It is well established that various sources of uncertainties play a critical role in the safety assessment of engineering structures. Some widely used frameworks, such as performance-based earthquake engineering (PBEE), explicitly consider the ground motion record-to-record randomness, while the material and modeling uncertainty remain to be primarily based on judgments or limited analysis. This paper presents the results of a comprehensive uncertainty quantification and sensitivity analysis of a reinforced concrete structural component. First, different modeling strategies are adopted to develop several parent models. Next, various sources of uncertainty are propagated through the parent models to generate thousands of children models. The children models are further combined with material uncertainty to produce grandchildren models, and nonlinear transient simulations are conducted using an innovative artificial acceleration at different seismic intensity levels. The results are post-processed using a range of probabilistic, statistical, and machine learning methods. The study finds that the modeling strategy and its associated variability can cause significant bias and dispersion in the drift response, while material uncertainty has a relatively minor effect. The study quantifies the importance of modeling uncertainty, which is often overlooked in engineering practice.

众所周知，各种不确定性来源在工程结构的安全评估中发挥着至关重要的作用。一些广泛使用的框架，例如基于性能的地震工程（PBEE），明确考虑了地震动记录间的随机性，而材料和建模的不确定性仍然主要基于判断或有限的分析。本文介绍了钢筋混凝土结构构件的综合不确定性量化和敏感性分析的结果。首先，采用不同的建模策略来开发多个父模型。接下来，各种不确定性来源通过父模型传播，以生成数千个子模型。子模型进一步与材料不确定性相结合，生成孙模型，并使用不同地震烈度级别的创新人工加速度进行非线性瞬态模拟。使用一系列概率、统计和机器学习方法对结果进行后处理。研究发现，建模策略及其相关的可变性可能会导致漂移响应出现显着的偏差和离散，而材料不确定性的影响相对较小。该研究量化了建模不确定性的重要性，而这在工程实践中经常被忽视。