Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model
Publication: Journal of Materials in Civil Engineering
Volume 27, Issue 11
Abstract
The main objective of the research study described herein is to build two analytical models based on artificial neural networks (ANNs) and the statistical mixture design (SMD) method to predict the required performance of ultra-high-performance concrete (UHPC). Two different curing conditions—heat treatment and water storage—were applied to the specimens. To train the neural network, a total set of 53 different mixtures was designed based on the design matrix of SMD. The statistical analysis results showed the adequacy of both models to predict the required performance of UHPC; however, the ANN model could predict the compressive strength (water storage) and slump flow with higher accuracy than the SMD. The optimum combination of the cement, silica fume, and quartz flour was determined to be 24, 9, and 5% by total volume to achieve a flowable mixture with the highest compressive strength. The accuracy of the model was verified with additional experimental tests.
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Acknowledgments
The authors are grateful for the financial support of the Portuguese Science and Technology Foundation (FCT) through project PTDC/ECM/098497/2008, entitled “Intelligent Super Skin (ISS),” as well as through Ph.D. grant SFRH/BD/91402/2012.
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Published In
Journal of Materials in Civil Engineering
Volume 27 • Issue 11 • November 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: May 27, 2014
Accepted: Jan 6, 2015
Published online: Feb 26, 2015
Discussion open until: Jul 26, 2015
Published in print: Nov 1, 2015
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