RT Journal Article
T1 Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic
A1 Obrer Marco, Creu
A1 Garzón Roca, Julio
A1 Adam, José Miguel
AB The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry.
PB Elsevier
SN 0141-0296
YR 2013
FD 2013
LK https://hdl.handle.net/20.500.14352/95198
UL https://hdl.handle.net/20.500.14352/95198
LA eng
NO Garzón-Roca, Julio, et al. «Compressive Strength of Masonry Made of Clay Bricks and Cement Mortar: Estimation Based on Neural Networks and Fuzzy Logic». Engineering Structures, vol. 48, marzo de 2013, pp. 21-27. https://doi.org/10.1016/j.engstruct.2012.09.029.
DS Docta Complutense
RD 10 abr 2025