Predicting the behavior of reinforced concrete columns confined by fiber reinforced polymers using data mining techniques

Abstract

Fiber Reinforced Polymer (FRP) usage to wrap reinforced concrete (RC) structures has become a popular technology. Most studies about RC columns wrapped with FRP in literature ignored the internal steel reinforcement. This paper aims to develop a model for the axial compressive strength and axial strain for FRP confined concrete columns with internal steel reinforcement. The impact of FRP, Transverse, and longitudinal reinforcement is studied. Two non-destructive analysis methods are explored: Artificial Neural Networks (ANNs) and Regression Analysis (RA). The database used in the analysis contains the experimental results of sixty-four concrete columns under the compressive concentric load available in the literature. The results show that both models can predict the column's compressive stress and strain reasonably with low error and high accuracy. FRP has the highest effect on the confined compressive stress and strain compared to other materials. While the longitudinal steel actively contributes to the compressive strength, and the transverse steel actively contributes to the compressive strain.