Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs)

• Published in: Fibers Vol. 9; no. 12; p. 78
• Main Authors: Turco, Chiara, Funari, Marco Francesco, Teixeira, Elisabete, Mateus, Ricardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Artificial intelligence; Artificial Neural Networks; Back propagation; Compressed Earth Blocks; compression strength; Compressive strength; Construction; Correlation coefficients; Data collection; Ductility; Fiber reinforced materials; Mechanical properties; Microscopy; natural fibres; Neural networks; Neurons; prediction; Propagation; reinforcement; Sugarcane; Tensile strength
• ISSN: 2079-6439; 2079-6439
• DOI: 10.3390/fib9120078

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.