Development of prediction models for shear strength of SFRCB using a machine learning approach

Sarveghadi, M; Gandomi, AH; Bolandi, H; Alavi, AH

Development of prediction models for shear strength of SFRCB using a machine learning approach

Sarveghadi, M Gandomi, AH

Bolandi, H Alavi, AH

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Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2019, 31 (7), pp. 2085 - 2094
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Sarveghadi, M	en_US
dc.contributor.author	Gandomi, AH https://orcid.org/0000-0002-2798-0104	en_US
dc.contributor.author	Bolandi, H	en_US
dc.contributor.author	Alavi, AH	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Neural Computing and Applications, 2019, 31 (7), pp. 2085 - 2094	en_US
dc.identifier.issn	0941-0643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139169
dc.description.abstract	© 2015, The Natural Computing Applications Forum. In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.	en_US
dc.relation.ispartof	Neural Computing and Applications	en_US
dc.relation.isbasedon	10.1007/s00521-015-1997-6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Development of prediction models for shear strength of SFRCB using a machine learning approach	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	31	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

© 2015, The Natural Computing Applications Forum. In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.

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http://hdl.handle.net/10453/139169