Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network (ANN) modelling

Abstract

Basalt FRP geopolymer composites are considered a sustainable solution to address the conventional OPC-Steel composite structures' structural and environmental concerns, especially the increased carbon emissions and structural deterioration due to corrosion attacks. Recent research on novel composites reports the superior performance of these FRP-geopolymer concrete structural applications reporting high strength and durability. However, the existing design guidelines do not accurately predict the flexural capacity and strength characteristics, which hinders the broader use of FRP-based geopolymer composites. This study aims to develop a prediction tool to help the researchers model the structural performance of the novel FRP-geopolymer composite based on modern machine learning algorithms. It shall address the lack of data on novel composites' structural and long-term performance in various exposure conditions. This paper thus reports the suitability of Artificial Neural Network (ANN) based models to accurately predict the flexural strength of Basalt FRP reinforced self-compacting geopolymer concrete beams under various exposure conditions. The ANN model is trained and validated against the experimental data from the long-term study conducted on the beam specimens subjected to one-year ambient and marine exposures. The ANN prediction model developed in this study reported close agreement with the experimental data showing better prediction accuracy than the existing empirical and numerical models.