Abstract Performance evaluation and reliability assessment of real-world structures under earthquakes is of paramount importance. Generally, different mechanical property parameters of a structure are usually not independent, nor completely dependent, but partly dependent or correlated. Therefore, how to reasonably characterize such partial dependency and whether such partial dependency real matters in the stochastic response and reliability of structures under earthquakes are crucial issues. For this purpose, in the present paper, a novel physically-guided data-driven methodology of capturing the correlation configuration of basic random variables and the probability density evolution method are synthesized. The physically-guided data-driven methodology is firstly outlined. In this methodology, the underlying physical mechanism between dependent random variables is firstly involved to establish a random function model, and then the available observed data are adopted to identify the parameters in this model. What is more, physical constraints are also revealed for the initial modulus of elasticity and compressive strength of concrete. The probability density evolution method is then adopted, and the point selection by minimizing the GF-discrepancy is adjusted according to the correlation configuration and physical constraints. A reinforced concrete frame structure subjected to earthquake input is studied. It is found that when the structure is in the strongly nonlinear stage, the correlation configuration has considerable effects on the standard deviation of the stochastic responses, by a factor of nearly 2. In addition, whether the mechanical parameters in different floors are independent or not has great effects on the stochastic responses as well. Problems to be further studied are also outlined.

中文翻译：

---

具有相关随机参数的结构随机地震响应的概率密度演化分析

摘要 地震作用下真实世界结构的性能评估和可靠性评估至关重要。通常，结构的不同力学性能参数通常不是独立的，也不是完全相关的，而是部分相关或相关的。因此，如何合理地刻画这种部分依赖以及这种部分依赖在地震作用下结构的随机响应和可靠性中是否真正重要是至关重要的问题。为此，在本文中，综合了一种新的物理引导数据驱动方法，用于捕获基本随机变量的相关配置和概率密度演化方法。首先概述了物理引导的数据驱动方法。在这种方法论中，首先涉及因随机变量之间的潜在物理机制，建立随机函数模型，然后利用可用的观测数据识别模型中的参数。此外，还揭示了混凝土初始弹性模量和抗压强度的物理约束。然后采用概率密度演化方法，根据相关配置和物理约束调整最小化GF-discrepancy的点选择。研究了受地震输入的钢筋混凝土框架结构。发现当结构处于强非线性阶段时，相关配置对随机响应的标准偏差有相当大的影响，接近2倍。此外，不同楼层的力学参数是否独立对随机响应也有很大影响。还概述了有待进一步研究的问题。