Data-driven analysis on axial strength of GFRP-NSC columns based on practical artificial neural network tool

Abstract

Previous research is deficient in the theoretical estimation of the axial load-carrying strength (ALCS) of glass fiber reinforced polymer (GFRP) composite columns. This study aims to calculate the ALCS of GFRP normal strength concrete (NSC) columns (GFRP-NSC) using two different approaches; empirical modeling and artificial neural networks (ANNs). The geometric, material, and testing details of 250 GFRP-NSC columns were collected from the previous studies. An initial assessment of the previous strength models for the ALCS of GFRP-NSC columns was accomplished over the constructed database. General regression and curve-fitting were used for suggesting an improved empirical model. The various hidden layers and neurons were calibrated to find an improved ANN model. Both new empirical and ANN models showed higher accuracy than the previous model for capturing the ALCS with R2 = 0.740, and R2 = 0.882, individually. A comparison of the predictions through one-way analysis of variance (ANOVA) at a 5% significance level depicts that the proposed models presented a higher accuracy than the previous models for capturing the ALCS of GFRP-NSC columns. Additionally, a detailed parametric investigation of GFRP-NSC members was carried out using the suggested empirical model to explore the influence of different variables of studied members.