Abstract
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.
Highlights
With rapid population growth and damaged infrastructure, increasing attention is being focused on the construction industry
Nagajothi and Elavenil [38] used an artificial neural network (ANN) model to predict the mechanical properties of aluminum silicate on geopolymer concrete, and the results showed that the prediction results of the ANN were in good agreement with the experimental results
In this study, the proposed radialbased function (RBF)–back propagation (BP) composite neural network model is crucial for the prediction of the compressive strength and chloride ion permeability of geopolymer mortar merging polyvinyl alcohol (PVA) fibers and NS
Summary
With rapid population growth and damaged infrastructure, increasing attention is being focused on the construction industry. With the rapid growth of the global economy, it is estimated that in the 30 years, cement output will increase to approximately 5 billion tons globally [4] Such massive emissions of carbon dioxide have caused serious environmental pollution and has brought about huge social pressure. Nagajothi and Elavenil [38] used an artificial neural network (ANN) model to predict the mechanical properties of aluminum silicate on geopolymer concrete, and the results showed that the prediction results of the ANN were in good agreement with the experimental results. In this study, the proposed RBF–BP composite neural network model is crucial for the prediction of the compressive strength and chloride ion permeability of geopolymer mortar merging PVA fibers and NS.
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