Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Prediction of compressive strength of nano silica and micro silica from rice husk ash using multivariate regression models ; volume:3 ; number:1 ; day:11 ; month:12 ; year:2024 ; pages:1-13 ; date:12.2024
AI in civil engineering ; 3, Heft 1 (11.12.2024), 1-13, 12.2024
- Creator
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Raji, Mustapha A.
Falola, Boluwatife M.
Enikuomehin, Jesse T.
Oyelade, Akintoye O.
Abiodun, Yetunde O.
Olaniyi, Yusuf A.
Olagunju, Olusola G.
Anyaegbuna, Kosisochukwu L.
Abdulkareem, Musa O.
Fapohunda, Christopher A.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s43503-024-00043-5
- URN
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urn:nbn:de:101:1-2502230605267.948090475896
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:31 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Raji, Mustapha A.
- Falola, Boluwatife M.
- Enikuomehin, Jesse T.
- Oyelade, Akintoye O.
- Abiodun, Yetunde O.
- Olaniyi, Yusuf A.
- Olagunju, Olusola G.
- Anyaegbuna, Kosisochukwu L.
- Abdulkareem, Musa O.
- Fapohunda, Christopher A.
- SpringerLink (Online service)
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Rice husk silica blended fillers for engine mount application
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An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete
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