ANN-based strength prediction of concrete with nano silica, glass, and coir fibers

ABSTRACT Ensuring improved strength and durability in concrete has become significant in modern infrastructure. This study presents Artificial Neural Networks for predictive analysis to investigate how glass fibers, coir fibers, and nanosilica affect the mechanical properties of concrete. Incorporating 3% nanosilica with varying proportions of glass, coir fibers (0.3 – 3%) revealed significant improvement in the concrete’s performance. At 1.5% concentration glass fiber increased compressive strength by 15%, flexural strength by 35.6%, and tensile strength by 43.5%. Similarly, 1.2% concentration, coir fibers improved these properties by 9%, 16.4%, and 32.5% respectively. The mean square propagation, testing, training, validation results are used to prepare the ANN model. For glass fibers, the results are 0.8772, 0.9734, 0.9828, and 0.9563, and the optimal validation results is 0.0107 at epoch 10; for coir fibers, the results are 0.9743, 0.9949, 0.9941, and 0.9909, and the optimal validation results is 0.0220 at epoch 8 correspondingly. To further validate the model’s reliability, additional statistical metrics are Willmott’s Index of Agreement was found to be 0.9953, the Nash–Sutcliffe Efficiency was 0.9814, and the percent bias was –0.0083%. These results confirm the robustness and practical applicability of the proposed ANN model in predicting concrete strength parameters with high fidelity.