Strength Prediction Models for Concrete Incorporating Fly Ash as a Waste Material.
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| Title: | Strength Prediction Models for Concrete Incorporating Fly Ash as a Waste Material. |
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| Authors: | Kumar, M.1 (AUTHOR) madan123kumar@gmail.com, Kumar, V.2 (AUTHOR) kumar2vijay@gmail.com, Priyadarshee, A.2 (AUTHOR) i.akashpriyadarshee1@gmail.com, Kumar Rahul, A.2 (AUTHOR) atulcivil.iitbhu@gmail.com, Kumar, R.3 (AUTHOR) ampuravi@gmail.com, Kumari, Shweta4 (AUTHOR) shweta@mitmuzaffarpur.org |
| Source: | Journal of Applied Engineering Sciences. May2026, Vol. 16 Issue 1, p47-56. 10p. |
| Subjects: | Machine learning, Boosting algorithms, Random forest algorithms, Compressive strength, Concrete, Multiple regression analysis, Support vector machines |
| Abstract: | A comparative machine learning–based methodology was adopted to predict the compressive strength of fly ash concrete using Multiple Linear Regression (MLR), Support Vector Regression (SVR), AdaBoost Regressor (ABR), Random Forest (RF), and Extreme Gradient Boosting models (XG). A dataset of 498 mix designs collected from published literature was used, considering cement, fine and coarse aggregate, fly ash content, water content, water–cement ratio, and curing period as input parameters. Model performance was evaluated using mean absolute error, root mean square error, and coefficient of determination. The Extreme Gradient Boosting model showed the best predictive capability (R² = 0.881; RMSE = 5.65 MPa). Sensitivity analysis identified curing period, cement content, and water content as the most influential variables. The results demonstrate reliable strength prediction and enable model comparison to support data-driven mix optimization for sustainable fly ash concrete (FAC). [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Engineering Sciences is the property of Paradigm Publishing Services and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193976377 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Strength Prediction Models for Concrete Incorporating Fly Ash as a Waste Material. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+M%2E%22">Kumar, M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> madan123kumar@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+V%2E%22">Kumar, V.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> kumar2vijay@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Priyadarshee%2C+A%2E%22">Priyadarshee, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> i.akashpriyadarshee1@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar+Rahul%2C+A%2E%22">Kumar Rahul, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> atulcivil.iitbhu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+R%2E%22">Kumar, R.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> ampuravi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumari%2C+Shweta%22">Kumari, Shweta</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> shweta@mitmuzaffarpur.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Engineering+Sciences%22">Journal of Applied Engineering Sciences</searchLink>. May2026, Vol. 16 Issue 1, p47-56. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Concrete%22">Concrete</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A comparative machine learning–based methodology was adopted to predict the compressive strength of fly ash concrete using Multiple Linear Regression (MLR), Support Vector Regression (SVR), AdaBoost Regressor (ABR), Random Forest (RF), and Extreme Gradient Boosting models (XG). A dataset of 498 mix designs collected from published literature was used, considering cement, fine and coarse aggregate, fly ash content, water content, water–cement ratio, and curing period as input parameters. Model performance was evaluated using mean absolute error, root mean square error, and coefficient of determination. The Extreme Gradient Boosting model showed the best predictive capability (R² = 0.881; RMSE = 5.65 MPa). Sensitivity analysis identified curing period, cement content, and water content as the most influential variables. The results demonstrate reliable strength prediction and enable model comparison to support data-driven mix optimization for sustainable fly ash concrete (FAC). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Engineering Sciences is the property of Paradigm Publishing Services and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2478/jaes-2026-0007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 47 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Compressive strength Type: general – SubjectFull: Concrete Type: general – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Strength Prediction Models for Concrete Incorporating Fly Ash as a Waste Material. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, M. – PersonEntity: Name: NameFull: Kumar, V. – PersonEntity: Name: NameFull: Priyadarshee, A. – PersonEntity: Name: NameFull: Kumar Rahul, A. – PersonEntity: Name: NameFull: Kumar, R. – PersonEntity: Name: NameFull: Kumari, Shweta IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22473769 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Journal of Applied Engineering Sciences Type: main |
| ResultId | 1 |
