Data-Driven Prediction Models For Total Shear Strength of Reinforced Concrete Beams With Fiber Reinforced Polymers Using An Evolutionary Machine Learning Approach

Anvari, AT Babanajad, S Gandomi, AH

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Field	Value	Language
dc.contributor.author	Anvari, AT
dc.contributor.author	Babanajad, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-01-19T03:42:52Z
dc.date.available	2023-01-19T03:42:52Z
dc.date.issued	2023-02-01
dc.identifier.citation	Engineering Structures, 2023, 276, pp. 115292
dc.identifier.issn	0141-0296
dc.identifier.uri	http://hdl.handle.net/10453/165201
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Engineering Structures
dc.relation.isbasedon	10.1016/j.engstruct.2022.115292
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Civil Engineering
dc.title	Data-Driven Prediction Models For Total Shear Strength of Reinforced Concrete Beams With Fiber Reinforced Polymers Using An Evolutionary Machine Learning Approach
dc.type	Journal Article
utslib.citation.volume	276
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-01-19T03:42:51Z
pubs.publication-status	Accepted
pubs.volume	276

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