Innovative Machine Learning Techniques for Sustainable Compressive Strength Estimation of Ultrahigh‐Performance Concrete.
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| Title: | Innovative Machine Learning Techniques for Sustainable Compressive Strength Estimation of Ultrahigh‐Performance Concrete. |
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| Authors: | Diao, Guangcheng1 (AUTHOR) syzxy103@163.com, Baghban, Alireza2 (AUTHOR) baghban1369@gmail.com, Binwal, Shikha (AUTHOR) sbinwal@wiley.com |
| Source: | Advances in Civil Engineering. 6/29/2026, Vol. 2026, p1-10. 10p. |
| Subjects: | Gaussian processes, Compressive strength, Kernel functions, Sensitivity analysis, Machine learning, Outlier detection, High strength concrete |
| Abstract: | In this work, the compressive strength of high‐performance concrete (HPC) mixtures was estimated by four various machine learning methods based on Gaussian process regression (GPR). For this purpose, kernel functions including exponential, Matern, rational quadratic, and squared exponential were employed in modeling. Also, a dataset with a number of 1030 points was collected so that its features were fine aggregate, blast furnace slag, coarse aggregate, water, superplasticizer, cement, age, and fly ash. According to mathematical and visual investigations, the GPR method including Matern kernel function is the best calculator for the determination of compressive strength. The R‐squared values in training, validation, and testing steps were determined to be 0.9863, 0.9214, and 0.9655, respectively, and these values confirmed the mentioned claim. In addition, the outlier detection process was conducted on the databank, and the reliability of available data points was confirmed. Also, the sensitivity analysis explained the effect of different inputs on the compressive strength was assessed, and it was obtained that age is the most effective parameter on the target. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Civil Engineering is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194946722 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Innovative Machine Learning Techniques for Sustainable Compressive Strength Estimation of Ultrahigh‐Performance Concrete. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Diao%2C+Guangcheng%22">Diao, Guangcheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> syzxy103@163.com</i><br /><searchLink fieldCode="AR" term="%22Baghban%2C+Alireza%22">Baghban, Alireza</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> baghban1369@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Binwal%2C+Shikha%22">Binwal, Shikha</searchLink> (AUTHOR)<i> sbinwal@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Civil+Engineering%22">Advances in Civil Engineering</searchLink>. 6/29/2026, Vol. 2026, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22High+strength+concrete%22">High strength concrete</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this work, the compressive strength of high‐performance concrete (HPC) mixtures was estimated by four various machine learning methods based on Gaussian process regression (GPR). For this purpose, kernel functions including exponential, Matern, rational quadratic, and squared exponential were employed in modeling. Also, a dataset with a number of 1030 points was collected so that its features were fine aggregate, blast furnace slag, coarse aggregate, water, superplasticizer, cement, age, and fly ash. According to mathematical and visual investigations, the GPR method including Matern kernel function is the best calculator for the determination of compressive strength. The R‐squared values in training, validation, and testing steps were determined to be 0.9863, 0.9214, and 0.9655, respectively, and these values confirmed the mentioned claim. In addition, the outlier detection process was conducted on the databank, and the reliability of available data points was confirmed. Also, the sensitivity analysis explained the effect of different inputs on the compressive strength was assessed, and it was obtained that age is the most effective parameter on the target. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Civil Engineering is the property of Wiley-Blackwell 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: 10.1155/adce/2499864 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Gaussian processes Type: general – SubjectFull: Compressive strength Type: general – SubjectFull: Kernel functions Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: High strength concrete Type: general Titles: – TitleFull: Innovative Machine Learning Techniques for Sustainable Compressive Strength Estimation of Ultrahigh‐Performance Concrete. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Diao, Guangcheng – PersonEntity: Name: NameFull: Baghban, Alireza – PersonEntity: Name: NameFull: Binwal, Shikha IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 06 Text: 6/29/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16878086 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Advances in Civil Engineering Type: main |
| ResultId | 1 |
