Prediction of Energy Absorption Capability in Fiber Reinforced Self-Compacting Concrete Containing Nano-Silica Particles using artificial neural network

Abstract

The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated. For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCO). Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, toughness, fracture energy and flexural strength of SCC.