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An ERAFNN Prediction of Concrete Compressive Strength Based on Physical Properties of Electric Arc Furnace Oxidizing Slag

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Abstract:

In this study, exact radial basis function neural network (ERBFNN) was used to predict the concrete compressive strength based on physical properties of electric arc furnace oxidizing slag. The mean absolute percentage error (MAPE) was used to evaluate the predicting performance. The results indicated the minimum MAPE of 0.08 % and 5.28 % could be achieved when training and predicting, respectively. According to the results, it revealed that ERBFNN was an efficiently tool for providing information.

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Periodical:

Applied Mechanics and Materials (Volume 627)

Pages:

111-114

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.627.111

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Online since:

September 2014

Authors:

Li Chen, Tzu Yi Pai*

Keywords:

Concrete Compressive Strength, Electric Arc Furnace, Exact Radial Basis Function Neural Network (ERBFNN), Oxidizing Slag, Physical Properties

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

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