An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand

Armaghani, DJ; Rasekh, H; Asteris, PG

An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand

Armaghani, DJ Rasekh, H

Asteris, PG

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Publisher:: TECHNO-PRESS
Publication Type:: Journal Article
Citation:: COMPUTERS AND CONCRETE, 2024, 33, (1), pp. 77-90
Issue Date:: 2024-01

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Field	Value	Language
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Rasekh, H https://orcid.org/0000-0003-2064-0753
dc.contributor.author	Asteris, PG
dc.date.accessioned	2025-02-20T21:37:17Z
dc.date.available	2025-02-20T21:37:17Z
dc.date.issued	2024-01
dc.identifier.citation	COMPUTERS AND CONCRETE, 2024, 33, (1), pp. 77-90
dc.identifier.issn	1598-8198
dc.identifier.issn	1598-818X
dc.identifier.uri	http://hdl.handle.net/10453/185236
dc.language	English
dc.publisher	TECHNO-PRESS
dc.relation.ispartof	COMPUTERS AND CONCRETE
dc.relation.isbasedon	10.12989/cac.2024.33.1.077
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Building & Construction
dc.subject.classification	4005 Civil engineering
dc.title	An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	0905 Civil Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
utslib.copyright.status	in_progress	*
dc.date.updated	2025-02-20T21:37:15Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	1

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http://hdl.handle.net/10453/185236