Evaluation of Artificial Neural Network Predicted Mechanical Properties of Jute and Bamboo Fiber Reinforced Concrete Along with Silica Fume.

The aim of the effort is to estimate the effect of jute and bamboo fibers with silica fume (SF) of different proportions on mechanical properties of concrete. Cube, cylinder and prism specimens are tested to determine the compressive, split tensile and flexural strength at 14 and 28 days of curing. To verify the experimental findings, further Artificial Neural Network (ANN) analysis is conducted. The study employs neural network (NN), such as the Neural Network-Leven Berg–Marquardt and the Neural Network Gradient Descent. In this investigation, feed-ahead lower back promulgation neural networks were employed. The NN predicted values are validated with actual values and the variation is found to be within 10% only. The predicted ANN results are compared with existing Response Surface Methodology model. Under compressive, split tensile and flexural load, the broken surface is examined at a smaller-scale level with a scanning electron microscope (SEM). The experimental results show that concrete with 0.5% bamboo fibers and 0.5% jute fibers with 10% SF had higher influence on the mechanical properties of concrete. When comparing ANN results, the suggested ANN model showed high level of accuracy in estimating the mechanical properties of natural-fiber-reinforced concrete. SEM examination displayed the failure pattern of concrete and fibers. [ABSTRACT FROM AUTHOR]