Compressive strength and anti-chloride ion penetration assessment of geopolymer mortar merging PVA fiber and nano-SiO2 using RBF–BP composite neural network

Abstract

In this study, we investigated the mechanical properties and chloride ion permeation resistance of geopolymer mortars based on fly ash modified with nano-SiO2 (NS) and polyvinyl alcohol (PVA) fiber and metakaolin (MK) at dose levels of 0–1.2% for PVA fiber and 0–2.5% for NS. The Levenberg–Marquardt (L–M) back propagation (BP) neural network, as well as the radial-based function (RBF) neural network, was used to predict the compressive strength and chloride ion permeation resistance of the geopolymer mortar with different admixtures of nanoparticles and PVA fiber, wherein the electric flux value was used as the index for chloride ion permeation performance. The RBF–BP composite neural network was constructed to study the compressive strength and chloride ion permeation resistance of nanoparticle-doped and PVA fiber ground geopolymer mortars. According to the experimental results of the RBF–BP composite neural network model, the mean square error (MSE) was observed to be 0.00071943, root mean square error (RMSE) was 0.026822, and mean absolute error (MAE) was 0.026822, thereby showing higher prediction accuracy, faster convergence, and better fitting effect compared with the single BP neural network and RBF neural network models. In this study, we combined the RBF–BP composite artificial neural network, providing a new method for the future assessment of the compressive strength and chloride ion penetration resistance of geopolymer mortar merging PVA fibers and NS in experiments and engineering studies.

Keywords

• geopolymer mortar
• rbf–bp composite neural network
• resistance to chloride ion penetration
• compressive strength
• prediction