Abstract Fragility functions enable the assessment of a structural system for a given hazard scenario. Specifically, the fragility function provides the probability of an undesirable structural state conditioned on the occurrence of a specific hazard level. Multiple sources of uncertainty are present when estimating fragility functions, e.g., record-to-record variation, uncertain material and geometric properties, model assumptions, and limited data to characterize the hazard. The objective of this study is to develop a methodology that will accelerate the process of fragility function estimation under limitations in computational resources and data. The approach used in the methodology is as follows. The stochastic map between hazard level and structural response is first constructed using Bayesian inference for a finite number of simulations. The Bayesian approach enables the quantification of the epistemic uncertainty due to a limited number of simulations. This epistemic uncertainty is exploited to sequentially select subsequent simulations that accelerate learning based on up to two different earthquake intensity measures, peak ground velocity and spectral velocity. The methodology is applied to a benchmark model of a twenty-story nonlinear building. Simulations are performed using a set of synthetic ground motions obtained from scenario earthquakes in California. Through this case study the methodology developed here is demonstrated. Additionally, the case study highlights the ability of the methodology to achieve lower levels of epistemic uncertainty than traditional techniques using the same number of simulations. This approach is expected to enable more efficient fragility function determination.

中文翻译：

---

通过顺序选择有效的地震脆性函数

摘要 脆弱性函数能够针对给定的危险情景评估结构系统。具体而言，脆性函数提供了以特定危险水平的发生为条件的不良结构状态的概率。在估计脆弱性函数时，存在多种不确定性来源，例如记录间的变化、不确定的材料和几何特性、模型假设以及描述危害特征的有限数据。本研究的目的是开发一种方法，在计算资源和数据的限制下加速脆性函数估计的过程。该方法中使用的方法如下。危险水平和结构响应之间的随机映射首先使用贝叶斯推理构建，用于有限数量的模拟。由于有限数量的模拟，贝叶斯方法能够量化认知不确定性。利用这种认知不确定性来顺序选择后续模拟，这些模拟可基于多达两种不同的地震强度度量、峰值地速和谱速度来加速学习。该方法应用于二十层非线性建筑的基准模型。使用从加利福尼亚的情景地震中获得的一组合成地面运动进行模拟。通过这个案例研究，展示了这里开发的方法。此外，案例研究强调了该方法比使用相同数量模拟的传统技术实现更低水平的认知不确定性的能力。