Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete
In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the propo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6682283 |
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| _version_ | 1849691021762363392 |
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| author | Muhammad Izhar Shah Shazim Ali Memon Muhammad Sohaib Khan Niazi Muhammad Nasir Amin Fahid Aslam Muhammad Faisal Javed |
| author_facet | Muhammad Izhar Shah Shazim Ali Memon Muhammad Sohaib Khan Niazi Muhammad Nasir Amin Fahid Aslam Muhammad Faisal Javed |
| author_sort | Muhammad Izhar Shah |
| collection | DOAJ |
| description | In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment. |
| format | Article |
| id | doaj-art-e91fc6f827d84c9f88ffdda9ba520fe4 |
| institution | DOAJ |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-e91fc6f827d84c9f88ffdda9ba520fe42025-08-20T03:21:09ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66822836682283Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable ConcreteMuhammad Izhar Shah0Shazim Ali Memon1Muhammad Sohaib Khan Niazi2Muhammad Nasir Amin3Fahid Aslam4Muhammad Faisal Javed5Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, KazakhstanCivil Engineering Department, Qurtuba University of Science and Information Technology, Dera Ismail Khan, PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P. O. 380, Al-Hofuf, Al Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanIn this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.http://dx.doi.org/10.1155/2021/6682283 |
| spellingShingle | Muhammad Izhar Shah Shazim Ali Memon Muhammad Sohaib Khan Niazi Muhammad Nasir Amin Fahid Aslam Muhammad Faisal Javed Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete Advances in Civil Engineering |
| title | Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete |
| title_full | Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete |
| title_fullStr | Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete |
| title_full_unstemmed | Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete |
| title_short | Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete |
| title_sort | machine learning based modeling with optimization algorithm for predicting mechanical properties of sustainable concrete |
| url | http://dx.doi.org/10.1155/2021/6682283 |
| work_keys_str_mv | AT muhammadizharshah machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete AT shazimalimemon machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete AT muhammadsohaibkhanniazi machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete AT muhammadnasiramin machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete AT fahidaslam machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete AT muhammadfaisaljaved machinelearningbasedmodelingwithoptimizationalgorithmforpredictingmechanicalpropertiesofsustainableconcrete |