Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms
There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete....
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523009257 |
| _version_ | 1797399058166317056 |
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| author | Adil Khan Majid Khan Mohsin Ali Murad Khan Asad Ullah Khan Muhammad Shakeel Muhammad Fawad Taoufik Najeh Yaser Gamil |
| author_facet | Adil Khan Majid Khan Mohsin Ali Murad Khan Asad Ullah Khan Muhammad Shakeel Muhammad Fawad Taoufik Najeh Yaser Gamil |
| author_sort | Adil Khan |
| collection | DOAJ |
| description | There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector. |
| first_indexed | 2024-03-09T01:33:57Z |
| format | Article |
| id | doaj.art-232f39ddcf1f4239aaefc2bb97e02130 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T01:33:57Z |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-232f39ddcf1f4239aaefc2bb97e021302023-12-09T06:06:20ZengElsevierCase Studies in Construction Materials2214-50952024-07-0120e02744Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithmsAdil Khan0Majid Khan1Mohsin Ali2Murad Khan3Asad Ullah Khan4Muhammad Shakeel5Muhammad Fawad6Taoufik Najeh7Yaser Gamil8Department of Advanced Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire BD7 1DP, UKDepartment of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, PakistanSchool of Civil Engineering, Southeast University Nanjing, ChinaSchool of Civil Engineering, Tianjin University, Tianjin 300354, ChinaDepartment of Civil Engineering, COMSATS University, Islamabad, Abbottabad Campus 22060, PakistanDepartment of Civil Engineering, University of Engineering and Technology, Peshawar 25120, PakistanSilesian University of Technology Poland, Poland; Budapest University of Technology and Economics Hungary, HungaryOperation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden; Corresponding author.Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, MalaysiaThere has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.http://www.sciencedirect.com/science/article/pii/S2214509523009257Fly ashAbrasion resistanceGene expression programmingMulti-expression programmingSHAP |
| spellingShingle | Adil Khan Majid Khan Mohsin Ali Murad Khan Asad Ullah Khan Muhammad Shakeel Muhammad Fawad Taoufik Najeh Yaser Gamil Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms Case Studies in Construction Materials Fly ash Abrasion resistance Gene expression programming Multi-expression programming SHAP |
| title | Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms |
| title_full | Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms |
| title_fullStr | Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms |
| title_full_unstemmed | Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms |
| title_short | Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms |
| title_sort | predictive modeling for depth of wear of concrete modified with fly ash a comparative analysis of genetic programming based algorithms |
| topic | Fly ash Abrasion resistance Gene expression programming Multi-expression programming SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523009257 |
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