Structural Performance of GFRP Bars Based High-Strength RC Columns: An Application of Advanced Decision-Making Mechanism for Experimental Profile Data

Several past studies have shown the use of glass fibre-reinforced polymer (GFRP) bars to alleviate reinforced steel rusting issue in different concrete structures. However, practise GFRP columns has not yet achieved a sufficient confidence level due lack theoretical model found literature. The objective current study is introduce novel prediction for axial capability made with GFRP. For this purpose, two approaches, such as data envelopment analysis (DEA) and artificial neural networks (ANNs) modelling, are used on collected dataset 266 column specimens from previous literature works. Eight parameters were predict performance GFRP-based RC columns. proposed DEA ANNs predictions demonstrated good correlation testing dataset, having R2 values 0.811 0.836, respectively. A comparative models undertaken, it was that suggested capable accurately forecasting structural response GFRP-made Then, comprehensive parametric performed effect materials their geometrical shape.