Abstract Nuclear engineers are increasingly relying on large-scale simulations particularly for seismic risk assessment. Experimentally validated simulation models are used to consider the effects of uncertainties and evaluate fragilities by conducting multiple nonlinear analyses. However, such an approach becomes computationally prohibitive and care is needed to achieve desired degree of accuracy with a reasonable amount of computational effort. In this paper, a statistical framework is presented to minimize the total computational effort needed in conducting large-scale simulations for seismic risk assessment. The salient features of the framework are: (i) use of Bayesian inference to allow consideration of data from diverse sources like experiments, field data, existing or simplified approaches, and data from large-scale simulations, and (ii) embedment of Bayesian methods within an iterative process to plan and allocate adequate computing resources such that the desired accuracy is achieved using minimum possible simulations. The applicability and efficiency of the proposed framework is illustrated using the example of a box-shaped reinforced concrete shear wall.

中文翻译：

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使用大规模非线性有限元分析进行地震风险评估的贝叶斯推理驱动计算框架

摘要 核工程师越来越依赖大规模模拟，特别是在地震风险评估方面。实验验证的仿真模型用于考虑不确定性的影响，并通过进行多个非线性分析来评估脆弱性。然而，这种方法在计算上变得令人望而却步，需要注意以合理的计算量实现所需的准确度。在本文中，提出了一个统计框架，以最大限度地减少进行大规模地震风险评估模拟所需的总计算量。该框架的显着特点是：(i) 使用贝叶斯推理来考虑来自不同来源的数据，如实验、现场数据、现有或简化的方法，以及来自大规模模拟的数据，(ii) 在迭代过程中嵌入贝叶斯方法以规划和分配足够的计算资源，以便使用尽可能少的模拟实现所需的准确性。以箱形钢筋混凝土剪力墙为例说明了所提出框架的适用性和效率。