Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machin...
Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches
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Full title
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Publisher
Switzerland: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Switzerland: MDPI AG
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Scope and Contents
Contents
The entraining and distribution of air voids in the concrete matrix is a complex process that makes the mechanical properties of lightweight foamed concrete (LFC) highly unpredictable. To study the complex nature of aerated concrete, a reliable and robust prediction model is required, employing different machine learning (ML) techniques. This study...
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Full title
Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches
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Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9102231
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9102231
Other Identifiers
ISSN
1996-1944
E-ISSN
1996-1944
DOI
10.3390/ma15093166
