With the structural design paradigm shift since the early 2000′s from the traditional approach to performance-based design (PBD), there has been a growing need for reliable nonlinear finite element (FE) models that can accurately predict the response of structures when subjected to extreme loads, such as earthquakes. In the case of reinforced concrete (RC) structures, a proper representation of the hysteretic nonlinear behavior of reinforcing steel becomes crucial in order to carry out nonlinear time history analyses. The Giuffrè-Menegotto-Pinto (GMP) uniaxial steel constitutive law has been widely used by researchers and practitioners to model reinforcing steel bars. Despite the widespread in its implementation, a limited number of studies have proposed well-calibrated parameter values for this model. In addition, low identifiability of its governing parameters and the high cost of generating reliable experimental data have prevented a thorough probabilistic characterization of the GMP model parameters. Usually, only default parameter values from the early development of the model tend to be used. This paper uses experimental data from cyclic tests conducted on 36 reinforcing steel coupons manufactured in accordance to ASTM A615 and A706 Grade 60 reinforcing steel and proposes a joint probability density function (PDF) for the most influential parameters of the GMP material model. First, a local sensitivity analysis (LSA) is conducted to provide insight into the influence of each parameter in the model response. Also, global sensitivity analysis (GSA) is used to have a deep understanding of the composition of the variability in the model response due to parameter uncertainty and the level of interactions among parameters. The Bayesian approach is combined with the information obtained from GSA and LSA as input, to estimate model parameters and quantify the estimation uncertainties and propagate them to the material stress response. Uncertainty in model predictions obtained with the proposed PDF is assessed, and the impact of considering parameter correlations on the material response is investigated.

自2000年代初以来，随着结构设计范式从传统方法向基于性能的设计（PBD）的转变，对可靠的非线性有限元（FE）模型的需求日益增长，该模型可以准确地预测结构在受力时的响应。承受极端负荷，例如地震。对于钢筋混凝土（RC）结构，钢筋的滞后非线性行为的正确表示对于进行非线性时程分析至关重要。Giuffrè-Menegotto-Pinto（GMP）单轴钢本构定律已被研究人员和从业人员广泛用于建模钢筋。尽管其实施广泛，但有限的研究提出了针对该模型的经过良好校准的参数值。此外，其控制参数的低可识别性以及生成可靠实验数据的高昂成本已无法对GMP模型参数进行彻底的概率表征。通常，仅倾向于使用模型早期开发中的默认参数值。本文使用了对根据ASTM A615和A706 60级钢筋制造的36种钢筋试样进行的循环测试的实验数据，并针对GMP材料模型中最具影响力的参数提出了联合概率密度函数（PDF）。首先，进行局部敏感性分析（LSA），以深入了解模型响应中每个参数的影响。也，全局敏感性分析（GSA）用于深入了解由于参数不确定性和参数之间的相互作用水平而导致的模型响应中的变异性组成。贝叶斯方法与从GSA和LSA获得的信息相结合，以估计模型参数并量化估计不确定性，并将其传播到材料应力响应中。评估了通过建议的PDF获得的模型预测的不确定性，并研究了考虑参数相关性对材料响应的影响。估计模型参数并量化估计不确定性，并将其传播到材料应力响应中。评估了通过建议的PDF获得的模型预测的不确定性，并研究了考虑参数相关性对材料响应的影响。估计模型参数并量化估计不确定性，并将其传播到材料应力响应。评估了通过建议的PDF获得的模型预测的不确定性，并研究了考虑参数相关性对材料响应的影响。