Kernel-based models for prediction of cement compressive strength.
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| Title: | Kernel-based models for prediction of cement compressive strength. |
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| Authors: | Verma, Mohit mohitverma@serc.res.in, Thirumalaiselvi, A., Rajasankar, J. |
| Source: | Neural Computing & Applications. Dec2017 Supplement 1, Vol. 28, p1083-1100. 18p. |
| Subjects: | Cement testing, Compressive strength, Kernel functions, Particle swarm optimization, Support vector machines |
| Abstract: | This paper employs three different kernel-based models-support vector regression (SVR), relevance vector machine (RVM) and Gaussian process regression (GPR)-for the prediction of cement compressive strength. The input variables for the model are taken as CS (%), SO (%), Alkali (%) and Blaine (cm/g), while the output is 28-day cement compressive strength (N/mm) of the cement. The hyperparameters of the SVR are obtained using two different metaheuristic optimization algorithms-particle swarm optimization (PSO) and symbiotic organism search (SOS). Trial-and-error-based approach is used for arriving at the hyperparameters of RVM and GPR. The compressive strength predicted using different kernel-based models is also compared with that obtained from ANN and fuzzy logic models reported in the literature. The performance of the different kernel-based models is benchmarked using six different error indices and residual analysis. The performance of the kernel-based models is found to be at par with ANN. The better generalization capability and excellent empirical performance of the kernel-based models overcome the disadvantages associated with ANN and provide a good tool for the prediction of the cement compressive strength. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature 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: 126403694 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Kernel-based models for prediction of cement compressive strength. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Verma%2C+Mohit%22">Verma, Mohit</searchLink><i> mohitverma@serc.res.in</i><br /><searchLink fieldCode="AR" term="%22Thirumalaiselvi%2C+A%2E%22">Thirumalaiselvi, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Rajasankar%2C+J%2E%22">Rajasankar, J.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Dec2017 Supplement 1, Vol. 28, p1083-1100. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cement+testing%22">Cement testing</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="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper employs three different kernel-based models-support vector regression (SVR), relevance vector machine (RVM) and Gaussian process regression (GPR)-for the prediction of cement compressive strength. The input variables for the model are taken as CS (%), SO (%), Alkali (%) and Blaine (cm/g), while the output is 28-day cement compressive strength (N/mm) of the cement. The hyperparameters of the SVR are obtained using two different metaheuristic optimization algorithms-particle swarm optimization (PSO) and symbiotic organism search (SOS). Trial-and-error-based approach is used for arriving at the hyperparameters of RVM and GPR. The compressive strength predicted using different kernel-based models is also compared with that obtained from ANN and fuzzy logic models reported in the literature. The performance of the different kernel-based models is benchmarked using six different error indices and residual analysis. The performance of the kernel-based models is found to be at par with ANN. The better generalization capability and excellent empirical performance of the kernel-based models overcome the disadvantages associated with ANN and provide a good tool for the prediction of the cement compressive strength. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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: 10.1007/s00521-016-2419-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1083 Subjects: – SubjectFull: Cement testing Type: general – SubjectFull: Compressive strength Type: general – SubjectFull: Kernel functions Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Kernel-based models for prediction of cement compressive strength. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Verma, Mohit – PersonEntity: Name: NameFull: Thirumalaiselvi, A. – PersonEntity: Name: NameFull: Rajasankar, J. IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 12 Text: Dec2017 Supplement 1 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 28 Titles: – TitleFull: Neural Computing & Applications Type: main |
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