1. Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs)
- Author
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E.R. Teixeira, Marco Francesco Funari, Ricardo Mateus, Chiara Turco, and Universidade do Minho
- Subjects
Saúde de qualidade ,QH301-705.5 ,Computer science ,QC1-999 ,0211 other engineering and technologies ,Chemicals: Manufacture, use, etc ,Compressive strength ,02 engineering and technology ,Tensile strength ,Biomaterials ,Engenharia e Tecnologia::Engenharia Civil ,natural fibres ,TP890-933 ,021105 building & construction ,Ultimate tensile strength ,Energias renováveis e acessíveis ,Biology (General) ,Produção e consumo sustentáveis ,Artificial Neural Networks ,Compressed Earth Blocks ,reinforcement ,compressive strength ,tensile strength ,Civil and Structural Engineering ,Cidades e comunidades sustentáveis ,Science & Technology ,Artificial neural network ,Orientation (computer vision) ,business.industry ,Physics ,TP200-248 ,Structural engineering ,Textile bleaching, dyeing, printing, etc ,021001 nanoscience & nanotechnology ,Reinforcement ,Mechanics of Materials ,Engenharia Civil [Engenharia e Tecnologia] ,Ceramics and Composites ,0210 nano-technology ,business ,Natural fibres - Abstract
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., This work was funded by the FCT (Foundation for Science and Technology), under grant agreement UIBD/150874/2021 attributed to the first author. This work was also partly financed by Fundação “La Caixa”, under the reference PV20-00072, and FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020.
- Published
- 2021
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