PakistanCenter of Research, Faculty of Engineering, Future University, New Cairo 11835, EgyptThis study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.http://www.sciencedirect.com/science/article/pii/S2238785423004143Geopolymer concreteCompressive strengthSlumpArtificial intelligence techniquesMachine learning algorithms (MLA)Artificial neural networks |
| spellingShingle | Sohaib Nazar Jian Yang Muhammad Nasir Amin Kaffayatullah Khan Muhammad Ashraf Fahid Aslam Mohammad Faisal Javed Sayed M. Eldin Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer Journal of Materials Research and Technology Geopolymer concrete Compressive strength Slump Artificial intelligence techniques Machine learning algorithms (MLA) Artificial neural networks |
| title | Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer |
| title_full | Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer |
| title_fullStr | Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer |
| title_full_unstemmed | Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer |
| title_short | Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer |
| title_sort | machine learning interpretable prediction models to evaluate the slump and strength of fly ash based geopolymer |
| topic | Geopolymer concrete Compressive strength Slump Artificial intelligence techniques Machine learning algorithms (MLA) Artificial neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423004143 |
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