Application of Artificial Neural Network in Predicting Compressive Strength of Vege Block

Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/15123

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DC Field	Value	Language
dc.contributor.author	ABDULRAHMAN, HASSAN SHUAIBU	-
dc.contributor.author	JOHNSON, O A	-
dc.contributor.author	MADZLAN, N.	-
dc.contributor.author	KAMARUDDIN, I	-
dc.contributor.author	OLORUNTOBI, O.O.	-
dc.date.accessioned	2022-12-12T04:41:51Z	-
dc.date.available	2022-12-12T04:41:51Z	-
dc.date.issued	2016-01-05	-
dc.identifier.uri	http://repository.futminna.edu.ng:8080/jspui/handle/123456789/15123	-
dc.description.abstract	Vege block is a building or construction block manufactured from the mixture of sand aggregates and waste cooking oil as a sustainable binder. This study explores the use of Artificial Neural Network (ANN) in the prediction of the compressive strength. Nine ANN models were developed with different hidden neurons ranges from 7-15 and it performances were tested after properly trained using the Root Mean Square Error (RMSE) and coefficient of determination(R-square) and correlation coefficient (r). The result shows that model with 8 hidden neurons show a better performance.	en_US
dc.language.iso	en	en_US
dc.publisher	Maxwell Scientific Publication Corp.	en_US
dc.subject	Artificial neural network, compressive strength, linear regression, waste cooking oil	en_US
dc.title	Application of Artificial Neural Network in Predicting Compressive Strength of Vege Block	en_US
dc.type	Article	en_US
Appears in Collections:	Civil Engineering

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