This paper presents an optimization methodology to simulate the monotonic and cyclic response of steel reinforcement smooth bars when subjected to inelastic buckling. A finite element (FE) model of steel rebars, based on non-linear fibre sections and an initial geometrical imperfection, is adopted. The multi-step optimization proposed herein to identify the main parameters of the material constitutive models is based on genetic algorithms (GA) and Bayesian model updating. The methodology consists of comparing available experimental tests from literature with the corresponding numerical results. New empirical relationships and probabilistic distributions of the optimized model parameters, such as post-yielding hardening ratio, isotropic hardening in compression and tension, plus initial curvature, are presented. Finally, utilizing both the GA-based and Bayesian-based calibration, an improvement of an existing analytical model for inelastic buckling of smooth steel rebars is proposed. Such analytical modelling can be efficient and reliable for future building codes and assessment guidelines for existing buildings.

本文提出了一种优化方法来模拟钢筋非光滑屈曲时钢筋平滑杆的单调响应和循环响应。采用了基于非线性纤维截面和初始几何缺陷的钢筋的有限元模型。本文提出的用于识别材料本构模型主要参数的多步优化基于遗传算法（GA）和贝叶斯模型更新。该方法包括将文献中可用的实验测试与相应的数值结果进行比较。提出了优化模型参数的新经验关系和概率分布，例如屈服后硬化率，压缩和拉伸中的各向同性硬化以及初始曲率。最后，利用基于GA和基于贝叶斯的标定，提出了一种对光滑钢的非弹性屈曲的现有分析模型的改进。对于将来的建筑物规范和现有建筑物的评估准则，这种分析模型可能是高效且可靠的。