Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/74826
Título: | Artificial neural networks to predict the mechanical properties of natural fibre-reinforced Compressed Earth Blocks (CEBs) |
Autor(es): | Chiara, Turco Funari, Marco Francesco Teixeira, Elisabete Rodrigues Mateus, Ricardo |
Palavras-chave: | Compressed Earth Blocks Natural fibres Reinforcement Compressive strength Tensile strength Artificial Neural Networks |
Data: | 1-Dez-2021 |
Editora: | MDPI Publishing |
Revista: | Fibers |
Citação: | Turco, 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 |
Resumo(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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/74826 |
DOI: | 10.3390/fib9120078 |
e-ISSN: | 2079-6439 |
Versão da editora: | https://www.mdpi.com/2079-6439/9/12/78 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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CTurco_MFunari_ETeixeira_RMateus_fibers.pdf | Manuscript | 4,4 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons