Performance evaluation of discontinuous coconut and steel fibers as reinforcement in concrete using the artificial neural network approach

Fibre-Reinforced Concrete (FRC) has significant benefits due to its ability to improve the tensile and flexural strengths of hardened concrete. This study aimed to investigate the use of Artificial Neural Network (ANN) to predict and optimize coconut husk fibers and recycled tyre steel fibers reinforced concrete in terms of compressive and flexural strengths, density, and water absorption. A laboratory experiment of eighteen (18) different mix proportions was carried out with the volumetric fraction of the fibers varied between 0% and 2%. A quick propagation neural network that uses a supervised learning algorithm and feed-forward architecture was used for training and testing experimental data. The comparison of measurements between the experimental and ANN led to an accurate determination of compressive and flexural strengths as well as density and water absorption. The performance of the ANN was statistically measured using Root Mean Squared Error (RMSE), Mean Percentage Error (MPE), and Absolute Average Error (AAE) showing that the results are reliable. The performance of the ANN model was further evaluated using the absolute relative percent errors (PE). The values of PE obtained were 3.82032 and 12.4481, 3.63029, and 9.27939 for density, water absorption, compressive strength, and flexural strength respectively. An optimum combination of 500 kg/m(3) for cement content, 25 mm for granite size, 0.92% for steel fibers, and 1.08% for coconut fibers was achieved by the Genetic Algorithm tool of ANN. The optimal value predicted for the outputs was also validated with experimental work and was found to be adequate. This strongly suggests that ANN can understand the existing relationship between the input variables and the output and is therefore recommended for the prediction and evaluation of fiber-reinforced concrete containing steel and coconut fibers.