Analyzing of Nano-SiO2 Usage with Fly Ash for Grouts with Artificial Neural Network Models

Abstract

During grout penetrating to voids and cracks in soils and rock layers, pumping grouts easily and effectively is vital parameter for especially grouting works in geotechnical improvements. For this reason, improving the rheological parameters of cement-based grouts and increasing the fluidity are important for an effective grouting injection. In this study how nano silica (n-SiO2) together with fly ash will affect the rheological behavior of cement-based grouts has been experimentally investigated and analyzed with artificial neural network (ANN) models. 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 plastic viscosity and yield stress values of cement-based grouts incorporated with fly ash as mineral additive at different constitutes (%0-for control purpose, %5, %10, %15, %20, %25 and %30) were investigated in this study. Moreover, using the obtained experimental data, a feed-forward (FF) back-propagation (BP) multi-layer perceptron (MLP) artificial neural network (ANN) has been developed to predict the plastic viscosity and yield stress of cement-based grouts with n-SiO2 nanoparticle additives. The developed ANN model can predict the plastic viscosity and yield stress values of cement-based grouts containing n-SiO2 nanoparticle doped fly ash with high accuracy.