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Masonry Compressive Strength Prediction Using Artificial Neural Networks

2019Journal article Open access
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dc.contributor.authorAsteris, Panagiotis G.
dc.contributor.authorArgyropoulos, Ioannis
dc.contributor.authorCavaleri, Liborio
dc.contributor.authorRodrigues, Hugo
dc.contributor.authorVarum, Humberto
dc.contributor.authorThomas, Job
dc.contributor.authorLourenço, Paulo B.
dc.date.accessioned2019-10-07T16:04:28Z
dc.date.available2019-10-07T16:04:28Z
dc.date.issued2019
dc.description.abstractThe masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAsteris, Panagiotis & Argyropoulos, Ioannis & Cavaleri, L. & Rodrigues, Hugo & Varum, H. & Thomas, Job & Lourenco, Paulo (2018). Masonry Compressive Strength Prediction using Artificial Neural Networks.pt_PT
dc.identifier.doihttps://doi.org/10.1007/978-3-030-12960-6_14
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10400.8/4181
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-12960-6_14pt_PT
dc.subjectArtificial Neural Networks (ANNs)pt_PT
dc.subjectBuilding materialspt_PT
dc.subjectCompressive strengthpt_PT
dc.subjectMasonrypt_PT
dc.subjectMasonry unitpt_PT
dc.subjectMortarpt_PT
dc.subjectSoft-computing techniquespt_PT
dc.titleMasonry Compressive Strength Prediction Using Artificial Neural Networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceSwitzerlandpt_PT
oaire.citation.endPage224pt_PT
oaire.citation.startPage200pt_PT
oaire.citation.titleCommunications in Computer and Information Sciencept_PT
oaire.citation.volume962pt_PT
person.familyNamePinheiro Rodrigues
person.givenNameHugo Filipe
person.identifierE-5195-2010
person.identifier.ciencia-idB610-29E9-0E49
person.identifier.orcid0000-0003-1373-4540
person.identifier.scopus-author-id23019838500
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa461f4ce-a879-4898-99ec-2fa09e1cfb46
relation.isAuthorOfPublication.latestForDiscoverya461f4ce-a879-4898-99ec-2fa09e1cfb46

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