Fibre Reinforced Polymer (FRP) composites can provide efficient enhancements in terms of strength and deformability for concrete structures, in which accurate predictions of FRP confined concrete ultimate conditions is highly essential to maintain safety levels, further structural analysis and members design. In this paper, three novel hybrid intelligence models were proposed based on the hybridization of Support Vector Regression (SVR) model with three bio-inspired optimization algorithms as genetic algorithm (GA), particle swarm optimization (PSO) and Whale optimization algorithm (WOA) for predicting the ultimate conditions of FRP-confined concrete. Moreover, 15 existing empirical relations for the prediction of the ultimate strength and strain of FRP-confined concrete have been comprehensively reviewed. The performances of the empirical models and the proposed hybrid models as SVR-GA, SVR-PSO and SVR-WOA are evaluated and compared based on a large database, including 780 circular FRP-confined concrete specimens, which are collected from the open-source published experiments. By comparing the predicted results based on several statistical indicators, the proposed hybrid SVR-models are generally outperforming the existing empirical relations in terms of accuracy and agreement with the experimental database. SVR-WOA provides superior performances than SVR-PSO, SVR-GA and all existed empirical models. The root mean square error is improved using SVR-WOA by 0.9%, 14.9 % and 37% for the ultimate strain capacity, and 2.7%, 4.6% and 17.3% for the ultimate strength compared to SVR-PSO, SVR-GA and the best empirical relation, respectively.

纤维增强聚合物 (FRP) 复合材料可以有效增强混凝土结构的强度和变形能力，其中准确预测 FRP 约束混凝土极限条件对于保持安全水平、进一步的结构分析和构件设计至关重要。在本文中，基于支持向量回归（SVR）模型与遗传算法（GA）、粒子群优化（PSO）和鲸鱼优化算法（WOA）三种仿生优化算法的混合，提出了三种新颖的混合智能模型。用于预测 FRP 约束混凝土的极限条件。此外，还全面回顾了 15 个现有的 FRP 约束混凝土极限强度和应变预测经验关系。基于大型数据库评估和比较经验模型和所提出的混合模型 SVR-GA、SVR-PSO 和 SVR-WOA 的性能，其中包括从开源收集的 780 个圆形 FRP 约束混凝土试件发表的实验。通过比较基于几个统计指标的预测结果，所提出的混合 SVR 模型在准确性和与实验数据库的一致性方面通常优于现有的经验关系。SVR-WOA 提供比 SVR-PSO、SVR-GA 和所有现有经验模型更优越的性能。与 SVR-PSO、SVR-GA 相比，使用 SVR-WOA 的极限应变能力均方根误差分别提高了 0.9%、14.9% 和 37%，极限强度提高了 2.7%、4.6% 和 17.3%。最好的经验关系，分别。