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 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 theor...

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Bibliographic Details
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
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Summary: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.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings12050611