Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete

Vakhshouri, B; Nejadi, S

Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete

Vakhshouri, B Nejadi, S

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Publisher:: Foundation of Computer Science
Publication Type:: Journal Article
Citation:: International Journal of Computer Applications, 2014, 101 (5), pp. 39 - 48
Issue Date:: 2014-09-18

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Field	Value	Language
dc.contributor.author	Vakhshouri, B	en_US
dc.contributor.author	Nejadi, S	en_US
dc.date.issued	2014-09-18	en_US
dc.identifier.citation	International Journal of Computer Applications, 2014, 101 (5), pp. 39 - 48	en_US
dc.identifier.issn	0975-8887	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30417
dc.description.abstract	Adaptive Neuro-Fuzzy Inference System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of prediction. In this study the nonlinear relation between splitting tensile strength and modulus of elasticity with compressive strength of high strength concrete is modelled and the effect of different effective parameters of Adaptive Neuro-Fuzzy Inference System is investigated on these models. To specify the best arrangements of parameters in the System to utilize in high strength concrete properties, different combinations of optimization methods and membership functions in the Sugeno system have been applied on more than 300 previously conducted experimental datasets. Both the grid partition and sub-clustering methods have been applied to models and compared to get the best combination of parameters.	en_US
dc.publisher	Foundation of Computer Science	en_US
dc.relation.ispartof	International Journal of Computer Applications	en_US
dc.relation.isbasedon	10.5120/17687-8548	en_US
dc.title	Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	101	en_US
utslib.for	0905 Civil Engineering	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
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/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	101	en_US

Abstract:

Adaptive Neuro-Fuzzy Inference System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of prediction. In this study the nonlinear relation between splitting tensile strength and modulus of elasticity with compressive strength of high strength concrete is modelled and the effect of different effective parameters of Adaptive Neuro-Fuzzy Inference System is investigated on these models. To specify the best arrangements of parameters in the System to utilize in high strength concrete properties, different combinations of optimization methods and membership functions in the Sugeno system have been applied on more than 300 previously conducted experimental datasets. Both the grid partition and sub-clustering methods have been applied to models and compared to get the best combination of parameters.

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http://hdl.handle.net/10453/30417