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Artificial neural networks to predict the mechanical properties of natural fibre-reinforced Compressed Earth Blocks (CEBs)
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.
Artificial neural networks to predict the mechanical properties of natural fibre-reinforced Compressed Earth Blocks (CEBs)
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.
Artificial neural networks to predict the mechanical properties of natural fibre-reinforced Compressed Earth Blocks (CEBs)
Chiara, Turco (author) / Funari, Marco Francesco (author) / Teixeira, Elisabete Rodrigues (author) / Mateus, Ricardo (author)
2021-12-01
doi:10.3390/fib9120078
Article (Journal)
Electronic Resource
English
DDC:
690
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