Modeling the confined compressive strength of CFRP-jacketed noncircular concrete columns using artificial intelligence techniques

Abstract

In this paper, an extensive literature search has been employed to extract multiple data on the confined compressive strength of carbon fiber reinforced polymer (CFRP) concrete columns with noncircular cross-sections. The values collected are related to width (b), length (h), radius of corner (r), thickness of fiber (t), unconfined concrete strength (f’co), tensile strength of fiber (ftf), elastic modulus of fiber (Ef) and the confined strength of the CFRP-jacketed concrete columns (f’cc). The database was used to propose predictive models by artificial neural network (ANN-BP, -GA & -GRG), genetic programming (GP) and the evolutionary polynomial regression (EPR) techniques. The sum of squares errors (SSE), root mean square errors (RMSE) and coefficient determination (R2) performance indices were used to evaluate the performance accuracy and efficiency of the models. At the end of the exercise, the GP and EPR produced closed form equation with performance indices of 0.623 (28%) and 0.815 (20.9%), respectively, and these did not come close to the performance of ANN-BP, -GRG and GA which performed in that order with 0.967 (9.4%), 0.960 (10.3%) and 0.957 (10.6%), respectively. Last, the relative importance of the parameters conducted showed that f’co has the greatest influence on the f’cc of the concrete structure.