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

• Published in: Buildings (Basel) 2022-05, Vol.12 (5), p.611
• Main Authors: Anwar, Muhammad Kashif, Shah, Syyed Adnan Raheel, Azab, Marc, Shah, Ibrahim, Chauhan, Muhammad Khalid Sharif, Iqbal, Fahad
• Format: Article
• Language: English
• Subjects: Artificial neural networks; axial capacity; Bars; Comparative analysis; Composite materials; Concrete; Concrete columns; Concrete structures; Confidence intervals; construction; Data envelopment analysis; Datasets; Decision making; Fiber reinforced polymers; Glass fiber reinforced plastics; glass fibre-reinforced polymer (GFRP); Load; Neural networks; Optimization; Parametric analysis; Polymers; Prediction models; Regression analysis; Reinforced concrete; Reinforcing steels; Software; Structural response; sustainability
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12050611

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