- -

Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks

Show simple item record

Files in this item

dc.contributor.author Aguilar, V. es_ES
dc.contributor.author Sandoval, Cristian es_ES
dc.contributor.author Adam Martínez, José Miguel es_ES
dc.contributor.author Garzón-Roca, Julio es_ES
dc.contributor.author Valdebenito, Galo es_ES
dc.date.accessioned 2017-06-26T09:39:15Z
dc.date.available 2017-06-26T09:39:15Z
dc.date.issued 2016-03
dc.identifier.issn 1573-2479
dc.identifier.uri http://hdl.handle.net/10251/83608
dc.description.abstract This paper analyses the accuracy of a selection of expressions currently available to estimate the in-plane shear strength of reinforced masonry (RM) walls, including those presented in some international masonry codes. For this purpose, predictions of such expressions are compared with a set of xperimental results reported in the literature. The experimental database includes specimens built with ceramic bricks and concrete blocks tested in partially and fully grouted conditions, which typically present a shear failure mode. Based on the experimental data collected and using artificial neural networks (ANN), this paper presents alternative expressions to the different existing methods to predict the in-plane shear strength of RM walls. The wall aspect ratio, the axial pre-compression level on the wall, the compressive strength of masonry, as well as the amount and spacing of vertical and horizontal reinforcement throughout the wall are taken into consideration as the input parameters for the proposed expressions. The results obtained show that ANN-based proposals give good predictions and in general fit the experimental results better than other calculation methods. es_ES
dc.description.sponsorship This work was supported by the Fondo Nacional de Ciencia y Tecnologia de Chile, (Fondecyt de Iniciacion) [grant number 11121161]. en_EN
dc.language Inglés es_ES
dc.publisher Taylor & Francis (Routledge) es_ES
dc.relation Fondo Nacional de Ciencia y Tecnologia de Chile, (Fondecyt de Iniciacion) 11121161 es_ES
dc.relation.ispartof Structure and Infrastructure Engineering es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Reinforced masonry es_ES
dc.subject Experimental database es_ES
dc.subject Shear strength es_ES
dc.subject Neural networks es_ES
dc.subject Sensitivity analysis es_ES
dc.subject.classification INGENIERIA DEL TERRENO es_ES
dc.subject.classification INGENIERIA DE LA CONSTRUCCION es_ES
dc.title Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/15732479.2016.1157824
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports es_ES
dc.description.bibliographicCitation Aguilar, V.; Sandoval, C.; Adam Martínez, JM.; Garzón-Roca, J.; Valdebenito, G. (2016). Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks. Structure and Infrastructure Engineering. 12(12):1661-1674. doi:10.1080/15732479.2016.1157824 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1080/15732479.2016.1157824 es_ES
dc.description.upvformatpinicio 1661 es_ES
dc.description.upvformatpfin 1674 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 12 es_ES
dc.relation.senia 311603 es_ES


This item appears in the following Collection(s)

Show simple item record