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Authors: Fatih Altun and Tamer Dirikgil

Affiliation: Erciyes University, Turkey

Keyword(s): Polypropylene Fiber, Concrete, High Temperature, Flexure Strength, Multilayer Perceptron, Radial Basis Function Neural Network.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Complex-Valued Neural Networks ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In order to improve the mechanical qualities of a concrete, various kinds of fibers are added to the concrete. In the studies, polypropylene (PP) fibers are employed as a fiber type. It has a significant place in the researches that PP fibers not only improve the mechanical qualities of the concrete under normal temperatures, but also prevents the bursting of the concrete with the internal vapour compression under high temperatures. The distributions and locations of the fibers in the concrete and the variables employed for experimental proceedings affect the mechanical results. This makes it difficult to link the obtained results to each other. In order to establish a complicated link, it is inevitable to create a learning mechanism. In this study, multilayered perceptrons (MLP) and radial basis function artificial neural network (RBFNN) models were used and their flexure strengths were sought to be predicted. Both of the neural network models put in a successful performance and ena bled the prediction of the experimental results with a satisfying approximation. (More)

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Paper citation in several formats:
Altun, F. and Dirikgil, T. (2012). Prediction of the Behaviours by the Prismatic Beams with Polypropylene Fibers under High Temperature Effects through Artificial Neural Networks. In Proceedings of the 4th International Joint Conference on Computational Intelligence - NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4; ISSN 2184-2825, pages 611-615. DOI: 10.5220/0004114006110615

@conference{ncta12,
author={Fatih Altun. and Tamer Dirikgil.},
title={Prediction of the Behaviours by the Prismatic Beams with Polypropylene Fibers under High Temperature Effects through Artificial Neural Networks},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - NCTA, (IJCCI 2012)},
year={2012},
pages={611-615},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004114006110615},
isbn={978-989-8565-33-4},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - NCTA, (IJCCI 2012)
TI - Prediction of the Behaviours by the Prismatic Beams with Polypropylene Fibers under High Temperature Effects through Artificial Neural Networks
SN - 978-989-8565-33-4
IS - 2184-2825
AU - Altun, F.
AU - Dirikgil, T.
PY - 2012
SP - 611
EP - 615
DO - 10.5220/0004114006110615

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