Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.
artificial intelligence
compressive strength
geopolymer concrete
machine learning
prediction models
Journal
Polymers
ISSN: 2073-4360
Titre abrégé: Polymers (Basel)
Pays: Switzerland
ID NLM: 101545357
Informations de publication
Date de publication:
23 May 2022
23 May 2022
Historique:
received:
29
03
2022
revised:
11
05
2022
accepted:
18
05
2022
entrez:
28
5
2022
pubmed:
29
5
2022
medline:
29
5
2022
Statut:
epublish
Résumé
The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na
Identifiants
pubmed: 35632011
pii: polym14102128
doi: 10.3390/polym14102128
pmc: PMC9147713
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : King Faisal University
ID : This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. AN000500]
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