Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network

Abstract:

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An artificial neural network (ANN) is presented to predict the workability of self compacting concrete (SCC) containing slump, slump flow and V-test. A data set of a laboratory work, in which a total of 23 concretes were produced, was utilized in the ANNs study. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, fly ash, blast furnace slag, super plasticizer, sand ratio and water/binder, three output parameters which are slump, slump flow and V-test of SCC. ANN-1, ANN-2 and ANN-3 models which containing 15 ,11 and 5 neurons in the hidden layers, respectively are found to predict workability of concrete well within the ranges of the input parameters considered. The three models are tested by comparing to the results to actual measured data. The results showed that ANN-2 is the best suitable for predicting the workability of SCC using concrete ingredients as input parameters.

Info:

Periodical:

Advanced Materials Research (Volumes 168-170)

Edited by:

Lijuan Li

Pages:

1730-1734

DOI:

10.4028/www.scientific.net/AMR.168-170.1730

Citation:

F. X. Li et al., "Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network", Advanced Materials Research, Vols. 168-170, pp. 1730-1734, 2011

Online since:

December 2010

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Price:

$35.00

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