Analysing of nano-silica usage with fly ash for grouts with artificial neural network models

Abstract

When grout is used to penetrate voids and cracks in soils and rock layers, easy and effective pumping of the grouts is vital, especially for grouting works during geotechnical improvements. For this reason, improving the rheological parameters of cement-based grouts and increasing their fluidity are important for effective grouting injection. In this study, an experimental investigation and analysis using artificial neural network (ANN) models were used to discover how nano silica (n-SiO2) together with fly ash affects the rheological behaviour of cement-based grouts. The effects of nano silica (n-SiO2) additions at different contents by mass (0.0%, 0.3%, 0.6%, 0.9%, 1.2% and 1.5%) on the plastic viscosity and yield stress values of cement-based grouts incorporating fly ash as a mineral additive at different amounts (0% – as a control, 5%, 10%, 15%, 20%, 25% and 30%) were investigated. Using the experimental data obtained, a feed-forward (FF) back-propagation (BP) multi-layer perceptron (MLP) artificial neural network (ANN) was developed to predict the plastic viscosity and yield stress of cement-based grouts with nano silica nanoparticle additives. The ANN model developed can predict the plastic viscosity and yield stress values of cement-based grouts containing nano silica nanoparticle-doped fly ash with high accuracy.