Artificial Neural Network–Based Numerical Model to Predict Flexural Capacity of Masonry Panels Strengthened with Textile Reinforced Mortar
Publication: Journal of Composites for Construction
Volume 25, Issue 1
Abstract
Widespread acceptance of textile-reinforced mortar (TRM) systems for various applications would require a universally accepted procedure for the qualification, characterization, and design of TRM. Based on several independent studies, it is well established that the tensile strength of TRM is not just dependent on the type and volume fraction of the textile but also on the bond characteristics of the textile with the cementitious binder. The customization possibilities for selecting the textile and binder according to various product applications makes it necessary to characterize the tensile behavior independently when deriving design parameters. This paper is aimed at developing a methodology for the design of textile-strengthened masonry panels that accounts for its flexural behavior. In the proposed methodology, first predictions of the tensile strength and efficiency of various textiles in TRM are made, and the possibilities of adopting artificial neural networks (ANNs) to this aim are explored. The method is independently validated using experimental data of uniaxial tensile characterization. Further, the flexural capacity predicted for masonry panels strengthened with TRM has been validated against the experimental results to prove the feasibility of the proposed methodology.
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Journal of Composites for Construction
Volume 25 • Issue 1 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Mar 18, 2020
Accepted: Sep 24, 2020
Published online: Dec 7, 2020
Published in print: Feb 1, 2021
Discussion open until: May 7, 2021
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