Prediction of Compressive Strength of Cement Using Gene Expression Programming

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DC Field	Value	Language
dc.contributor.author	Barai, Sudhir Kumar	-
dc.date.accessioned	2021-11-14T07:43:19Z	-
dc.date.available	2021-11-14T07:43:19Z	-
dc.date.issued	2009	-
dc.identifier.uri	https://link.springer.com/chapter/10.1007/978-3-540-89619-7_20	-
dc.identifier.uri	http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/3568	-
dc.description.abstract	Gene Expression Programming is employed to predict the 28 days compressive strength of cement mortar. The input parameters considered are C3S, SO3, Blaine and Alkali and the output parameter is the 28 days compressive strength. The model was able to predict successfully with a root mean square error of 1.4956. This model is compared with the Fuzzy Logic Model and ANN-GA model. The GEP model is proved to perform better than the Fuzzy Logic Model. It yields an expression that relates the inputs to outputs thereby overcoming the disadvantages of the artificial neural networks.	en_US
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	Civil Engineering	en_US
dc.subject	Compressive Strength	en_US
dc.subject	Unconfined Compressive Strength	en_US
dc.subject	Gene Expression Programming	en_US
dc.title	Prediction of Compressive Strength of Cement Using Gene Expression Programming	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Civil Engineering

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