Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement

Gandomi, AH; Alavi, AH; Gandomi, M; Kazemi, S

Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement

Gandomi, AH Alavi, AH Gandomi, M Kazemi, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Measurement, 2017, 95, pp. 367-376
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Gandomi, AH
dc.contributor.author	Alavi, AH
dc.contributor.author	Gandomi, M
dc.contributor.author	Kazemi, S
dc.date.accessioned	2022-08-25T02:28:52Z
dc.date.available	2022-08-25T02:28:52Z
dc.date.issued	2017-01-01
dc.identifier.citation	Measurement, 2017, 95, pp. 367-376
dc.identifier.issn	0263-2241
dc.identifier.issn	1873-412X
dc.identifier.uri	http://hdl.handle.net/10453/160823
dc.description.abstract	In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) beams with stirrups. The derived model relates the shear strength to mechanical and geometrical properties. The model is developed using a database containing 466 experimental test results gathered from the literature. Sensitivity and parametric analyses are performed for further verification of the model. The comparative study proves the superior performance of the GEP model compared to the expressions developed in several codes of practice.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Measurement
dc.relation.isbasedon	10.1016/j.measurement.2016.10.024
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement
dc.type	Journal Article
utslib.citation.volume	95
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T02:28:50Z
pubs.publication-status	Published
pubs.volume	95

Abstract:

In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) beams with stirrups. The derived model relates the shear strength to mechanical and geometrical properties. The model is developed using a database containing 466 experimental test results gathered from the literature. Sensitivity and parametric analyses are performed for further verification of the model. The comparative study proves the superior performance of the GEP model compared to the expressions developed in several codes of practice.

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http://hdl.handle.net/10453/160823