This article deals with the study of predicting the confinement effect of
carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using
metaheuristics-based artificial neural networks. A detailed database of 708
CFRP confined concrete cylinders is developed from previously published
research with information on 8 parameters including geometrical parameters like
the diameter (d) and height (h) of a cylinder, unconfined compressive strength
of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef),
unconfined concrete strain confined concrete strain and the ultimate
compressive strength of confined concrete fcc'. Three metaheuristic models are
implemented including particle swarm optimization (PSO), grey wolf optimizer
(GWO), and bat algorithm (BA). These algorithms are trained on the data using
an objective function of mean square error and their predicted results are
validated against the experimental studies and finite element analysis. The
study shows that the hybrid model of PSO predicted the strength of
CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO
predicted the results with an accuracy of 98.17%. The high accuracy of axial
compressive strength predictions demonstrated that these prediction models are
a reliable solution to the empirical methods. The prediction models are
especially suitable for avoiding full-scale time-consuming experimental tests
that make the process quick and economical.

使用基于元启发式人工神经网络的方法，预测碳纤维增强聚合物（CFRPs）对混凝土圆柱体强度的束缚效应。研究结果表明，PSO 模型和 GWO 模型能够准确预测 CFRP 受限混凝土圆柱体的强度，为避免耗时且昂贵的实验测试提供了可靠的解决方案。