Abstract Deterioration parameters that are commonly used to simulate nonlinear behavior of steel components were mainly calibrated based on the results from experiments on steel beams. Recently, the state of knowledge for deterioration behavior of steel columns has been improved by experimental and analytical studies on the wide-flange steel columns. These deteriorating characteristics are introduced as regression relationships in which the associated uncertainties are represented by the coefficient of variation (COV). Accounting for these uncertainties in estimating collapse fragility curves through the incremental dynamic analysis (IDA) and simulation-based reliability methods is impractical due to the large amount of required computational effort. In this study, two main goals are pursued. The first goal is comprehensive evaluation of the main, interaction, and quadratic effects of the modeling random variables on the collapse capacity of steel structures. The second goal is to propose an efficient approach to create response surface (RS), which in combination with the Monte Carlo (MC) sampling method will be used to incorporate modeling uncertainties into the collapse fragility. This efficiency will be achieved by employing screening design techniques to reduce the amount of analysis required to create a quadratic RS and also endurance time (ET) analysis as an efficient alternative nonlinear dynamic analysis method with less computational time to estimate the structural responses. In order to develop a reliable probabilistic model, the Bayesian model inference approach is applied to account for the uncertainties in the created model. The proposed procedure is performed with both IDA and ET methods on a prototype 5-story steel frame. Results indicate that collapse capacity is highly influenced by the strength modeling variables of beam as well as the ultimate rotation capacity of column components. In addition, ET method by a considerable reduction in the computational costs provides comparable responses with IDA in a probabilistic framework.

中文翻译：

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一种将建模不确定性纳入钢结构倒塌脆性的有效方法

摘要 常用的钢构件非线性行为模拟的退化参数主要是根据钢梁的试验结果进行标定的。最近，通过对宽翼缘钢柱的实验和分析研究，对钢柱劣化行为的了解程度有所提高。这些恶化的特征被引入为回归关系，其中相关的不确定性由变异系数 (COV) 表示。由于需要大量的计算工作，因此在通过增量动态分析 (IDA) 和基于模拟的可靠性方法估计坍塌脆性曲线时考虑这些不确定性是不切实际的。在这项研究中，追求两个主要目标。第一个目标是综合评价主要，相互作用，以及建模随机变量对钢结构倒塌能力的二次影响。第二个目标是提出一种创建响应面 (RS) 的有效方法，该方法与蒙特卡罗 (MC) 采样方法相结合，将用于将建模不确定性纳入崩溃脆性中。这种效率将通过采用筛选设计技术来减少创建二次 RS 和耐久性时间 (ET) 分析所需的分析量来实现，作为一种有效的替代非线性动力分析方法，用更少的计算时间来估计结构响应。为了开发可靠的概率模型，应用贝叶斯模型推理方法来解释所创建模型中的不确定性。建议的程序是在原型 5 层钢框架上使用 IDA 和 ET 方法执行的。结果表明，倒塌能力受梁的强度建模变量以及柱组件的极限旋转能力的影响很大。此外，ET 方法通过显着降低计算成本在概率框架中提供了与 IDA 相当的响应。