Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods
This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for e...
| Published in: | Journal of Soft Computing in Civil Engineering |
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| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2024-04-01
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| Online Access: | https://www.jsoftcivil.com/article_175577_528c8fb9860492115321373d81d9e76d.pdf |
| _version_ | 1849477669868011520 |
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| author | Ismail Husein Ramaswamy Sivaraman Sarwar Hasan Mohmmad Forqan Ali Hussein Al-Khafaji Sokaina Issa Kadhim Yousof Rezakhani |
| author_facet | Ismail Husein Ramaswamy Sivaraman Sarwar Hasan Mohmmad Forqan Ali Hussein Al-Khafaji Sokaina Issa Kadhim Yousof Rezakhani |
| author_sort | Ismail Husein |
| collection | DOAJ |
| container_title | Journal of Soft Computing in Civil Engineering |
| description | This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP. |
| format | Article |
| id | doaj-art-4bd0aa74a5df40bd8961b199bbc8e129 |
| institution | Directory of Open Access Journals |
| issn | 2588-2872 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| spelling | doaj-art-4bd0aa74a5df40bd8961b199bbc8e1292025-08-20T03:14:28ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722024-04-018211810.22115/scce.2023.389726.1619175577Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS MethodsIsmail Husein0Ramaswamy Sivaraman1Sarwar Hasan Mohmmad2Forqan Ali Hussein Al-Khafaji3Sokaina Issa Kadhim4Yousof Rezakhani5Department of Mathematics, Universitas Islam Negeri Sumatera Utara, Medan, IndonesiaAssociate Professor, Department of Mathematics, Dwaraka Doss Goverdhan Doss Vaishnav College, Arumbakkam, University of Madras, Chennai, IndiaPh.D., Lecture, College of Engineering, Sulaimani Polytechnic University, Sulaimani, IraqDepartment of Media, Al-Mustaqbal University College, 51001, Babylon, Hillah, IraqBuilding and Construction Technical Engineering Department, College of Technical Engineering, The Islamic university, Najaf, IraqDepartment of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, IranThis paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP.https://www.jsoftcivil.com/article_175577_528c8fb9860492115321373d81d9e76d.pdfgepmarsslump concretedata-driven methods |
| spellingShingle | Ismail Husein Ramaswamy Sivaraman Sarwar Hasan Mohmmad Forqan Ali Hussein Al-Khafaji Sokaina Issa Kadhim Yousof Rezakhani Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods gep mars slump concrete data-driven methods |
| title | Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods |
| title_full | Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods |
| title_fullStr | Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods |
| title_full_unstemmed | Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods |
| title_short | Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods |
| title_sort | predictive equations for estimation of the slump of concrete using gep and mars methods |
| topic | gep mars slump concrete data-driven methods |
| url | https://www.jsoftcivil.com/article_175577_528c8fb9860492115321373d81d9e76d.pdf |
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