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Sensors 2017, 17(6), 1344; doi:10.3390/s17061344

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

1
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR, 14121 Athens, Greece
2
Department of Civil and Environmental Engineering, University of Cyprus, 1678 Nicosia, Cyprus
*
Author to whom correspondence should be addressed.
Academic Editors: Maria Gabriella Xibilia, Alex Alexandridis and Elias N. Zois
Received: 2 May 2017 / Revised: 28 May 2017 / Accepted: 5 June 2017 / Published: 9 June 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Abstract

This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature. View Full-Text
Keywords: artificial intelligence techniques; artificial neural networks; compressive strength; modulus of elasticity; non-destructive testing (NDT) methods; sandcrete materials; soft-computing techniques; soft sensors; velocity of ultrasonic pulses artificial intelligence techniques; artificial neural networks; compressive strength; modulus of elasticity; non-destructive testing (NDT) methods; sandcrete materials; soft-computing techniques; soft sensors; velocity of ultrasonic pulses
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Asteris, P.G.; Roussis, P.C.; Douvika, M.G. Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials. Sensors 2017, 17, 1344.

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