Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/74826

TitleArtificial neural networks to predict the mechanical properties of natural fibre-reinforced Compressed Earth Blocks (CEBs)
Author(s)Chiara, Turco
Funari, Marco Francesco
Teixeira, Elisabete Rodrigues
Mateus, Ricardo
KeywordsCompressed Earth Blocks
Natural fibres
Reinforcement
Compressive strength
Tensile strength
Artificial Neural Networks
Issue date1-Dec-2021
PublisherMDPI Publishing
JournalFibers
CitationTurco, C.; Funari, M.F.; Teixeira, E.; Mateus, R. Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs). Fibers 2021, 9, 78. https://doi.org/10.3390/fib9120078
Abstract(s)The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the available literature. Data relating to 332 specimens (Database 1) were used for the prediction of the compressive strength (ANN1), and, due to the lack of some information, those relating to 130 specimens (Database 2) were used for the prediction of the tensile strength (ANN2). The developed tools showed high accuracy, i.e., correlation coefficients (R-value) equal to 0.97 for ANN1 and 0.91 for ANN2. Such promising results prompt their applicability for the design and orientation of experimental campaigns and support numerical investigations.
TypeArticle
URIhttps://hdl.handle.net/1822/74826
DOI10.3390/fib9120078
e-ISSN2079-6439
Publisher versionhttps://www.mdpi.com/2079-6439/9/12/78
Peer-Reviewedyes
AccessOpen access
Appears in Collections:ISISE - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
CTurco_MFunari_ETeixeira_RMateus_fibers.pdfManuscript4,4 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID